From 0f6f700f5d0bfad06b065f50a59629074670879c Mon Sep 17 00:00:00 2001 From: Alex Bartlett Date: Wed, 1 May 2024 09:47:10 -0400 Subject: [PATCH 01/55] on devel branch, make DEG rmd work with nfcore --- .../templates/rnaseq/skeleton/DE/DEG.Rmd | 38 ++++---- .../templates/rnaseq/skeleton/DE/params_de.R | 10 +- .../rnaseq/skeleton/DE/run_markdown.R | 7 +- .../templates/rnaseq/skeleton/load_data.R | 93 +++++++++++++++++++ 4 files changed, 124 insertions(+), 24 deletions(-) create mode 100644 inst/rmarkdown/templates/rnaseq/skeleton/load_data.R diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index f390ad4..b1f3452 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -21,13 +21,16 @@ params: denominator: normal subset_value: NA ruv: true - params_file: params_de.R - project_file: ../information.R + params_file: ./inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R + project_file: ./inst/rmarkdown/templates/rnaseq/skeleton/information.R + functions_file: ./inst/rmarkdown/templates/rnaseq/skeleton/load_data.R + --- ```{r load_params, echo = F} source(params$params_file) source(params$project_file) +source(params$functions_file) ``` ```{r load_libraries, cache = FALSE, message = FALSE, warning=FALSE, echo=FALSE,} @@ -47,6 +50,7 @@ library(bcbioR) library(ggprism) library(viridis) library(pheatmap) +library(janitor) colors=cb_friendly_cols(1:15) ggplot2::theme_set(theme_prism(base_size = 14)) opts_chunk[["set"]]( @@ -106,21 +110,20 @@ name_pathways_fn=file.path( ``` -```{r read_counts_data} -coldata=read.csv(coldata_fn) -stopifnot(column %in% names(coldata)) +```{r load_coldata} +coldata <- load_coldata(coldata_fn, params$numerator, params$denominator, subset_column, params$subset_value) +``` -# use only some samples, by default use all -if (!is.na(subset_column)){ - coldata <- coldata[coldata[[paste(subset_column)]] == params$subset_value, ] -} -coldata <- coldata[coldata[[paste(column)]] %in% c(params$numerator, params$denominator), ] -rownames(coldata) <- coldata$description -coldata[[column]] = relevel(as.factor(coldata[[column]]), params$denominator) +```{r load metrics} +metrics <- load_metrics(se_object, multiqc_data_dir) %>% + left_join(coldata, by = c('sample' = 'description')) +rownames(metrics) <- metrics$sample +``` -counts <- read_csv(counts_fn) %>% column_to_rownames('gene') -counts <- counts[,colnames(counts) %in% coldata$description] +```{r load_counts_data} +counts <- load_counts(counts_fn) +counts <- counts[,colnames(counts) %in% coldata$description] # if the names don't match in order or string check files names and coldata information counts = counts[rownames(coldata)] @@ -142,14 +145,9 @@ norm_matrix = assay(vsd_before) # Covariate analysis ```{r covariates, fig.height = 6, fig.width = 10} - -se <- readRDS(se_object) #local -metrics <- metadata(se)$metrics %>% mutate(sample = toupper(sample)) %>% - left_join(coldata %>% rownames_to_column('sample')) %>% column_to_rownames('sample') - degCovariates( norm_matrix, - metrics + metrics %>% as.data.frame %>% column_to_rownames('sample') ) ``` diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R index c1212fc..e6cc763 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R @@ -2,7 +2,15 @@ date = "YYYYMMDD" column = "sample_type" subset_column = NA +basedir <- './' + +# params for bcbio coldata_fn = "https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/coldata.csv" counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/tximport-counts.csv' se_object=url("https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/bcbio-se.rds") -basedir <- './' +# +# params for nfcore +# coldata_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' +# counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.tsv' +# multiqc_data_dir = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/multiqc-report-data/' +# se_object = NA diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R index 44e430b..c6d1217 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R @@ -3,8 +3,8 @@ library(rmarkdown) render_de <- function(numerator, denominator, subset_value = NA, params_file = 'params_de.R'){ - rmarkdown::render(input = "DEG.Rmd", - output_dir = "./", + rmarkdown::render(input = "./inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd", + output_dir = "./inst/rmarkdown/templates/rnaseq/skeleton/DE/", output_format = "html_document", output_file = ifelse(!is.na(subset_value), paste0('DE_', subset_value, '_', numerator, '_vs_', denominator, '.html'), @@ -17,7 +17,8 @@ render_de <- function(numerator, denominator, subset_value = NA, numerator = numerator, denominator = denominator, params_file = params_file, - project_file = '../information.R' + project_file = '../information.R', + functions_file = '../load_data.R' ) ) } diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/load_data.R b/inst/rmarkdown/templates/rnaseq/skeleton/load_data.R new file mode 100644 index 0000000..149da90 --- /dev/null +++ b/inst/rmarkdown/templates/rnaseq/skeleton/load_data.R @@ -0,0 +1,93 @@ +library(tidyverse) + +load_metrics <- function(se_object, multiqc_data_dir){ + + # bcbio input + if (!is.na(se_object)){ + + se <- readRDS(se_object) + metrics <- metadata(se)$metrics %>% mutate(sample = toupper(sample)) #%>% + # left_join(coldata %>% rownames_to_column('sample')) %>% column_to_rownames('sample') + + } else { #nf-core input + + metrics <- read_tsv(paste0(multiqc_data_dir, 'multiqc_general_stats.txt')) + metrics_qualimap <- read_tsv(paste0(multiqc_data_dir, 'mqc_qualimap_genomic_origin_1.txt')) + + metrics <- metrics %>% full_join(metrics_qualimap) + metrics <- metrics %>% + clean_names() %>% + dplyr::rename_with(~gsub('.*mqc_generalstats_', '', .)) + + total_reads <- metrics %>% + filter(grepl('_', sample)) %>% + remove_empty(which = 'cols') %>% + dplyr::rename(single_sample = sample) %>% + mutate(sample = gsub('_[12]+$', '', single_sample)) %>% + group_by(sample) %>% + summarize(total_reads = sum(fastqc_raw_total_sequences)) + + metrics <- metrics %>% + filter(!grepl('_', sample)) %>% + remove_empty(which = 'cols') %>% + full_join(total_reads) %>% + mutate(mapped_reads = samtools_reads_mapped) %>% + mutate(exonic_rate = exonic/(star_uniquely_mapped * 2)) %>% + mutate(intronic_rate = intronic/(star_uniquely_mapped * 2)) %>% + mutate(intergenic_rate = intergenic/(star_uniquely_mapped * 2)) %>% + mutate(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% + mutate(x5_3_bias = qualimap_5_3_bias) %>% + dplyr::select( + sample, + total_reads, + mapped_reads, + exonic_rate, + intronic_rate, + intergenic_rate, + r_rna_rate, + x5_3_bias + ) + + # metrics <- metrics %>% + # full_join(meta_df , by = c("sample" = "sample")) + } + rownames(metrics) <- metrics$sample + return(metrics) +} + +load_coldata <- function(coldata_fn, numerator, denominator, subset_column = NA, subset_value = NA){ + coldata=read.csv(coldata_fn) + stopifnot(column %in% names(coldata)) + + # use only some samples, by default use all + if (!is.na(subset_column)){ + coldata <- coldata[coldata[[paste(subset_column)]] == subset_value, ] + } + coldata <- coldata[coldata[[paste(column)]] %in% c(numerator, denominator), ] + + if('description' %in% names(coldata)){ + rownames(coldata) <- coldata$description + } + else { + rownames(coldata) <- coldata$sample + coldata$description <- coldata$sample + } + coldata[[column]] = relevel(as.factor(coldata[[column]]), denominator) + + return(coldata) +} + +load_counts <- function(counts_fn){ + + # bcbio input + if(grepl('csv', counts_fn)){ + counts <- read_csv(counts_fn) %>% column_to_rownames('gene') + } else { # nf-core input + counts <- read_tsv(counts_fn) %>% dplyr::select(-gene_name) %>% + mutate(gene_id = str_sub(gene_id, 1, 15)) %>% + column_to_rownames('gene_id') %>% round + + return(counts) + } + +} \ No newline at end of file From a225f29edcebba676f7113a43124f1b2e1ca271a Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Thu, 2 May 2024 10:48:58 -0400 Subject: [PATCH 02/55] bump devel version for new features --- DESCRIPTION | 2 +- README.Rmd | 22 +++++++++++++++++++--- README.md | 27 +++++++++++++++++++++------ 3 files changed, 41 insertions(+), 10 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index ef9d990..e8d6962 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: bcbioR Type: Package Title: Templates and functions to guide downstream analysis and data interpretation -Version: 0.1.1 +Version: 0.1.2 Authors@R: person("Pantano", "Lorena", , "lorena.pantano@gmail.com", role = c("aut", "cre")) Description: Collaborative code repository at the Harvard Chan Bioinformatics Core. diff --git a/README.Rmd b/README.Rmd index 8029c88..b58c0b8 100644 --- a/README.Rmd +++ b/README.Rmd @@ -52,11 +52,27 @@ Go to the vignette to know more `vignette("bcbioR_quick_start,package="bcbioR")` ## How to Contribute -You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this. +### Open an issue -Use `usethis::use_import_from("stringr","str_replace_all")` to add a function you are using in the code. +* If you find a bug +* If you want a new feature +* If you want to add code to the templates -Don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN. +### Modify the code + +* Clone the repository +* Make sure you are in the `devel` branch +* Create a new branch `git checkout -b feature1` +* Modify you code, add and commit +* Push to GitHub the new branch +* Create a PR from your branch to `devel` +* Assignt the PR to me or Alex + +Some best practices when developing: + +* install `devtools` +* render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this. +* Use `usethis::use_import_from("stringr","str_replace_all")` to add a new function you are using in the code. ### Contributors diff --git a/README.md b/README.md index 0578665..e9019da 100644 --- a/README.md +++ b/README.md @@ -58,14 +58,29 @@ Go to the vignette to know more ## How to Contribute -You’ll still need to render `README.Rmd` regularly, to keep `README.md` -up-to-date. `devtools::build_readme()` is handy for this. +### Open an issue -Use `usethis::use_import_from("stringr","str_replace_all")` to add a -function you are using in the code. +- If you find a bug +- If you want a new feature +- If you want to add code to the templates -Don’t forget to commit and push the resulting figure files, so they -display on GitHub and CRAN. +### Modify the code + +- Clone the repository +- Make sure you are in the `devel` branch +- Create a new branch `git checkout -b feature1` +- Modify you code, add and commit +- Push to GitHub the new branch +- Create a PR from your branch to `devel` +- Assignt the PR to me or Alex + +Some best practices when developing: + +- install `devtools` +- render `README.Rmd` regularly, to keep `README.md` up-to-date. + `devtools::build_readme()` is handy for this. +- Use `usethis::use_import_from("stringr","str_replace_all")` to add a + new function you are using in the code. ### Contributors From ba8f4199912dec8d63b24632c3734c157db0f07e Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 6 May 2024 13:10:57 -0400 Subject: [PATCH 03/55] clean up code to be compatible with nf-core rnaseq --- DESCRIPTION | 4 +- .../templates/common/skeleton/README.md | 5 +- .../templates/rnaseq/skeleton/DE/DEG.Rmd | 83 ++++++++++--------- .../rnaseq/skeleton/{ => DE}/load_data.R | 65 ++++++++------- .../templates/rnaseq/skeleton/DE/params_de.R | 4 +- .../rnaseq/skeleton/DE/run_markdown.R | 2 +- .../templates/rnaseq/skeleton/README.md | 25 +++++- 7 files changed, 110 insertions(+), 78 deletions(-) rename inst/rmarkdown/templates/rnaseq/skeleton/{ => DE}/load_data.R (80%) diff --git a/DESCRIPTION b/DESCRIPTION index e8d6962..460e302 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -20,6 +20,8 @@ Imports: R.utils Suggests: knitr, - rmarkdown + rmarkdown, + testthat (>= 3.0.0) VignetteBuilder: knitr URL: http://bcb.io/bcbioR/ +Config/testthat/edition: 3 diff --git a/inst/rmarkdown/templates/common/skeleton/README.md b/inst/rmarkdown/templates/common/skeleton/README.md index 1cb86f1..3f00ca3 100644 --- a/inst/rmarkdown/templates/common/skeleton/README.md +++ b/inst/rmarkdown/templates/common/skeleton/README.md @@ -14,10 +14,11 @@ - `meta` should contain the CSV/YAML files used by *bcbio* or *nextflow* - `scripts` should contain `sbatch` scripts or any custom scripts used in this project -- `data` contains raw data +- `data` contains raw data, it can contains big data objects - `reports` contains `Rmd` and `html` together with their files that will be added to *DropBox*. Each type of project have different guidelines. - `final` contains the output of *nextflow/bcbio* -- For any relevant files or papers use the `docs` folder on *DropBox* +- `code` contains any other files that support custom analysis and don't generate a report +- For any relevant client files or papers use the `docs` folder on *DropBox* ## Download diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index b1f3452..5c9564a 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -19,11 +19,13 @@ editor_options: params: numerator: tumor denominator: normal - subset_value: NA + column: sample_type + subset_column: null + subset_value: null ruv: true - params_file: ./inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R - project_file: ./inst/rmarkdown/templates/rnaseq/skeleton/information.R - functions_file: ./inst/rmarkdown/templates/rnaseq/skeleton/load_data.R + params_file: params_de.R + project_file: ../information.R + functions_file: load_data.R --- @@ -31,6 +33,30 @@ params: source(params$params_file) source(params$project_file) source(params$functions_file) +# set this values if you are not using the parameters at the top +column=params$column +numerator=params$numerator +denominator=params$denominator +subset_column=params$subset_column +subset_value=params$subset_value +run_ruv=params$ruv +``` + + +```{r load_data} +# this code will load from bcbio or nf-core folder +# NOTE make sure to set numerator and denominator +coldata <- load_coldata(coldata_fn, column, + numerator, denominator, + subset_column, subset_value) +metrics <- load_metrics(se_object, multiqc_data_dir) %>% + left_join(coldata, by = c('sample' = 'description')) +rownames(metrics) <- metrics$sample +counts <- load_counts(counts_fn) +counts <- counts[,colnames(counts) %in% coldata$description] +# if the names don't match in order or string check files names and coldata information +counts = counts[rownames(coldata)] +stopifnot(all(names(counts) == rownames(coldata))) ``` ```{r load_libraries, cache = FALSE, message = FALSE, warning=FALSE, echo=FALSE,} @@ -85,18 +111,18 @@ sanitize_datatable = function(df, ...) { - Analyst: `r analyst` - Experiment: `r experiment` - Aim: `r aim` -- Comparison: `r paste0(params$subset_value, ': ', params$numerator, ' vs. ', params$denominator)` +- Comparison: `r paste0(subset_value, ': ', numerator, ' vs. ', denominator)` ```{r create_filenames} -if (!is.na(params$subset_value)){ - filenames = str_interp("${params$subset_value}_${params$numerator}_vs_${params$denominator}") +if (!is.null(subset_value)){ + filenames = str_interp("${subset_value}_${numerator}_vs_${denominator}") } else { - filenames = str_interp("${params$numerator}_vs_${params$denominator}") + filenames = str_interp("${numerator}_vs_${denominator}") } -contrasts = c(column,params$numerator,params$denominator) -coef=paste0(column,"_",params$numerator,"_vs_",params$denominator) +contrasts = c(column,numerator,denominator) +coef=paste0(column,"_",numerator,"_vs_",denominator) name_expression_fn=file.path( basedir, @@ -110,25 +136,8 @@ name_pathways_fn=file.path( ``` -```{r load_coldata} -coldata <- load_coldata(coldata_fn, params$numerator, params$denominator, subset_column, params$subset_value) -``` - -```{r load metrics} -metrics <- load_metrics(se_object, multiqc_data_dir) %>% - left_join(coldata, by = c('sample' = 'description')) -rownames(metrics) <- metrics$sample -``` - ```{r load_counts_data} -counts <- load_counts(counts_fn) -counts <- counts[,colnames(counts) %in% coldata$description] - -# if the names don't match in order or string check files names and coldata information -counts = counts[rownames(coldata)] -stopifnot(all(names(counts) == rownames(coldata))) - rdata = AnnotationDbi::select(org.Hs.eg.db, rownames(counts), 'SYMBOL', 'ENSEMBL') %>% dplyr::select(gene_id = ENSEMBL, gene_name = SYMBOL) @@ -172,14 +181,14 @@ vsd_before <- vst(dds_to_use) norm_matrix = assay(vsd_before) ``` -```{r, eval=params$ruv, results='asis', echo=FALSE} +```{r, eval=run_ruv, results='asis', echo=FALSE} cat("# Remove Unwanted Variability When performing differential expression analysis, it is important to ensure that any detected differences are truly a result of the experimental comparison being made and not any additional variability in the data.") ``` -```{r do_RUV, eval=params$ruv} +```{r do_RUV, eval=run_ruv} # If you want to skip the code, just set up formula to be your model in the next chunk of code design <- coldata[[column]] diffs <- makeGroups(design) @@ -202,7 +211,7 @@ pca2 + scale_color_cb_friendly() ``` -```{r after_RUV, eval=params$ruv} +```{r after_RUV, eval=run_ruv} dds_to_use <- DESeqDataSetFromMatrix(counts, new_cdata, design = formula) vsd_to_use<- vst(dds_to_use, blind=FALSE) @@ -384,21 +393,21 @@ pathways_ora_all %>% sanitize_datatable() ```{r write-files} counts_norm=norm_matrix %>% as.data.frame() %>% rownames_to_column("gene_id") %>% - mutate(comparison = str_interp("${params$numerator}_vs_${params$denominator}")) + mutate(comparison = str_interp("${numerator}_vs_${denominator}")) res_for_writing <- res %>% - mutate(comparison = str_interp("${params$numerator}_vs_${params$denominator}")) + mutate(comparison = str_interp("${numerator}_vs_${denominator}")) pathways_for_writing <- pathways_long %>% - mutate(comparison = str_interp("${params$numerator}_vs_${params$denominator}")) + mutate(comparison = str_interp("${numerator}_vs_${denominator}")) -if (!is.na(params$subset_value)){ +if (!is.null(subset_value)){ counts_norm <- counts_norm %>% - mutate(subset = params$subset_value) + mutate(subset = subset_value) res_for_writing <- res_for_writing %>% - mutate(subset = params$subset_value) + mutate(subset = subset_value) pathways_for_writing <- pathways_for_writing %>% - mutate(subset = params$subset_value) + mutate(subset = subset_value) } write_csv(counts_norm, name_expression_fn) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/load_data.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R similarity index 80% rename from inst/rmarkdown/templates/rnaseq/skeleton/load_data.R rename to inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R index 149da90..bdfda5c 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/load_data.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R @@ -1,34 +1,35 @@ library(tidyverse) +library(SummarizedExperiment) load_metrics <- function(se_object, multiqc_data_dir){ - + # bcbio input if (!is.na(se_object)){ - - se <- readRDS(se_object) - metrics <- metadata(se)$metrics %>% mutate(sample = toupper(sample)) #%>% + + se <- readRDS(se_object) + metrics <- metadata(se)$metrics %>% mutate(sample = toupper(sample)) %>% as.data.frame() # left_join(coldata %>% rownames_to_column('sample')) %>% column_to_rownames('sample') - + } else { #nf-core input - + metrics <- read_tsv(paste0(multiqc_data_dir, 'multiqc_general_stats.txt')) metrics_qualimap <- read_tsv(paste0(multiqc_data_dir, 'mqc_qualimap_genomic_origin_1.txt')) - + metrics <- metrics %>% full_join(metrics_qualimap) metrics <- metrics %>% - clean_names() %>% + clean_names() %>% dplyr::rename_with(~gsub('.*mqc_generalstats_', '', .)) - - total_reads <- metrics %>% - filter(grepl('_', sample)) %>% + + total_reads <- metrics %>% + filter(grepl('_', sample)) %>% remove_empty(which = 'cols') %>% dplyr::rename(single_sample = sample) %>% mutate(sample = gsub('_[12]+$', '', single_sample)) %>% group_by(sample) %>% summarize(total_reads = sum(fastqc_raw_total_sequences)) - - metrics <- metrics %>% - filter(!grepl('_', sample)) %>% + + metrics <- metrics %>% + filter(!grepl('_', sample)) %>% remove_empty(which = 'cols') %>% full_join(total_reads) %>% mutate(mapped_reads = samtools_reads_mapped) %>% @@ -38,16 +39,16 @@ load_metrics <- function(se_object, multiqc_data_dir){ mutate(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% mutate(x5_3_bias = qualimap_5_3_bias) %>% dplyr::select( - sample, + sample, total_reads, mapped_reads, - exonic_rate, + exonic_rate, intronic_rate, intergenic_rate, - r_rna_rate, + r_rna_rate, x5_3_bias - ) - + ) %>% as.data.frame() + # metrics <- metrics %>% # full_join(meta_df , by = c("sample" = "sample")) } @@ -55,39 +56,39 @@ load_metrics <- function(se_object, multiqc_data_dir){ return(metrics) } -load_coldata <- function(coldata_fn, numerator, denominator, subset_column = NA, subset_value = NA){ +load_coldata <- function(coldata_fn, column, numerator, denominator, subset_column = NULL, subset_value = NULL){ coldata=read.csv(coldata_fn) stopifnot(column %in% names(coldata)) - + # use only some samples, by default use all - if (!is.na(subset_column)){ + if (!is.null(subset_column)){ coldata <- coldata[coldata[[paste(subset_column)]] == subset_value, ] } - coldata <- coldata[coldata[[paste(column)]] %in% c(numerator, denominator), ] - + #coldata <- coldata[coldata[[paste(column)]] %in% c(numerator, denominator), ] + if('description' %in% names(coldata)){ rownames(coldata) <- coldata$description - } - else { + }else { rownames(coldata) <- coldata$sample coldata$description <- coldata$sample } + #browser() coldata[[column]] = relevel(as.factor(coldata[[column]]), denominator) - + return(coldata) } load_counts <- function(counts_fn){ - + # bcbio input if(grepl('csv', counts_fn)){ counts <- read_csv(counts_fn) %>% column_to_rownames('gene') } else { # nf-core input - counts <- read_tsv(counts_fn) %>% dplyr::select(-gene_name) %>% + counts <- read_tsv(counts_fn) %>% dplyr::select(-gene_name) %>% mutate(gene_id = str_sub(gene_id, 1, 15)) %>% column_to_rownames('gene_id') %>% round - + return(counts) } - -} \ No newline at end of file + +} diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R index e6cc763..edbcf5d 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R @@ -1,14 +1,12 @@ # project params date = "YYYYMMDD" -column = "sample_type" -subset_column = NA basedir <- './' # params for bcbio coldata_fn = "https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/coldata.csv" counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/tximport-counts.csv' se_object=url("https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/bcbio-se.rds") -# +# # params for nfcore # coldata_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' # counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.tsv' diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R index c6d1217..18d6398 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R @@ -1,5 +1,5 @@ library(rmarkdown) - +# set working directory to this file before using the function render_de <- function(numerator, denominator, subset_value = NA, params_file = 'params_de.R'){ diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/README.md b/inst/rmarkdown/templates/rnaseq/skeleton/README.md index 9df2e43..ec942d7 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/README.md +++ b/inst/rmarkdown/templates/rnaseq/skeleton/README.md @@ -1,6 +1,27 @@ # Guideline for RNAseq downstream analysis -# DropBox +Make sure there is a project name for this. + +## Run data with nf-core rnaseq + +- Make sure you have access to our [Seqera WorkSpace](https://cloud.seqera.io/orgs/HBC/workspaces/core_production/launchpad) +- Transfer data to HCBC S3: Ask Alex/Lorena. Files will be at our S3 bucket `input/rawdata` folder +- Prepare the CSV file according this [instructions](https://nf-co.re/rnaseq/3.14.0/docs/usage#multiple-runs-of-the-same-sample). File should look like this: + +```csv +sample,fastq_1,fastq_2,strandedness +CONTROL_REP1,s3path/AEG588A1_S1_L002_R1_001.fastq.gz,s3path/AEG588A1_S1_L002_R2_001.fastq.gz,auto +CONTROL_REP1,s3path/AEG588A1_S1_L003_R1_001.fastq.gz,s3path/AEG588A1_S1_L003_R2_001.fastq.gz,auto +CONTROL_REP1,s3path/AEG588A1_S1_L004_R1_001.fastq.gz,s3path/AEG588A1_S1_L004_R2_001.fastq.gz,auto +``` +- Upload file to our `Datasets` in Seqera using the name of the project but starting with `nfcore-rnaseq` +- Go to `Launchpad`, select `nf-core_rnaseq` pipeline, and select the previous created `Datasets` in the `input` parameter after clicking in `Browser` + - Select an output directory with the same name used for the `Dataset` inside the `results` folder in S3 +- When pipeline is down, data will be copied to our on-premise HPC + +## Downstream analysis + +## DropBox - In `reports/QC` - [ ] copy `bcbio-se.rds` and `tximport-counts.csv` @@ -16,7 +37,7 @@ - [ ] Significant genes results file as described above, but additionally append columns containing normalized count values for each sample. - Make sure to append the gene symbols to these tables so the researcher can interpret the results. -# GitHub +## GitHub - [ ] Push all `*Rmd` `*R` files used for the *QC* and *DE* analysis respecting folder structure. From 208257b9df2bf2e866ee81bffb5b7ba53aefd59f Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 6 May 2024 13:11:42 -0400 Subject: [PATCH 04/55] update NEWS --- NEWS.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/NEWS.md b/NEWS.md index 7c10e9e..150abe5 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,7 @@ +# bcbioR 0.1.2 + +* Adapt templates to nf-core rnaseq + # bcbioR 0.1.1 * Add page to github From 60d210500aa496463ee70ff4f1a2bd29f1838f9a Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 6 May 2024 15:16:42 -0400 Subject: [PATCH 05/55] add docs for multiplicative DE --- README.Rmd | 2 +- README.md | 2 +- .../skeleton/DE/Multiplicative_DE_docs.md.md | 129 ++++++++++++++++++ .../templates/rnaseq/skeleton/README.md | 26 ++++ tests/testthat.R | 12 ++ tests/testthat/rnaseq.R | 28 ++++ tests/testthat/setup.R | 3 + 7 files changed, 200 insertions(+), 2 deletions(-) create mode 100644 inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md.md create mode 100644 tests/testthat.R create mode 100644 tests/testthat/rnaseq.R create mode 100644 tests/testthat/setup.R diff --git a/README.Rmd b/README.Rmd index b58c0b8..b45480c 100644 --- a/README.Rmd +++ b/README.Rmd @@ -48,7 +48,7 @@ bcbio_templates(type="rnaseq", outpath="test_folder/reports") ### Discover more... -Go to the vignette to know more `vignette("bcbioR_quick_start,package="bcbioR")` +Go to the vignette to know more `vignette("bcbioR_quick_start",package="bcbioR")` ## How to Contribute diff --git a/README.md b/README.md index e9019da..3ec9104 100644 --- a/README.md +++ b/README.md @@ -54,7 +54,7 @@ bcbio_templates(type="rnaseq", outpath="test_folder/reports") ### Discover more… Go to the vignette to know more -`vignette("bcbioR_quick_start,package="bcbioR")` +`vignette("bcbioR_quick_start",package="bcbioR")` ## How to Contribute diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md.md b/inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md.md new file mode 100644 index 0000000..aa35021 --- /dev/null +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md.md @@ -0,0 +1,129 @@ +# Overview + +This is an example of complex DE analysis with multiple covariates with multiple levels. + +We have the SEX variable (2 levels) and the GENOTYPE VARIABLE (4 levels) + +# Intercept Analysis + +``` +# Model design and creating dds object from the dataset +design = ~sex + genotype + sex:genotype +dds <- DESeqDataSet(se_Striatum, design) +``` + +## Filtering lowly expressed genes +We are filtering out genes with fewer than 10 raw counts in total and are present in fewer than 3 samples. + +``` +keep <- rowSums(counts(dds)>=10) >=4 +dds <- dds[keep, ] +#dds # comment out this line to print the dds object and compare the dimension of the dataset before and after filtering is applied. +``` + +setting up WT as reference genotype and Male and reference sex. Otherwise DESeq2 will use the conditions in their alphabetical order. + +``` +dds$genotype <- relevel(dds$genotype, ref = "WT") +dds$sex <- relevel(dds$sex, ref = "Male") + +#Checking model design and reference condition comment out the three lines below to print the design and order of genotype and sex +design(dds) +levels(dds$genotype) +levels(dds$sex) + +#estimating size factors for normalization and fitting our model with DESeq model +dds <- estimateSizeFactors(dds) +dds <- DESeq(dds) +resultsNames(dds) #This will print out the name of coefficients being compared, comment it out to view + +# get coefficient matrix +mod_mat <- model.matrix(design(dds), data = colData(dds)) +mod_mat + +(Intercept) sexFemale genotypeCR3KO genotypeQ175 genotypeQ175_CR3KO sexFemale:genotypeCR3KO sexFemale:genotypeQ175 sexFemale:genotypeQ175_CR3KO +a10_st_q175_m_r1 1 0 0 1 0 0 0 0 +a12_st_q175_f_r1 1 1 0 1 0 0 1 0 +a14_st_wt_m_r1 1 0 0 0 0 0 0 0 +a16_st_wt_f_r1 1 1 0 0 0 0 0 0 +``` + + +coefficient weights extracted from the mod_mat above + +``` +WT_M <- c(1, 0, 0, 0, 0, 0, 0, 0) +WT_F <- c(1, 1, 0, 0, 0, 0, 0, 0) +WTCR3ko_M <- c(1, 0, 1, 0, 0, 0, 0, 0) +WTCR3ko_F <- c( 1, 1, 1, 0, 0, 1, 0, 0) +Q175_M <- c(1, 0, 0, 1, 0, 0, 0, 0) +Q175_F <- c(1, 1, 0, 1, 0, 0, 1, 0) +Q175CR3ko_M <- c(1, 0, 0, 0, 1, 0, 0, 0) +Q175CR3ko_F <- c(1, 1, 0, 0, 1, 0, 0, 1) +``` + +# Differential gene expression analysis + +## Comp_2: Female vs Male : WTCR3ko +``` +comp2_F.v.M_WTCR3ko <- results(dds, contrast = c(WTCR3ko_F - WTCR3ko_M)) +comp2_F.v.M_WTCR3ko_shrink <- lfcShrink(dds, contrast = c(WTCR3ko_F - WTCR3ko_M), type = "ashr") +summary(comp2_F.v.M_WTCR3ko) +``` + +## Comp_3: Female vs Male : Q175 +``` +comp3_F.v.M_Q175 <- results(dds, contrast = c(Q175_F - Q175_M)) +comp3_F.v.M_Q175_shrink <- lfcShrink(dds, contrast = c(Q175_F - Q175_M), type = "ashr") +summary(comp3_F.v.M_Q175) +``` + +## Comp_5: WTCR3ko vs WT : Male +```{r} +comp5_WTCR3ko.v.WT_Male <- results(dds, contrast = c(WTCR3ko_M - WT_M)) +comp5_WTCR3ko.v.WT_Male_shrink <- lfcShrink(dds, contrast = c(WTCR3ko_M - WT_M), type = "ashr") +summary(comp5_WTCR3ko.v.WT_Male) +``` + +## Comp_6: WTCR3ko vs WT : Female +```{r} +comp6_WTCR3ko.v.WT_Female <- results(dds, contrast = c(WTCR3ko_F - WT_F)) +comp6_WTCR3ko.v.WT_Female_shrink <- lfcShrink(dds, contrast = c(WTCR3ko_F - WT_F), type = "ashr") +summary(comp6_WTCR3ko.v.WT_Female) +``` + +## Comp_11: Q175CR3ko vs Q175 : Male +``` +comp11_Q175CR3ko.v.Q175_Male <- results(dds, contrast = c(Q175CR3ko_M - Q175_M)) +comp11_Q175CR3ko.v.Q175_Male_shrink <- lfcShrink(dds, contrast = c(Q175CR3ko_M - Q175_M), type = "ashr") +summary(comp11_Q175CR3ko.v.Q175_Male) +``` + +## Comp_12: Q175CR3ko vs Q175 : Female +``` +comp12_Q175CR3ko.v.Q175_Female <- results(dds, contrast = c(Q175CR3ko_F - Q175_F)) +comp12_Q175CR3ko.v.Q175_Female_shrink <- lfcShrink(dds, contrast = c(Q175CR3ko_F - Q175_F), type = "ashr") +summary(comp12_Q175CR3ko.v.Q175_Female) +``` + +## Comp_15: (Q175CR3ko-Q176) - (WTCR3ko - WT) : Male + +Does the CR3 knockout in Q175 differ from CR3 knockout in WT for Males? + +``` +comp15_CR3koinQ175.v.CR3koinWT_Male <- results(dds, + contrast = c(Q175CR3ko_M - Q175_M) - (WTCR3ko_M - WT_M)) +comp15_CR3koinQ175.v.CR3koinWT_Male_shrink <- lfcShrink(dds, + contrast = c(Q175CR3ko_M - Q175_M) - (WTCR3ko_M - WT_M), type = "ashr") +summary(comp15_CR3koinQ175.v.CR3koinWT_Male) +``` + +## Comp_17: (WTCR3koall) - (WTall) + +Does the average of the samples in WTCR3KO differ from average of the samples in WT + +``` +comp17_WTCR3all.v.WTall <- results(dds, contrast = c(WTCR3ko_M + WTCR3ko_F)/2 - (WT_M + WT_F)/2) +comp17_WTCR3all.v.WTall_shrink <- lfcShrink(dds, contrast = c((WTCR3ko_M + WTCR3ko_F)/2 - (WT_M + WT_F)/2), type = "ashr") +summary(comp17_WTCR3all.v.WTall) +``` diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/README.md b/inst/rmarkdown/templates/rnaseq/skeleton/README.md index ec942d7..f6014de 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/README.md +++ b/inst/rmarkdown/templates/rnaseq/skeleton/README.md @@ -21,6 +21,32 @@ CONTROL_REP1,s3path/AEG588A1_S1_L004_R1_001.fastq.gz,s3path/AEG588A1_S1_L004_R2_ ## Downstream analysis +Please, modify `information.R` with the right information. You can use this file with any other Rmd to include the project/analysis information. + +### QC + +`QC/QC.Rmd` is a template for QC metrics. Use `params_qc.R` for `bcbio` + or `QC/QC_nf-core.Rmd` `params_qc_nf-core.R` for `nf-core/rnaseq` outputs. + +Read instruction in the R and Rmd scripts to render it. + +### DE + +`DE/DEG.Rmd` is a template for two groups comparison. `params_de.R` has the information of the input files to load. You can point to `bcbio` or `nf-core/rnaseq` output files. + +On the `YAML` header file of the `Rmd` you can specify some parameters or just set them up in the first chunk of code of the template. This template has examples of: + +- sub-setting data +- two groups comparison +- volcano plot +- MA plot +- Pathway analysis +- Tables + +There are some code related to alternative analysis: + +- `DE/Multiplicative_DE_docs.md` that shows some cases when there is multiple variables in the model with multiple levels: sex (2 levels) and genotype (4 levels) + ## DropBox - In `reports/QC` diff --git a/tests/testthat.R b/tests/testthat.R new file mode 100644 index 0000000..93f2dbb --- /dev/null +++ b/tests/testthat.R @@ -0,0 +1,12 @@ +# This file is part of the standard setup for testthat. +# It is recommended that you do not modify it. +# +# Where should you do additional test configuration? +# Learn more about the roles of various files in: +# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview +# * https://testthat.r-lib.org/articles/special-files.html + +library(testthat) +library(bcbioR) + +test_check("bcbioR") diff --git a/tests/testthat/rnaseq.R b/tests/testthat/rnaseq.R new file mode 100644 index 0000000..2ddf74d --- /dev/null +++ b/tests/testthat/rnaseq.R @@ -0,0 +1,28 @@ +library(bcbioR) + +test_that("rnaseq testing", { + path <- withr::local_tempdir() + print(path) + bcbio_templates(type="rnaseq", outpath=path) + numerator="tumor" + denominator="normal" + subset_value=NA + rmarkdown::render(input = file.path(path,"DE/DEG.Rmd"), + output_dir = file.path(path,"DE"), + output_format = "html_document", + output_file = ifelse(!is.na(subset_value), + paste0('DE_', subset_value, '_', numerator, '_vs_', denominator, '.html'), + paste0('DE_', numerator, '_vs_', denominator, '.html') + ), + clean = TRUE, + envir = new.env(), + params = list( + subset_value = subset_value, + numerator = numerator, + denominator = denominator, + params_file = file.path(path,'DE/params_de.R'), + project_file = file.path(path,'information.R'), + functions_file = file.path(path,'DE/load_data.R') + ) + ) +}) diff --git a/tests/testthat/setup.R b/tests/testthat/setup.R new file mode 100644 index 0000000..03fe938 --- /dev/null +++ b/tests/testthat/setup.R @@ -0,0 +1,3 @@ +op <- options(reprex.clipboard = FALSE, reprex.html_preview = FALSE) + +withr::defer(options(op), teardown_env()) From f3135a3798917099a5393dc6ab795bfee2c29f1e Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Tue, 7 May 2024 09:26:14 -0400 Subject: [PATCH 06/55] update docs in coldata and fix path to supporting files in Rmd --- inst/rmarkdown/templates/rnaseq/skeleton/QC/QC.Rmd | 4 ++-- inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd | 4 ++-- inst/rmarkdown/templates/rnaseq/skeleton/README.md | 3 +++ 3 files changed, 7 insertions(+), 4 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC.Rmd index 14a5006..48afe76 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC.Rmd @@ -17,8 +17,8 @@ output: editor_options: chunk_output_type: console params: - params_file: ./inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc.R - project_file: ./inst/rmarkdown/templates/rnaseq/skeleton/information.R + params_file: params_qc.R + project_file: ../information.R --- diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index 89b2a84..54fb3b0 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -17,8 +17,8 @@ output: editor_options: chunk_output_type: console params: - params_file: ./inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R - project_file: ./inst/rmarkdown/templates/rnaseq/skeleton/information.R + params_file: params_qc_nf-core.R + project_file: ../information.R --- diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/README.md b/inst/rmarkdown/templates/rnaseq/skeleton/README.md index f6014de..3a35cb6 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/README.md +++ b/inst/rmarkdown/templates/rnaseq/skeleton/README.md @@ -14,6 +14,9 @@ CONTROL_REP1,s3path/AEG588A1_S1_L002_R1_001.fastq.gz,s3path/AEG588A1_S1_L002_R2_ CONTROL_REP1,s3path/AEG588A1_S1_L003_R1_001.fastq.gz,s3path/AEG588A1_S1_L003_R2_001.fastq.gz,auto CONTROL_REP1,s3path/AEG588A1_S1_L004_R1_001.fastq.gz,s3path/AEG588A1_S1_L004_R2_001.fastq.gz,auto ``` + +You can add more columns to this file with more metadata, and use this file as the `coldata` file the templates. + - Upload file to our `Datasets` in Seqera using the name of the project but starting with `nfcore-rnaseq` - Go to `Launchpad`, select `nf-core_rnaseq` pipeline, and select the previous created `Datasets` in the `input` parameter after clicking in `Browser` - Select an output directory with the same name used for the `Dataset` inside the `results` folder in S3 From 366868087c235bf0e5b32e2750c62fea7e0bbc17 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Tue, 7 May 2024 09:29:52 -0400 Subject: [PATCH 07/55] update install docs --- README.Rmd | 3 ++- README.md | 7 +++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/README.Rmd b/README.Rmd index b45480c..c81fba9 100644 --- a/README.Rmd +++ b/README.Rmd @@ -14,9 +14,10 @@ The goal of `bcbioR` is to create guidelines for NGS data interpretation based o You can install the development version of bcbioR from [GitHub](https://github.com/) with: -``` r +``` # install.packages("devtools") devtools::install_github("bcbio/bcbioR") +devtools::install_github("bcbio/bcbioR",ref = "devel") ``` ## Quick start diff --git a/README.md b/README.md index 3ec9104..5314ae8 100644 --- a/README.md +++ b/README.md @@ -15,10 +15,9 @@ everybody who contributes to this package. You can install the development version of bcbioR from [GitHub](https://github.com/) with: -``` r -# install.packages("devtools") -devtools::install_github("bcbio/bcbioR") -``` + # install.packages("devtools") + devtools::install_github("bcbio/bcbioR") + devtools::install_github("bcbio/bcbioR",ref = "devel") ## Quick start From 13715aef3efe796e91f00bd1c6e9a9d14bb83338 Mon Sep 17 00:00:00 2001 From: Alex Bartlett Date: Thu, 9 May 2024 15:33:20 -0400 Subject: [PATCH 08/55] include emma's file --- .gitignore | 1 + .../rnaseq/skeleton/QC/QC_nf-core.Rmd | 134 +++++++++++------- 2 files changed, 86 insertions(+), 49 deletions(-) diff --git a/.gitignore b/.gitignore index 80e3c2f..607f2ff 100644 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,4 @@ inst/doc docs /doc/ /Meta/ +.DS* \ No newline at end of file diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index 54fb3b0..a867971 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -38,6 +38,7 @@ library(pheatmap) library(DT) library(pheatmap) library(bcbioR) +library(janitor) ggplot2::theme_set(theme_light(base_size = 14)) opts_chunk[["set"]]( cache = FALSE, @@ -116,12 +117,24 @@ sanitize_datatable = function(df, ...) { # Samples and metadata + +```{r set factor of interest} +###Main factor of interest### +# This will be used to order the samples and annotate the QC figures +FOI <- "tissue_type" +``` + + + ```{r load_metadata} -meta_df=read_csv(metadata_fn) %>% mutate(sample = description) %>% - dplyr::select(-description) +## May 8th, I edited the metadata read in to take the input csv from nf-core which MAY have duplicated lines in the case of dup lanes## +meta_df=read_csv(metadata_fn) %>% arrange(.data[[FOI]]) %>% distinct(sample, .keep_all = T) + +order <- meta_df$sample -ggplot(meta_df, aes(sample_type, fill = sample_type)) + + +ggplot(meta_df, aes(.data[[FOI]], fill = .data[[FOI]])) + geom_bar() + ylab("") + xlab("") + scale_fill_cb_friendly() ``` @@ -138,7 +151,7 @@ metrics <- metrics %>% dplyr::rename_with(~gsub('.*mqc_generalstats_', '', .)) total_reads <- metrics %>% - filter(grepl('_', sample)) %>% + dplyr::filter(grepl('_', sample)) %>% remove_empty(which = 'cols') %>% dplyr::rename(single_sample = sample) %>% mutate(sample = gsub('_[12]+$', '', single_sample)) %>% @@ -146,18 +159,22 @@ total_reads <- metrics %>% summarize(total_reads = sum(fastqc_raw_total_sequences)) metrics <- metrics %>% - filter(!grepl('_', sample)) %>% + dplyr::filter(!grepl('_', sample)) %>% remove_empty(which = 'cols') %>% full_join(total_reads) %>% mutate(mapped_reads = samtools_reads_mapped) %>% mutate(exonic_rate = exonic/(star_uniquely_mapped * 2)) %>% mutate(intronic_rate = intronic/(star_uniquely_mapped * 2)) %>% mutate(intergenic_rate = intergenic/(star_uniquely_mapped * 2)) %>% - mutate(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% + # mutate(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% mutate(x5_3_bias = qualimap_5_3_bias) metrics <- metrics %>% full_join(meta_df , by = c("sample" = "sample")) + + +### Just for this analysis, not to be permanently added##### +metrics$sample <- gsub("X4","4", metrics$sample) ``` ```{r show-metadata} @@ -177,13 +194,13 @@ Here, we want to see consistency and a minimum of 20 million reads. ```{r plot_total_reads} metrics %>% - ggplot(aes(x = sample_type, + ggplot(aes(x = factor(sample, level = order), y = total_reads, - color = sample_type)) + - geom_point(alpha=0.5) + + fill = .data[[FOI]])) + + geom_bar(stat = "identity") + coord_flip() + scale_y_continuous(name = "million reads") + - scale_color_cb_friendly() + + scale_fill_cb_friendly() + xlab("") + ggtitle("Total reads") ``` @@ -201,9 +218,9 @@ The genomic mapping rate represents the percentage of reads mapping to the refer ```{r plot_mapping_rate} metrics$mapped_reads_pct <- round(metrics$mapped_reads/metrics$total_reads*100,1) metrics %>% - ggplot(aes(x = sample_type, + ggplot(aes(x = factor(sample, level = order), y = mapped_reads_pct, - color = sample_type)) + + color = .data[[FOI]])) + geom_point() + coord_flip() + scale_color_cb_friendly() + @@ -217,7 +234,7 @@ metrics %>% The number of genes represented in every sample is expected to be consistent and over 20K (blue line). -```{r plot_genes_detected} +```{r calc_genes_detected} se <- readRDS(se_object) genes_detected <- colSums(assays(se)[["counts"]] > 0) %>% enframe() sample_names <- metrics[,c("sample"), drop=F] @@ -225,14 +242,23 @@ genes_detected <- left_join(genes_detected, sample_names, by = c("name" = "sampl genes_detected <- genes_detected %>% group_by(name) genes_detected <- summarise(genes_detected, n_genes = max(value)) + +#### Code to fix stupid names, only for this analysis. Remove for git### +genes_detected$name <- gsub("X4","4", genes_detected$name) + metrics <- metrics %>% left_join(genes_detected, by = c("sample" = "name")) -ggplot(metrics,aes(x = sample_type, - y = n_genes, color = sample_type)) + - geom_point() + + +``` + + +```{r plot_genes_detected} +ggplot(metrics,aes(x = factor(sample, level = order), + y = n_genes, fill = .data[[FOI]])) + + geom_bar(stat = "identity") + coord_flip() + - scale_color_cb_friendly() + + scale_fill_cb_friendly() + ggtitle("Number of genes") + ylab("Number of genes") + xlab("") + @@ -240,6 +266,7 @@ ggplot(metrics,aes(x = sample_type, ``` + ## Gene detection saturation This plot shows how complex the samples are. We expect samples with more reads to detect more genes. @@ -248,7 +275,7 @@ This plot shows how complex the samples are. We expect samples with more reads t metrics %>% ggplot(aes(x = total_reads, y = n_genes, - color = sample_type)) + + color = .data[[FOI]])) + geom_point()+ scale_x_log10() + scale_color_cb_friendly() + @@ -262,9 +289,9 @@ Here we are looking for consistency, and exonic mapping rates around 70% or 75% ```{r plot_exonic_mapping_rate} metrics %>% - ggplot(aes(x = sample_type, + ggplot(aes(x = factor(sample, level = order), y = exonic_rate * 100, - color = sample_type)) + + color = .data[[FOI]])) + geom_point() + ylab("Exonic rate %") + ggtitle("Exonic mapping rate") + @@ -282,9 +309,9 @@ Here, we expect a low intronic mapping rate (≤ 15% - 20%) ```{r plot_intronic_mapping_rate} metrics %>% - ggplot(aes(x = sample_type, + ggplot(aes(x = factor(sample, level = order), y = intronic_rate * 100, - color = sample_type)) + + color = .data[[FOI]])) + geom_point() + ylab("Intronic rate %") + ggtitle("Intronic mapping rate") + @@ -302,13 +329,13 @@ Here, we expect a low intergenic mapping rate, which is true for all samples. ```{r plot_intergenic_mapping_rate} metrics %>% - ggplot(aes(x = sample_type, + ggplot(aes(x = factor(sample, level = order), y = intergenic_rate * 100, - color = sample_type)) + + color = .data[[FOI]])) + geom_point() + ylab("Intergenic rate %") + ggtitle("Intergenic mapping rate") + - coord_flip() + + coord_flip() + xlab("") + scale_color_cb_friendly() + ylim(c(0, 100)) ``` @@ -321,9 +348,9 @@ Samples should have a ribosomal RNA (rRNA) "contamination" rate below 10% # for some bad samples it could be > 50% rrna_ylim <- max(round(metrics$r_rna_rate*100, 2)) + 10 metrics %>% - ggplot(aes(x = sample_type, + ggplot(aes(x = factor(sample, level = order), y = r_rna_rate * 100, - color = sample_type)) + + color = .data[[FOI]])) + geom_point() + ylab("rRNA rate, %")+ ylim(0, rrna_ylim) + @@ -338,13 +365,13 @@ There should be little bias, i.e. the values should be close to 1, or at least c ```{r plot_53_bias} metrics %>% - ggplot(aes(x = sample_type, + ggplot(aes(x = factor(sample, level = order), y = x5_3_bias, - color = sample_type)) + + color = .data[[FOI]])) + geom_point() + ggtitle("5'-3' bias") + coord_flip() + - ylim(c(0.5,1.5)) + + ylim(c(0.5,1.5)) + xlab("") + ylab("5'-3' bias") + scale_color_cb_friendly()+ geom_hline(yintercept=1, color = cb_friendly_cols('blue')) ``` @@ -354,7 +381,7 @@ metrics %>% We expect consistency in the box plots here between the samples, i.e. the distribution of counts across the genes is similar ```{r plot_counts_per_gene} -metrics_small <- metrics %>% dplyr::select(sample, sample_type) +metrics_small <- metrics %>% dplyr::select(sample, .data[[FOI]]) metrics_small <- left_join(sample_names, metrics_small) counts <- @@ -363,13 +390,18 @@ counts <- filter(rowSums(.)!=0) %>% gather(name, counts) -counts <- left_join(counts, metrics, by = c("name" = "sample")) +### Just for this analysis, not to be permanently added##### +counts$name <- gsub("X4","4", counts$name) -ggplot(counts, aes(sample_type, + +counts <- left_join(counts, metrics_small, by = c("name" = "sample")) + +ggplot(counts, aes(factor(name, level = order), log2(counts+1), - fill = sample_type)) + + fill = .data[[FOI]])) + geom_boxplot() + scale_fill_cb_friendly() + + coord_flip() + xlab("") + ggtitle("Counts per gene, all non-zero genes") + scale_color_cb_friendly() ``` @@ -390,6 +422,11 @@ raw_counts <- assays(se)[["counts"]] %>% round() %>% filter(rowSums(.)!=0) %>% as.matrix() +### Just for this analysis, not to be permanently added##### +colnames(raw_counts) <- gsub("X4","4", colnames(raw_counts)) + + + vst <- vst(raw_counts) #fix samples names @@ -397,17 +434,22 @@ coldat_for_pca <- as.data.frame(metrics) rownames(coldat_for_pca) <- coldat_for_pca$sample coldat_for_pca <- coldat_for_pca[colnames(raw_counts),] pca1 <- degPCA(vst, coldat_for_pca, - condition = "sample_type", data = T)[["plot"]] + condition = FOI, data = T)[["plot"]] pca2 <- degPCA(vst, coldat_for_pca, - condition = "sample_type", data = T, pc1="PC3", pc2="PC4")[["plot"]] + condition = FOI, data = T, pc1="PC3", pc2="PC4")[["plot"]] pca1 + scale_color_cb_friendly() pca2 + scale_color_cb_friendly() ``` -```{r, eval=FALSE} -variables=degCovariates(vst, coldat_for_pca) +```{r covariate-plot,fig.height=12, fig.width=10} +## Remove non-useful columns output by nf-core +coldat_2 <- data.frame(coldat_for_pca[,!(colnames(coldat_for_pca) %in% c("fastq_1", "fastq_2", "salmon_library_types", "salmon_compatible_fragment_ratio", "samtools_reads_mapped_percent", "samtools_reads_properly_paired_percent", "samtools_mapped_passed_pct", "strandedness"))]) + +# Remove missing data +coldat_2 <- na.omit(coldat_2) +degCovariates(vst, metadata = coldat_2) ``` @@ -416,17 +458,11 @@ variables=degCovariates(vst, coldat_for_pca) vst_cor <- cor(vst) -annotation_cols <- cb_friendly_pal('grey')(length(unique(coldat_for_pca$sample_type))) -names(annotation_cols) <- unique(coldat_for_pca$sample_type) - -p <- pheatmap(vst_cor, - annotation = coldat_for_pca %>% select(sample_type) %>% mutate(sample_type = as.factor(sample_type)), - show_rownames = T, - show_colnames = T, - color = cb_friendly_pal('heatmap')(15), - annotation_colors = list(sample_type = annotation_cols) -) -p +annotation_cols <- cb_friendly_pal('grey')(length(unique(coldat_for_pca[[FOI]]))) +names(annotation_cols) <- unique(coldat_for_pca[[FOI]]) + +## Note am still unable to get the annotation colors with the CB palette to work## +pheatmap(vst_cor, annotation_col = coldat_for_pca[,FOI, drop=F], show_rownames = T, show_colnames = T, color = cb_friendly_pal('heatmap')(15)) ``` From 5897100c3c6fcdef332ebe293f97a43b522a3ce4 Mon Sep 17 00:00:00 2001 From: Alex Bartlett Date: Fri, 10 May 2024 12:38:02 -0400 Subject: [PATCH 09/55] finish incorporating changes --- .../rnaseq/skeleton/QC/QC_nf-core.Rmd | 101 +++++++++++------- 1 file changed, 60 insertions(+), 41 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index a867971..23c4298 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -19,6 +19,7 @@ editor_options: params: params_file: params_qc_nf-core.R project_file: ../information.R + factor_of_interest: sample_type --- @@ -118,23 +119,15 @@ sanitize_datatable = function(df, ...) { # Samples and metadata -```{r set factor of interest} -###Main factor of interest### -# This will be used to order the samples and annotate the QC figures -FOI <- "tissue_type" -``` - - - ```{r load_metadata} -## May 8th, I edited the metadata read in to take the input csv from nf-core which MAY have duplicated lines in the case of dup lanes## -meta_df=read_csv(metadata_fn) %>% arrange(.data[[FOI]]) %>% distinct(sample, .keep_all = T) + +meta_df=read_csv(metadata_fn) %>% arrange(.data[[params$factor_of_interest]]) %>% distinct(sample, .keep_all = T) order <- meta_df$sample -ggplot(meta_df, aes(.data[[FOI]], fill = .data[[FOI]])) + +ggplot(meta_df, aes(.data[[params$factor_of_interest]], fill = .data[[params$factor_of_interest]])) + geom_bar() + ylab("") + xlab("") + scale_fill_cb_friendly() ``` @@ -166,15 +159,13 @@ metrics <- metrics %>% mutate(exonic_rate = exonic/(star_uniquely_mapped * 2)) %>% mutate(intronic_rate = intronic/(star_uniquely_mapped * 2)) %>% mutate(intergenic_rate = intergenic/(star_uniquely_mapped * 2)) %>% - # mutate(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% + mutate(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% mutate(x5_3_bias = qualimap_5_3_bias) metrics <- metrics %>% full_join(meta_df , by = c("sample" = "sample")) -### Just for this analysis, not to be permanently added##### -metrics$sample <- gsub("X4","4", metrics$sample) ``` ```{r show-metadata} @@ -196,13 +187,22 @@ Here, we want to see consistency and a minimum of 20 million reads. metrics %>% ggplot(aes(x = factor(sample, level = order), y = total_reads, - fill = .data[[FOI]])) + + fill = .data[[params$factor_of_interest]])) + geom_bar(stat = "identity") + coord_flip() + scale_y_continuous(name = "million reads") + scale_fill_cb_friendly() + xlab("") + ggtitle("Total reads") +metrics %>% + ggplot(aes(x = .data[[params$factor_of_interest]], + y = total_reads, + color = .data[[params$factor_of_interest]])) + + geom_point(alpha = 0.5) + + coord_flip() + + scale_y_continuous(name = "million reads") + + scale_color_cb_friendly() + xlab("") + + ggtitle("Total reads") ``` ```{r calc_min_max_pct_mapped} @@ -220,7 +220,7 @@ metrics$mapped_reads_pct <- round(metrics$mapped_reads/metrics$total_reads*100,1 metrics %>% ggplot(aes(x = factor(sample, level = order), y = mapped_reads_pct, - color = .data[[FOI]])) + + color = .data[[params$factor_of_interest]])) + geom_point() + coord_flip() + scale_color_cb_friendly() + @@ -255,7 +255,7 @@ metrics <- metrics %>% ```{r plot_genes_detected} ggplot(metrics,aes(x = factor(sample, level = order), - y = n_genes, fill = .data[[FOI]])) + + y = n_genes, fill = .data[[params$factor_of_interest]])) + geom_bar(stat = "identity") + coord_flip() + scale_fill_cb_friendly() + @@ -263,6 +263,17 @@ ggplot(metrics,aes(x = factor(sample, level = order), ylab("Number of genes") + xlab("") + geom_hline(yintercept=20000, color = cb_friendly_cols('blue')) + +metrics %>% + ggplot(aes(x = .data[[params$factor_of_interest]], + y = n_genes, + color = .data[[params$factor_of_interest]])) + + geom_point(alpha = 0.5) + + coord_flip() + + scale_y_continuous(name = "million reads") + + scale_color_cb_friendly() + xlab("") + + ggtitle("Total reads") + ``` @@ -275,7 +286,7 @@ This plot shows how complex the samples are. We expect samples with more reads t metrics %>% ggplot(aes(x = total_reads, y = n_genes, - color = .data[[FOI]])) + + color = .data[[params$factor_of_interest]])) + geom_point()+ scale_x_log10() + scale_color_cb_friendly() + @@ -291,7 +302,7 @@ Here we are looking for consistency, and exonic mapping rates around 70% or 75% metrics %>% ggplot(aes(x = factor(sample, level = order), y = exonic_rate * 100, - color = .data[[FOI]])) + + color = .data[[params$factor_of_interest]])) + geom_point() + ylab("Exonic rate %") + ggtitle("Exonic mapping rate") + @@ -311,7 +322,7 @@ Here, we expect a low intronic mapping rate (≤ 15% - 20%) metrics %>% ggplot(aes(x = factor(sample, level = order), y = intronic_rate * 100, - color = .data[[FOI]])) + + color = .data[[params$factor_of_interest]])) + geom_point() + ylab("Intronic rate %") + ggtitle("Intronic mapping rate") + @@ -331,7 +342,7 @@ Here, we expect a low intergenic mapping rate, which is true for all samples. metrics %>% ggplot(aes(x = factor(sample, level = order), y = intergenic_rate * 100, - color = .data[[FOI]])) + + color = .data[[params$factor_of_interest]])) + geom_point() + ylab("Intergenic rate %") + ggtitle("Intergenic mapping rate") + @@ -345,12 +356,12 @@ metrics %>% Samples should have a ribosomal RNA (rRNA) "contamination" rate below 10% ```{r plot_rrna_mapping_rate} -# for some bad samples it could be > 50% + rrna_ylim <- max(round(metrics$r_rna_rate*100, 2)) + 10 metrics %>% ggplot(aes(x = factor(sample, level = order), y = r_rna_rate * 100, - color = .data[[FOI]])) + + color = .data[[params$factor_of_interest]])) + geom_point() + ylab("rRNA rate, %")+ ylim(0, rrna_ylim) + @@ -367,7 +378,7 @@ There should be little bias, i.e. the values should be close to 1, or at least c metrics %>% ggplot(aes(x = factor(sample, level = order), y = x5_3_bias, - color = .data[[FOI]])) + + color = .data[[params$factor_of_interest]])) + geom_point() + ggtitle("5'-3' bias") + coord_flip() + @@ -381,7 +392,7 @@ metrics %>% We expect consistency in the box plots here between the samples, i.e. the distribution of counts across the genes is similar ```{r plot_counts_per_gene} -metrics_small <- metrics %>% dplyr::select(sample, .data[[FOI]]) +metrics_small <- metrics %>% dplyr::select(sample, .data[[params$factor_of_interest]]) metrics_small <- left_join(sample_names, metrics_small) counts <- @@ -390,15 +401,11 @@ counts <- filter(rowSums(.)!=0) %>% gather(name, counts) -### Just for this analysis, not to be permanently added##### -counts$name <- gsub("X4","4", counts$name) - - counts <- left_join(counts, metrics_small, by = c("name" = "sample")) ggplot(counts, aes(factor(name, level = order), log2(counts+1), - fill = .data[[FOI]])) + + fill = .data[[params$factor_of_interest]])) + geom_boxplot() + scale_fill_cb_friendly() + coord_flip() + xlab("") + @@ -411,7 +418,7 @@ ggplot(counts, aes(factor(name, level = order), In this section, we look at how well the different groups in the dataset cluster with each other. Samples from the same group should ideally be clustering together. We use Principal Component Analysis (PCA). -## Principal component analysis (PCA) {.tabset} +## Principal component analysis (PCA) Principal Component Analysis (PCA) is a statistical technique used to simplify high-dimensional data by identifying patterns and reducing the number of variables. In the context of gene expression, PCA helps analyze large datasets containing information about the expression levels of thousands of genes across different samples (e.g., tissues, cells). @@ -434,9 +441,9 @@ coldat_for_pca <- as.data.frame(metrics) rownames(coldat_for_pca) <- coldat_for_pca$sample coldat_for_pca <- coldat_for_pca[colnames(raw_counts),] pca1 <- degPCA(vst, coldat_for_pca, - condition = FOI, data = T)[["plot"]] + condition = params$factor_of_interest, data = T)[["plot"]] pca2 <- degPCA(vst, coldat_for_pca, - condition = FOI, data = T, pc1="PC3", pc2="PC4")[["plot"]] + condition = params$factor_of_interest, data = T, pc1="PC3", pc2="PC4")[["plot"]] pca1 + scale_color_cb_friendly() pca2 + scale_color_cb_friendly() @@ -452,18 +459,30 @@ coldat_2 <- na.omit(coldat_2) degCovariates(vst, metadata = coldat_2) ``` - -```{r clustering fig, fig.width = 10, fig.asp = .62} ## Hierarchical clustering +```{r clustering fig, fig.width = 10, fig.asp = .62} vst_cor <- cor(vst) -annotation_cols <- cb_friendly_pal('grey')(length(unique(coldat_for_pca[[FOI]]))) -names(annotation_cols) <- unique(coldat_for_pca[[FOI]]) - -## Note am still unable to get the annotation colors with the CB palette to work## -pheatmap(vst_cor, annotation_col = coldat_for_pca[,FOI, drop=F], show_rownames = T, show_colnames = T, color = cb_friendly_pal('heatmap')(15)) - +colma=meta_df %>% as.data.frame() +rownames(colma) <- colma$sample +colma <- colma[rownames(vst_cor), ] +colma <- colma %>% dplyr::select(.data[[params$factor_of_interest]]) +anno_colors=lapply(colnames(colma), function(c){ + l.col=cb_friendly_pal('grey')(length(unique(colma[[c]]))) + names(l.col)=unique(colma[[c]]) + l.col +}) +names(anno_colors)=colnames(colma) + +p <- pheatmap(vst_cor, + annotation = colma, + annotation_colors = anno_colors, + show_rownames = T, + show_colnames = T, + color = cb_friendly_pal('heatmap')(15) + ) +p ``` # R session From 952b6fd23da551b90d245126dd29b11149a2a1b2 Mon Sep 17 00:00:00 2001 From: Alex Bartlett Date: Fri, 10 May 2024 12:41:58 -0400 Subject: [PATCH 10/55] tweak spacing, remove line --- inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd | 7 ------- 1 file changed, 7 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index 23c4298..e3f5e02 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -121,12 +121,9 @@ sanitize_datatable = function(df, ...) { ```{r load_metadata} - meta_df=read_csv(metadata_fn) %>% arrange(.data[[params$factor_of_interest]]) %>% distinct(sample, .keep_all = T) - order <- meta_df$sample - ggplot(meta_df, aes(.data[[params$factor_of_interest]], fill = .data[[params$factor_of_interest]])) + geom_bar() + ylab("") + xlab("") + scale_fill_cb_friendly() @@ -242,10 +239,6 @@ genes_detected <- left_join(genes_detected, sample_names, by = c("name" = "sampl genes_detected <- genes_detected %>% group_by(name) genes_detected <- summarise(genes_detected, n_genes = max(value)) - -#### Code to fix stupid names, only for this analysis. Remove for git### -genes_detected$name <- gsub("X4","4", genes_detected$name) - metrics <- metrics %>% left_join(genes_detected, by = c("sample" = "name")) From a8197d40fc2bbd3bfcdf07029804eff8064a3936 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Fri, 10 May 2024 18:04:56 -0400 Subject: [PATCH 11/55] small aes details --- .../rnaseq/skeleton/QC/QC_nf-core.Rmd | 18 +++++++++--------- .../templates/rnaseq/skeleton/README.md | 2 +- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index e3f5e02..38a43e0 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -195,7 +195,7 @@ metrics %>% ggplot(aes(x = .data[[params$factor_of_interest]], y = total_reads, color = .data[[params$factor_of_interest]])) + - geom_point(alpha = 0.5) + + geom_point(alpha = 0.5, size=4) + coord_flip() + scale_y_continuous(name = "million reads") + scale_color_cb_friendly() + xlab("") + @@ -218,7 +218,7 @@ metrics %>% ggplot(aes(x = factor(sample, level = order), y = mapped_reads_pct, color = .data[[params$factor_of_interest]])) + - geom_point() + + geom_point(alpha = 0.5, size=4) + coord_flip() + scale_color_cb_friendly() + ylim(0, 100) + @@ -261,7 +261,7 @@ metrics %>% ggplot(aes(x = .data[[params$factor_of_interest]], y = n_genes, color = .data[[params$factor_of_interest]])) + - geom_point(alpha = 0.5) + + geom_point(alpha = 0.5, size=4) + coord_flip() + scale_y_continuous(name = "million reads") + scale_color_cb_friendly() + xlab("") + @@ -280,7 +280,7 @@ metrics %>% ggplot(aes(x = total_reads, y = n_genes, color = .data[[params$factor_of_interest]])) + - geom_point()+ + geom_point(alpha = 0.5, size=4) + scale_x_log10() + scale_color_cb_friendly() + ggtitle("Gene saturation") + @@ -296,7 +296,7 @@ metrics %>% ggplot(aes(x = factor(sample, level = order), y = exonic_rate * 100, color = .data[[params$factor_of_interest]])) + - geom_point() + + geom_point(alpha = 0.5, size=4) + ylab("Exonic rate %") + ggtitle("Exonic mapping rate") + scale_color_cb_friendly() + @@ -316,7 +316,7 @@ metrics %>% ggplot(aes(x = factor(sample, level = order), y = intronic_rate * 100, color = .data[[params$factor_of_interest]])) + - geom_point() + + geom_point(alpha = 0.5, size=4) + ylab("Intronic rate %") + ggtitle("Intronic mapping rate") + scale_color_cb_friendly() + @@ -336,7 +336,7 @@ metrics %>% ggplot(aes(x = factor(sample, level = order), y = intergenic_rate * 100, color = .data[[params$factor_of_interest]])) + - geom_point() + + geom_point(alpha = 0.5, size=4) + ylab("Intergenic rate %") + ggtitle("Intergenic mapping rate") + coord_flip() + xlab("") + @@ -355,7 +355,7 @@ metrics %>% ggplot(aes(x = factor(sample, level = order), y = r_rna_rate * 100, color = .data[[params$factor_of_interest]])) + - geom_point() + + geom_point(alpha = 0.5, size=4) + ylab("rRNA rate, %")+ ylim(0, rrna_ylim) + ggtitle("rRNA mapping rate") + @@ -372,7 +372,7 @@ metrics %>% ggplot(aes(x = factor(sample, level = order), y = x5_3_bias, color = .data[[params$factor_of_interest]])) + - geom_point() + + geom_point(alpha = 0.5, size=4) + ggtitle("5'-3' bias") + coord_flip() + ylim(c(0.5,1.5)) + xlab("") + ylab("5'-3' bias") + diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/README.md b/inst/rmarkdown/templates/rnaseq/skeleton/README.md index 3a35cb6..379cfc2 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/README.md +++ b/inst/rmarkdown/templates/rnaseq/skeleton/README.md @@ -20,7 +20,7 @@ You can add more columns to this file with more metadata, and use this file as t - Upload file to our `Datasets` in Seqera using the name of the project but starting with `nfcore-rnaseq` - Go to `Launchpad`, select `nf-core_rnaseq` pipeline, and select the previous created `Datasets` in the `input` parameter after clicking in `Browser` - Select an output directory with the same name used for the `Dataset` inside the `results` folder in S3 -- When pipeline is down, data will be copied to our on-premise HPC +- When pipeline is down, data will be copied to our on-premise HPC in the scratch system under `bcbio` folder ## Downstream analysis From e5de5158265721079d311efde26d9f8ef8763999 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Fri, 10 May 2024 19:50:47 -0400 Subject: [PATCH 12/55] Fix code with real nfcore/rnaseq projects --- NEWS.md | 3 + README.md | 68 ++++++++----------- .../templates/rnaseq/skeleton/DE/DEG.Rmd | 40 +++++------ .../templates/rnaseq/skeleton/DE/load_data.R | 41 +++++++---- .../rnaseq/skeleton/QC/QC_nf-core.Rmd | 26 ++++--- 5 files changed, 96 insertions(+), 82 deletions(-) diff --git a/NEWS.md b/NEWS.md index 150abe5..9e7c016 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,9 @@ # bcbioR 0.1.2 * Adapt templates to nf-core rnaseq +* Fix when sample start by number +* Fix when rRNA rate is missing +* Add by sample plots in QC # bcbioR 0.1.1 diff --git a/README.md b/README.md index 5314ae8..fa1267a 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,22 @@ - # bcbioR [![R-CMD-check](https://github.com/bcbio/bcbioR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/bcbio/bcbioR/actions/workflows/R-CMD-check.yaml) + -The goal of `bcbioR` is to create guidelines for NGS data interpretation -based on the experience of the Harvard Chan Bioinformatics Core and -everybody who contributes to this package. +The goal of `bcbioR` is to create guidelines for NGS data interpretation based on the experience of the Harvard Chan Bioinformatics Core and everybody who contributes to this package. ## Installation -You can install the development version of bcbioR from -[GitHub](https://github.com/) with: +You can install the development version of bcbioR from [GitHub](https://github.com/) with: - # install.packages("devtools") - devtools::install_github("bcbio/bcbioR") - devtools::install_github("bcbio/bcbioR",ref = "devel") +``` +# install.packages("devtools") +devtools::install_github("bcbio/bcbioR") +devtools::install_github("bcbio/bcbioR",ref = "devel") +``` ## Quick start @@ -30,21 +29,17 @@ bcbio_set_project() ### Set base project -The following code will pop up a Rmd template and then clicking ‘save’ -will populate that folder with HCBC data structure guidelines +The following code will pop up a Rmd template and then clicking ‘save’ will populate that folder with HCBC data structure guidelines ``` r rmarkdown::draft("project_folder",template="common",package="bcbioR") ``` -As well, You can get this by going to File -\> New File -\> R -markdown: - then `From Template`, and choose `bcbio base` - choose the -folder to deploy files, it could be a new folder or an existing one +As well, You can get this by going to File -\> New File -\> R markdown: - then `From Template`, and choose `bcbio base` - choose the folder to deploy files, it could be a new folder or an existing one ### Set RNAseq report folder -This code will populate the folder with HCBC data structure guidelines -and Rmd code: +This code will populate the folder with HCBC data structure guidelines and Rmd code: ``` r bcbio_templates(type="rnaseq", outpath="test_folder/reports") @@ -52,39 +47,36 @@ bcbio_templates(type="rnaseq", outpath="test_folder/reports") ### Discover more… -Go to the vignette to know more -`vignette("bcbioR_quick_start",package="bcbioR")` +Go to the vignette to know more `vignette("bcbioR_quick_start",package="bcbioR")` ## How to Contribute ### Open an issue -- If you find a bug -- If you want a new feature -- If you want to add code to the templates +- If you find a bug +- If you want a new feature +- If you want to add code to the templates ### Modify the code -- Clone the repository -- Make sure you are in the `devel` branch -- Create a new branch `git checkout -b feature1` -- Modify you code, add and commit -- Push to GitHub the new branch -- Create a PR from your branch to `devel` -- Assignt the PR to me or Alex +- Clone the repository +- Make sure you are in the `devel` branch +- Create a new branch `git checkout -b feature1` +- Modify you code, add and commit +- Push to GitHub the new branch +- Create a PR from your branch to `devel` +- Assignt the PR to me or Alex Some best practices when developing: -- install `devtools` -- render `README.Rmd` regularly, to keep `README.md` up-to-date. - `devtools::build_readme()` is handy for this. -- Use `usethis::use_import_from("stringr","str_replace_all")` to add a - new function you are using in the code. +- install `devtools` +- render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this. +- Use `usethis::use_import_from("stringr","str_replace_all")` to add a new function you are using in the code. ### Contributors -- Lorena Pantano -- Alex Bartlett -- Emma Berdan -- Heather Wick -- James Billingsley +- Lorena Pantano +- Alex Bartlett +- Emma Berdan +- Heather Wick +- James Billingsley diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index 5c9564a..ae22b14 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -29,7 +29,7 @@ params: --- -```{r load_params, echo = F} +```{r load_params, cache = FALSE, message = FALSE, warning=FALSE} source(params$params_file) source(params$project_file) source(params$functions_file) @@ -43,23 +43,7 @@ run_ruv=params$ruv ``` -```{r load_data} -# this code will load from bcbio or nf-core folder -# NOTE make sure to set numerator and denominator -coldata <- load_coldata(coldata_fn, column, - numerator, denominator, - subset_column, subset_value) -metrics <- load_metrics(se_object, multiqc_data_dir) %>% - left_join(coldata, by = c('sample' = 'description')) -rownames(metrics) <- metrics$sample -counts <- load_counts(counts_fn) -counts <- counts[,colnames(counts) %in% coldata$description] -# if the names don't match in order or string check files names and coldata information -counts = counts[rownames(coldata)] -stopifnot(all(names(counts) == rownames(coldata))) -``` - -```{r load_libraries, cache = FALSE, message = FALSE, warning=FALSE, echo=FALSE,} +```{r load_libraries, cache = FALSE, message = FALSE, warning=FALSE} library(rtracklayer) library(DESeq2) library(tidyverse) @@ -96,6 +80,24 @@ opts_chunk[["set"]]( set.seed(1234567890L) ``` + +```{r load_data, message=F, warning=F} +# this code will load from bcbio or nf-core folder +# NOTE make sure to set numerator and denominator +coldata <- load_coldata(coldata_fn, column, + numerator, denominator, + subset_column, subset_value) +metrics <- load_metrics(se_object, multiqc_data_dir) %>% + left_join(coldata, by = c('sample' = 'description')) %>% + as.data.frame() +rownames(metrics) <- metrics$sample +counts <- load_counts(counts_fn) +counts <- counts[,colnames(counts) %in% coldata$description] +# if the names don't match in order or string check files names and coldata information +counts = counts[rownames(coldata)] +stopifnot(all(names(counts) == rownames(coldata))) +``` + ```{r sanitize_datatable} sanitize_datatable = function(df, ...) { # remove dashes which cause wrapping @@ -156,7 +158,7 @@ norm_matrix = assay(vsd_before) ```{r covariates, fig.height = 6, fig.width = 10} degCovariates( norm_matrix, - metrics %>% as.data.frame %>% column_to_rownames('sample') + metrics, ) ``` diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R index bdfda5c..457a6e1 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R @@ -1,6 +1,6 @@ library(tidyverse) library(SummarizedExperiment) - +library(janitor) load_metrics <- function(se_object, multiqc_data_dir){ # bcbio input @@ -8,7 +8,7 @@ load_metrics <- function(se_object, multiqc_data_dir){ se <- readRDS(se_object) metrics <- metadata(se)$metrics %>% mutate(sample = toupper(sample)) %>% as.data.frame() - # left_join(coldata %>% rownames_to_column('sample')) %>% column_to_rownames('sample') + # left_join(coldata %>% rownames_to_column('sample')) %>% column_to_rownames('sample') } else { #nf-core input @@ -28,15 +28,20 @@ load_metrics <- function(se_object, multiqc_data_dir){ group_by(sample) %>% summarize(total_reads = sum(fastqc_raw_total_sequences)) + if (!("custom_content_biotype_counts_percent_r_rna" %in% colnames(metrics))){ + metrics[["custom_content_biotype_counts_percent_r_rna"]] <- rep(0,nrow(metrics)) + } + # browser() + metrics <- metrics %>% - filter(!grepl('_', sample)) %>% + filter(!grepl('_[12]$', sample)) %>% remove_empty(which = 'cols') %>% full_join(total_reads) %>% - mutate(mapped_reads = samtools_reads_mapped) %>% + dplyr::rename(mapped_reads = samtools_reads_mapped) %>% mutate(exonic_rate = exonic/(star_uniquely_mapped * 2)) %>% mutate(intronic_rate = intronic/(star_uniquely_mapped * 2)) %>% mutate(intergenic_rate = intergenic/(star_uniquely_mapped * 2)) %>% - mutate(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% + dplyr::rename(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% mutate(x5_3_bias = qualimap_5_3_bias) %>% dplyr::select( sample, @@ -47,17 +52,26 @@ load_metrics <- function(se_object, multiqc_data_dir){ intergenic_rate, r_rna_rate, x5_3_bias - ) %>% as.data.frame() + ) %>% + dplyr::select(where(~!all(is.na(.)))) %>% + as.data.frame() # metrics <- metrics %>% # full_join(meta_df , by = c("sample" = "sample")) } + metrics$sample <- make.names(metrics$sample) rownames(metrics) <- metrics$sample return(metrics) } load_coldata <- function(coldata_fn, column, numerator, denominator, subset_column = NULL, subset_value = NULL){ - coldata=read.csv(coldata_fn) + coldata=read.csv(coldata_fn) %>% + dplyr::select(!matches("fastq") & !matches("strandness")) %>% + distinct() + if('description' %in% names(coldata)){ + coldata$sample <- coldata$description + } + coldata <- coldata %>% distinct(sample, .keep_all = T) stopifnot(column %in% names(coldata)) # use only some samples, by default use all @@ -65,14 +79,11 @@ load_coldata <- function(coldata_fn, column, numerator, denominator, subset_colu coldata <- coldata[coldata[[paste(subset_column)]] == subset_value, ] } #coldata <- coldata[coldata[[paste(column)]] %in% c(numerator, denominator), ] - - if('description' %in% names(coldata)){ - rownames(coldata) <- coldata$description - }else { - rownames(coldata) <- coldata$sample - coldata$description <- coldata$sample - } #browser() + coldata$sample <- make.names(coldata$sample) + rownames(coldata) <- coldata$sample + coldata$description <- coldata$sample + coldata[[column]] = relevel(as.factor(coldata[[column]]), denominator) return(coldata) @@ -85,7 +96,7 @@ load_counts <- function(counts_fn){ counts <- read_csv(counts_fn) %>% column_to_rownames('gene') } else { # nf-core input counts <- read_tsv(counts_fn) %>% dplyr::select(-gene_name) %>% - mutate(gene_id = str_sub(gene_id, 1, 15)) %>% + mutate(gene_id = str_replace(gene_id, pattern = "\\.[0-9]$", "")) %>% column_to_rownames('gene_id') %>% round return(counts) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index 38a43e0..e89e074 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -121,7 +121,10 @@ sanitize_datatable = function(df, ...) { ```{r load_metadata} -meta_df=read_csv(metadata_fn) %>% arrange(.data[[params$factor_of_interest]]) %>% distinct(sample, .keep_all = T) +meta_df=read_csv(metadata_fn) %>% + arrange(.data[[params$factor_of_interest]]) %>% distinct(sample, .keep_all = T) %>% + select(!matches("fastq"), !matches("strandness")) +meta_df$sample <- make.names(meta_df$sample) order <- meta_df$sample ggplot(meta_df, aes(.data[[params$factor_of_interest]], fill = .data[[params$factor_of_interest]])) + @@ -132,8 +135,8 @@ ggplot(meta_df, aes(.data[[params$factor_of_interest]], fill = .data[[params$fac ```{r prepare metrics} -metrics <- read_tsv(paste0(multiqc_data_dir, 'multiqc_general_stats.txt')) -metrics_qualimap <- read_tsv(paste0(multiqc_data_dir, 'mqc_qualimap_genomic_origin_1.txt')) +metrics <- read_tsv(file.path(multiqc_data_dir, 'multiqc_general_stats.txt')) +metrics_qualimap <- read_tsv(file.path(multiqc_data_dir, 'mqc_qualimap_genomic_origin_1.txt')) metrics <- metrics %>% full_join(metrics_qualimap) metrics <- metrics %>% @@ -156,11 +159,19 @@ metrics <- metrics %>% mutate(exonic_rate = exonic/(star_uniquely_mapped * 2)) %>% mutate(intronic_rate = intronic/(star_uniquely_mapped * 2)) %>% mutate(intergenic_rate = intergenic/(star_uniquely_mapped * 2)) %>% - mutate(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% mutate(x5_3_bias = qualimap_5_3_bias) +if ("custom_content_biotype_counts_percent_r_rna" %in% colnames(metrics)){ + metrics <- mutate(metrics, r_rna_rate = custom_content_biotype_counts_percent_r_rna) +}else{ + metrics[["r_rna_rate"]] <- NA +} + + +metrics$sample <- make.names(metrics$sample) metrics <- metrics %>% - full_join(meta_df , by = c("sample" = "sample")) + full_join(meta_df , by = c("sample" = "sample")) %>% + select(where(~!all(is.na(.)))) ``` @@ -422,14 +433,9 @@ raw_counts <- assays(se)[["counts"]] %>% round() %>% filter(rowSums(.)!=0) %>% as.matrix() -### Just for this analysis, not to be permanently added##### -colnames(raw_counts) <- gsub("X4","4", colnames(raw_counts)) - - vst <- vst(raw_counts) -#fix samples names coldat_for_pca <- as.data.frame(metrics) rownames(coldat_for_pca) <- coldat_for_pca$sample coldat_for_pca <- coldat_for_pca[colnames(raw_counts),] From 37c33a09d82a412c7b015923ca131c34c581cf5c Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 13 May 2024 14:18:03 -0400 Subject: [PATCH 13/55] add samplesheet check --- NAMESPACE | 1 + NEWS.md | 1 + R/hello.R | 25 +++++++++++++++++++ inst/extdata/rnaseq_good.csv | 7 ++++++ inst/extdata/rnaseq_missing_col.csv | 7 ++++++ inst/extdata/rnaseq_na.csv | 7 ++++++ inst/extdata/rnaseq_wnumbers.csv | 7 ++++++ .../templates/rnaseq/skeleton/DE/params_de.R | 14 +++++------ .../templates/rnaseq/skeleton/README.md | 2 ++ man/bcbio_nfcore_check.Rd | 19 ++++++++++++++ tests/testthat/misc.R | 10 ++++++++ tests/testthat/rnaseq.R | 1 + 12 files changed, 94 insertions(+), 7 deletions(-) create mode 100644 inst/extdata/rnaseq_good.csv create mode 100644 inst/extdata/rnaseq_missing_col.csv create mode 100644 inst/extdata/rnaseq_na.csv create mode 100644 inst/extdata/rnaseq_wnumbers.csv create mode 100644 man/bcbio_nfcore_check.Rd create mode 100644 tests/testthat/misc.R diff --git a/NAMESPACE b/NAMESPACE index 4cf6271..4cd1ffb 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,6 @@ # Generated by roxygen2: do not edit by hand +export(bcbio_nfcore_check) export(bcbio_set_project) export(bcbio_templates) export(cb_friendly_cols) diff --git a/NEWS.md b/NEWS.md index 9e7c016..4cd6151 100644 --- a/NEWS.md +++ b/NEWS.md @@ -4,6 +4,7 @@ * Fix when sample start by number * Fix when rRNA rate is missing * Add by sample plots in QC +* Add function to check nfcore samplesheet # bcbioR 0.1.1 diff --git a/R/hello.R b/R/hello.R index 0c8188e..127ba0b 100644 --- a/R/hello.R +++ b/R/hello.R @@ -3,6 +3,31 @@ return(x) } + +#' Function to check samplesheet for nf-core +#' +#' @param file path to CSV file for nf-core +#' @examples +#' +#' bcbio_nfcore_check(system.file("extdata", "rnaseq_good.csv", package = "bcbiR") ) +#' +#' @export +bcbio_nfcore_check <- function(file){ + required=c("sample","fastq_1","fastq_2","strandedness") + samplesheet=read_csv(file) + + if (!(all(required %in% colnames(samplesheet)))){ + print(colnames(samplesheet)) + stop("Missing required columns ", paste(required, collapse = " ")) + }else if (any(grepl("^[1-9]", samplesheet[["sample"]]))){ + stop("Avoid samples starting with numbers ") + }else if (any(is.na(samplesheet))){ + warning("Columns with missing values") + }else{ + message("All good.") + } +} + #' Function to help deploy analysis folder inside a project folder #' #' This function contains Rmd, R, md, files that help to structure diff --git a/inst/extdata/rnaseq_good.csv b/inst/extdata/rnaseq_good.csv new file mode 100644 index 0000000..dfa8b78 --- /dev/null +++ b/inst/extdata/rnaseq_good.csv @@ -0,0 +1,7 @@ +sample,fastq_1,fastq_2,strandedness,tissue,sex,4_weeks,bone_tissue +Fourwkbone1,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L001_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone1,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L002_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone2,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L001_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone2,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L002_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone3,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L001_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Male,4_weeks,bone_tissue +Fourwkbone3,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L002_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Male,4_weeks,bone_tissue diff --git a/inst/extdata/rnaseq_missing_col.csv b/inst/extdata/rnaseq_missing_col.csv new file mode 100644 index 0000000..f588040 --- /dev/null +++ b/inst/extdata/rnaseq_missing_col.csv @@ -0,0 +1,7 @@ +sample,fastq_1,strandedness,tissue,sex,4_weeks,bone_tissue +Fourwkbone1,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone1,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone2,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone2,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone3,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Male,4_weeks,bone_tissue +Fourwkbone3,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Male,4_weeks,bone_tissue diff --git a/inst/extdata/rnaseq_na.csv b/inst/extdata/rnaseq_na.csv new file mode 100644 index 0000000..a685b99 --- /dev/null +++ b/inst/extdata/rnaseq_na.csv @@ -0,0 +1,7 @@ +sample,fastq_1,fastq_2,strandedness,tissue,sex,4_weeks,bone_tissue +Fourwkbone1,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L001_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone1,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L002_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone2,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L001_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone2,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L002_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +Fourwkbone3,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L001_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Male,4_weeks,bone_tissue +Fourwkbone3,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L002_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Male,,bone_tissue diff --git a/inst/extdata/rnaseq_wnumbers.csv b/inst/extdata/rnaseq_wnumbers.csv new file mode 100644 index 0000000..75bf6d1 --- /dev/null +++ b/inst/extdata/rnaseq_wnumbers.csv @@ -0,0 +1,7 @@ +sample,fastq_1,fastq_2,strandedness,tissue,sex,4_weeks,bone_tissue +4wkbone1,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L001_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +4wkbone1,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L002_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_E02v1_4wkbone1_S13_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +4wkbone2,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L001_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +4wkbone2,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L002_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_F02v1_4wkbone2_S14_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Female,4_weeks,bone_tissue +4wkbone3,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L001_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L001_R2_001.fastq.gz,auto,4 week old bone tissue,Male,4_weeks,bone_tissue +4wkbone3,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L002_R1_001.fastq.gz,s3://hcbc-test/input/rawdata/P496_EMoor18780_G02v1_4wkbone3_S15_L002_R2_001.fastq.gz,auto,4 week old bone tissue,Male,,bone_tissue diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R index edbcf5d..3a32cf9 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R @@ -3,12 +3,12 @@ date = "YYYYMMDD" basedir <- './' # params for bcbio -coldata_fn = "https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/coldata.csv" -counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/tximport-counts.csv' -se_object=url("https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/bcbio-se.rds") +# coldata_fn = "https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/coldata.csv" +# counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/tximport-counts.csv' +# se_object=url("https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/bcbio-se.rds") # # params for nfcore -# coldata_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' -# counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.tsv' -# multiqc_data_dir = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/multiqc-report-data/' -# se_object = NA +coldata_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' +counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.tsv' +multiqc_data_dir = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/multiqc-report-data/' +se_object = NA diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/README.md b/inst/rmarkdown/templates/rnaseq/skeleton/README.md index 379cfc2..bdf1941 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/README.md +++ b/inst/rmarkdown/templates/rnaseq/skeleton/README.md @@ -15,6 +15,8 @@ CONTROL_REP1,s3path/AEG588A1_S1_L003_R1_001.fastq.gz,s3path/AEG588A1_S1_L003_R2_ CONTROL_REP1,s3path/AEG588A1_S1_L004_R1_001.fastq.gz,s3path/AEG588A1_S1_L004_R2_001.fastq.gz,auto ``` +Use `bcbio_nfcore_check(csv_file)` to check the file is correct. + You can add more columns to this file with more metadata, and use this file as the `coldata` file the templates. - Upload file to our `Datasets` in Seqera using the name of the project but starting with `nfcore-rnaseq` diff --git a/man/bcbio_nfcore_check.Rd b/man/bcbio_nfcore_check.Rd new file mode 100644 index 0000000..63348b3 --- /dev/null +++ b/man/bcbio_nfcore_check.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/hello.R +\name{bcbio_nfcore_check} +\alias{bcbio_nfcore_check} +\title{Function to check samplesheet for nf-core} +\usage{ +bcbio_nfcore_check(file) +} +\arguments{ +\item{file}{path to CSV file for nf-core} +} +\description{ +Function to check samplesheet for nf-core +} +\examples{ + +bcbio_nfcore_check(system.file("extdata", "rnaseq_good.csv", package = "bcbiR") ) + +} diff --git a/tests/testthat/misc.R b/tests/testthat/misc.R new file mode 100644 index 0000000..eebdfc8 --- /dev/null +++ b/tests/testthat/misc.R @@ -0,0 +1,10 @@ +library(bcbioR) + + +test_that("rnaseq samplesheet", { + + expect_no_error(bcbio_nfcore_check(system.file("extdata", "rnaseq_good.csv", package = "bcbioR"))) + expect_error(bcbio_nfcore_check(system.file("extdata", "rnaseq_missing_col.csv", package = "bcbioR"))) + expect_error(bcbio_nfcore_check(system.file("extdata", "rnaseq_wnumber.csv", package = "bcbioR"))) + expect_warning(bcbio_nfcore_check(system.file("extdata", "rnaseq_na.csv", package = "bcbioR"))) +}) diff --git a/tests/testthat/rnaseq.R b/tests/testthat/rnaseq.R index 2ddf74d..9a683ac 100644 --- a/tests/testthat/rnaseq.R +++ b/tests/testthat/rnaseq.R @@ -1,5 +1,6 @@ library(bcbioR) + test_that("rnaseq testing", { path <- withr::local_tempdir() print(path) From 32bf8481a3d8b7596652b5a7965943e49e22cd56 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 13 May 2024 15:16:24 -0400 Subject: [PATCH 14/55] add PCA viariance analysis --- .Rbuildignore | 1 + DESCRIPTION | 5 ++- NAMESPACE | 1 + NEWS.md | 1 + R/bcbioR-package.R | 3 +- R/data.R | 8 +++++ R/hello.R | 2 +- data-raw/DATASET.R | 29 ++++++++++++++++ data/bcbio_vsd_data.rda | Bin 0 -> 1505335 bytes .../skeleton/DE/PCA_variance_analysis.Rmd | 32 ++++++++++++++++++ man/bcbio_nfcore_check.Rd | 2 +- man/bcbio_vsd_data.Rd | 20 +++++++++++ 12 files changed, 100 insertions(+), 4 deletions(-) create mode 100644 R/data.R create mode 100644 data-raw/DATASET.R create mode 100644 data/bcbio_vsd_data.rda create mode 100644 inst/rmarkdown/templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd create mode 100644 man/bcbio_vsd_data.Rd diff --git a/.Rbuildignore b/.Rbuildignore index 6e71100..73a7c9a 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -11,3 +11,4 @@ test/* inst/*/*/*/*/*/*html inst/*/*/*/*/*/*csv +^data-raw$ diff --git a/DESCRIPTION b/DESCRIPTION index 460e302..9eaabc3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -17,7 +17,8 @@ Imports: hues, ggprism, grDevices, - R.utils + R.utils, + readr Suggests: knitr, rmarkdown, @@ -25,3 +26,5 @@ Suggests: VignetteBuilder: knitr URL: http://bcb.io/bcbioR/ Config/testthat/edition: 3 +Depends: + R (>= 2.10) diff --git a/NAMESPACE b/NAMESPACE index 4cd1ffb..9fce5a5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -14,4 +14,5 @@ import(ggprism) import(hues) importFrom(grDevices,colorRampPalette) importFrom(magrittr,"%>%") +importFrom(readr,read_csv) importFrom(stringr,str_replace_all) diff --git a/NEWS.md b/NEWS.md index 4cd6151..a70cefe 100644 --- a/NEWS.md +++ b/NEWS.md @@ -5,6 +5,7 @@ * Fix when rRNA rate is missing * Add by sample plots in QC * Add function to check nfcore samplesheet +* Add PCA with variance analysis # bcbioR 0.1.1 diff --git a/R/bcbioR-package.R b/R/bcbioR-package.R index 3f87d74..122b9ef 100644 --- a/R/bcbioR-package.R +++ b/R/bcbioR-package.R @@ -2,9 +2,10 @@ "_PACKAGE" ## usethis namespace: start +#' @importFrom grDevices colorRampPalette #' @importFrom magrittr %>% +#' @importFrom readr read_csv #' @importFrom stringr str_replace_all -#' @importFrom grDevices colorRampPalette ## usethis namespace: end #' @import ggplot2 #' @import hues diff --git a/R/data.R b/R/data.R new file mode 100644 index 0000000..5982116 --- /dev/null +++ b/R/data.R @@ -0,0 +1,8 @@ +#' varianceStabilizingTransformation Object from Test Data +#' +#' An example of the output of varianceStabilizingTransformation to +#' show different visualization code +#' +#' @format ## `bcbio_vsd_data` +#' VSD object from DESeq2 data +"bcbio_vsd_data" diff --git a/R/hello.R b/R/hello.R index 127ba0b..47e4f18 100644 --- a/R/hello.R +++ b/R/hello.R @@ -9,7 +9,7 @@ #' @param file path to CSV file for nf-core #' @examples #' -#' bcbio_nfcore_check(system.file("extdata", "rnaseq_good.csv", package = "bcbiR") ) +#' bcbio_nfcore_check(system.file("extdata", "rnaseq_good.csv", package = "bcbioR") ) #' #' @export bcbio_nfcore_check <- function(file){ diff --git a/data-raw/DATASET.R b/data-raw/DATASET.R new file mode 100644 index 0000000..c530f42 --- /dev/null +++ b/data-raw/DATASET.R @@ -0,0 +1,29 @@ +## code to prepare `DATASET` dataset goes here +library(DESeq2) +library(tidyverse) +coldata_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' +counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.tsv' +multiqc_data_dir = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/multiqc-report-data/' +se_object = NA +column = "sample_type" +numerator = "tumor" +denominator = "normal" +subset_column = NULL +subset_value = NULL + +coldata <- load_coldata(coldata_fn, column, + numerator, denominator, + subset_column, subset_value) +metrics <- load_metrics(se_object, multiqc_data_dir) %>% + left_join(coldata, by = c('sample' = 'description')) %>% + as.data.frame() +rownames(metrics) <- metrics$sample +counts <- load_counts(counts_fn) +counts <- counts[,colnames(counts) %in% coldata$description] +# if the names don't match in order or string check files names and coldata information +counts = counts[rownames(coldata)] +stopifnot(all(names(counts) == rownames(coldata))) + +dds_to_use <- DESeqDataSetFromMatrix(counts, coldata, design = ~sample_type) +bcbio_vsd_data <- vst(dds_to_use) +usethis::use_data(bcbio_vsd_data, overwrite = TRUE) diff --git a/data/bcbio_vsd_data.rda b/data/bcbio_vsd_data.rda new file mode 100644 index 0000000000000000000000000000000000000000..c1ccf33cf2a16ed47edac91d8b95aa44c071b6d8 GIT binary patch literal 1505335 zcmafZXIK+q(=G~vf}j*ds)`ipy$gsm=_tK}^cJKf0YZ`9K`GLa-iy=#A)wNGCyp!3nIAiI?L2!uyVuU%bI{s{Ry5_*H!VePpo!61A%FjWvazoa zMCe-TIyKIofYeS{dCF$CRAm^!pbWBQF3^vVrB#A?9oWu&e!~&`!PgQ6z$yXOl2C2l 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+library(DEGreport) +library(ggplot2) +library(ggforce) + +data("bcbio_vsd_data") + +colors=cb_friendly_cols(1:15) +ggplot2::theme_set(theme_prism(base_size = 14)) + +pca <- degPCA(assay(bcbio_vsd_data), colData(bcbio_vsd_data), + condition = "sample_type", name = "sample", data = T) + +pca$plot + ggtitle(paste0("All samples", "\nPCA using ", nrow(vst), " genes")) + + theme(plot.title=element_text(hjust=0.5)) + + geom_mark_ellipse(aes(color = sample_type)) +``` +```{r} +# NOTE:This is not confirmed to be a valid test but it could help to understand the data +library(vegan) +vare.disa <- vegdist(t(assay(bcbio_vsd_data))) + +mod = betadisper(vare.disa, colData(bcbio_vsd_data)[['sample_type']]) +anova(mod) +``` + + diff --git a/man/bcbio_nfcore_check.Rd b/man/bcbio_nfcore_check.Rd index 63348b3..76c954f 100644 --- a/man/bcbio_nfcore_check.Rd +++ b/man/bcbio_nfcore_check.Rd @@ -14,6 +14,6 @@ Function to check samplesheet for nf-core } \examples{ -bcbio_nfcore_check(system.file("extdata", "rnaseq_good.csv", package = "bcbiR") ) +bcbio_nfcore_check(system.file("extdata", "rnaseq_good.csv", package = "bcbioR") ) } diff --git a/man/bcbio_vsd_data.Rd b/man/bcbio_vsd_data.Rd new file mode 100644 index 0000000..9dbd982 --- /dev/null +++ b/man/bcbio_vsd_data.Rd @@ -0,0 +1,20 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{bcbio_vsd_data} +\alias{bcbio_vsd_data} +\title{varianceStabilizingTransformation Object from Test Data} +\format{ +\subsection{\code{bcbio_vsd_data}}{ + +VSD object from DESeq2 data +} +} +\usage{ +bcbio_vsd_data +} +\description{ +An example of the output of varianceStabilizingTransformation to +show different visualization code +} +\keyword{datasets} From cd23ad2301855914c38d13aeda018af466d4e3ba Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Tue, 14 May 2024 14:56:52 -0400 Subject: [PATCH 15/55] add teaseq QC Rmd and supporting files --- .../singlecell/skeleton/QC/QC-01-load_data.R | 49 ++ .../skeleton/QC/QC-02-run_analysis.R | 100 +++ .../singlecell/skeleton/QC/TEAseq.Rmd | 725 ++++++++++++++++++ .../skeleton/scripts/fix_filenames.R | 40 + .../skeleton/scripts/gtex_adt_hto.sbatch | 22 + .../skeleton/scripts/gtex_atac.sbatch | 24 + 6 files changed, 960 insertions(+) create mode 100644 inst/rmarkdown/templates/singlecell/skeleton/QC/QC-01-load_data.R create mode 100644 inst/rmarkdown/templates/singlecell/skeleton/QC/QC-02-run_analysis.R create mode 100644 inst/rmarkdown/templates/singlecell/skeleton/QC/TEAseq.Rmd create mode 100644 inst/rmarkdown/templates/singlecell/skeleton/scripts/fix_filenames.R create mode 100644 inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch create mode 100644 inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch diff --git a/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-01-load_data.R b/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-01-load_data.R new file mode 100644 index 0000000..2e79160 --- /dev/null +++ b/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-01-load_data.R @@ -0,0 +1,49 @@ +# prepare seurat object with all 3 modes + +# read cellranger-arc outs +#arc_h5 <- '../aspera_download_sep23/SN0288408/KW11249_atsibris/230628_10X_KW11252_bcl/cellranger-arc-2.0.0/GRCh38/BRI-2286/outs/filtered_feature_bc_matrix.h5' +arc_h5 <- '../final/batch3_atac/cellranger-arc_output/outs/filtered_feature_bc_matrix.h5' + +arc_data <- Read10X_h5(arc_h5) + +# read cellranger outs +#h5path <- '../aspera_download_sep23/SN0288408/KW11249_atsibris/230711_10X_KW11249_bcl/cellranger-7.1.0/GRCh38/BRI-2285_hashing/outs/filtered_feature_bc_matrix.h5' +h5path <- '../final/batch3_adt/AtheTeaSeqMulti/outs/multi/count/raw_feature_bc_matrix.h5' +counts <- Read10X_h5(h5path) + +# select cell barcodes detected by both ATAC and ADT +joint.bcs <- intersect(colnames(arc_data$Peaks), colnames(counts$`Antibody Capture`)) + +so <- CreateSeuratObject(counts = arc_data$`Gene Expression`[, joint.bcs]) + +# add cellranger ATAC +# fragpath <- '../aspera_download_sep23/SN0288408/KW11249_atsibris/230628_10X_KW11252_bcl/cellranger-arc-2.0.0/GRCh38/BRI-2286/outs/atac_fragments.tsv.gz' +fragpath <- '../final/batch3_atac/cellranger-arc_output/outs/atac_fragments.tsv.gz' +annotation <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86) +seqlevels(annotation) <- paste0('chr', seqlevels(annotation)) + +so[["CellRangerPeaks"]] <- CreateChromatinAssay( + counts = arc_data$Peaks[, joint.bcs], + sep = c(":", "-"), + fragments = fragpath, + annotation = annotation +) + +# add HTO +so[["HTO"]] <- CreateAssayObject(counts = counts$`Multiplexing Capture`[c('Hashtag1', 'Hashtag2','Hashtag3', 'Hashtag4', 'Hashtag5','Hashtag6','Hashtag7'), joint.bcs]) +# Normalize HTO data, here we use centered log-ratio (CLR) transformation +so <- NormalizeData(so, assay = "HTO", normalization.method = "CLR") +so <- HTODemux(so, assay = "HTO", positive.quantile = 0.99) + +# table(so@meta.data$orig.ident, so@meta.data$hash.ID) %>% as.matrix() %>% kable() %>% kable_styling() + +# add ADT data +so[["ADT"]] <- CreateAssayObject(counts = counts$`Antibody Capture`[, joint.bcs]) +so <- NormalizeData(so, assay = "ADT", normalization.method = "CLR", margin = 2) + +qsave(so, "data/tea_seurat.qs") + +hashtag_counts <- table(so@meta.data$hash.ID) %>% as.data.frame() %>% + set_colnames(c('Hashtag', 'Number of cells')) %>% dplyr::filter(grepl("Hash", Hashtag)) +write.csv(hashtag_counts, 'data/hashtag_counts.csv', row.names = FALSE) + diff --git a/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-02-run_analysis.R b/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-02-run_analysis.R new file mode 100644 index 0000000..997d00d --- /dev/null +++ b/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-02-run_analysis.R @@ -0,0 +1,100 @@ + +seurat <- qread("data/tea_seurat.qs") + +# keep singlet only +seurat <- subset(x = seurat, subset = HTO_classification.global == "Singlet") +# table(seurat@meta.data$hash.ID) + +# add sampleinfo to metadata +seurat@meta.data <- seurat@meta.data %>% + rownames_to_column("cellID") %>% + left_join(sampleinfo, by = "hash.ID") %>% + column_to_rownames("cellID") + +#Compute percent mito ratio +seurat$percent_mito <- PercentageFeatureSet(seurat, pattern = "^MT") +# Compute total reads +seurat$total_reads <- Matrix::colSums(GetAssayData(seurat, slot = "counts")) +# Compute novelty score +seurat$novelty <- log10(seurat@meta.data$nFeature_RNA)/log10(seurat@meta.data$nCount_RNA) + +seurat <- SCTransform(seurat, conserve.memory=TRUE) + +# cell cycle +cc_genes <- cc.genes.updated.2019 + +# Extract phase specific genes +s.genes <- cc_genes$s.genes +g2m.genes <- cc_genes$g2m.genes + +# Perform cell cycle scoring +seurat <- CellCycleScoring(object = seurat, + s.features = s.genes, + g2m.features = g2m.genes, + set.ident = TRUE) + +# clustering +seurat <- RunPCA(object = seurat, verbose = T) +pct <- seurat@reductions$pca@stdev/sum(seurat@reductions$pca@stdev) * 100 +co1 <- which(cumsum(pct) > 90 & pct < 5)[1] +co2 <- sort(which((pct[1:length(pct) - 1] - pct[2:length(pct)]) > 0.1), decreasing = T)[1] + 1 +# co <- min(co1, co2) +co <- 30 +seurat <- RunPCA(object = seurat, npcs = co, verbose = TRUE) +seurat <- FindNeighbors(object = seurat, reduction = "pca", dims = 1:co) +seurat <- RunUMAP(object = seurat, dims=1:co ) + +# clustering by ADT data # Error: didn't do CLR norm because copied the code from 1st dataset +seurat <- ScaleData(seurat, assay = "ADT") +seurat <- RunPCA(seurat, features = rownames(seurat@assays$ADT@counts), verbose = TRUE, assay= "ADT", reduction.name = "pcaADT", reduction.key = "pcaADT_") +seurat <- RunUMAP(seurat, dims = 1:20, reduction = "pcaADT", assay = "ADT", reduction.name = "umapADT", reduction.key = "umapADT_") + +# calling peaks using MACS2, following James's code +DefaultAssay(seurat)<-"CellRangerPeaks" +peaks <- CallPeaks(seurat) # took 16 min +peaks <- keepStandardChromosomes(peaks, pruning.mode = "coarse") +# peaks <- subsetByOverlaps(x = peaks, ranges = blacklist_hg38_unified, invert = TRUE) + + +# quantify counts in each peak +macs2_counts <- FeatureMatrix( + fragments = Fragments(seurat), + features = peaks, + cells = colnames(seurat) +) + +#qsave(seurat,"data/tmp.qs") +# ATAC data normalization, dimension reduction, and clustering +DefaultAssay(seurat)<-"MACS2peaks" +seurat <- RunTFIDF(seurat, assay = "MACS2peaks") +seurat <- FindTopFeatures(seurat, min.cutoff = 'q0', assay = "MACS2peaks") +seurat <- RunSVD(seurat, assay = "MACS2peaks") +seurat <- RunUMAP(object = seurat, dims = 2:30, reduction = 'lsi', assay = "MACS2peaks", reduction.name = "umapATAC", reduction.key = "umapATAC_" ) +seurat <- FindNeighbors(object = seurat, reduction = 'lsi', dims = 2:30) +seurat <- FindClusters(object = seurat, verbose = FALSE, algorithm = 3) + +seurat$blacklist_fraction <- FractionCountsInRegion( + object = seurat, + assay = 'MACS2peaks', + regions = blacklist_hg38_unified +) + +## add ATAC per barcode metrics +pbm <- read.csv( + file = "cellranger-arc_output/outs/per_barcode_metrics.csv", + header = TRUE, + row.names = 1 +) + +seurat@meta.data <- seurat@meta.data %>% + rownames_to_column("cellID") %>% + left_join(pbm %>% rownames_to_column('cellID'), by = "cellID") %>% + column_to_rownames("cellID") + +seurat$pct_reads_in_peaks <- seurat$atac_peak_region_fragments / seurat$atac_fragments * 100 + +# compute nucleosome signal score per cell +seurat <- NucleosomeSignal(object = seurat) # added "nucleosome_signal", "nucleosome_percentile" to metadata +# compute TSS enrichment score per cell +seurat <- TSSEnrichment(object = seurat, fast = FALSE) # added "TSS.enrichment", "TSS.percentile" to metadata +qsave(seurat, 'data/tea_seurat_unfiltered_clustered.qs') diff --git a/inst/rmarkdown/templates/singlecell/skeleton/QC/TEAseq.Rmd b/inst/rmarkdown/templates/singlecell/skeleton/QC/TEAseq.Rmd new file mode 100644 index 0000000..9f1a302 --- /dev/null +++ b/inst/rmarkdown/templates/singlecell/skeleton/QC/TEAseq.Rmd @@ -0,0 +1,725 @@ +--- +title: "Quality Control TEAseq" +title: "Differential Expression" +author: "Harvard Chan Bioinformatics Core" +date: "`r Sys.Date()`" +output: + html_document: + code_folding: hide + df_print: paged + highlights: pygments + number_sections: true + self_contained: true + theme: default + toc: true + toc_float: + collapsed: true + smooth_scroll: true +editor_options: + chunk_output_type: console +params: + maxGenes: !r Inf + minGenes: 750 + maxUMIs: !r Inf + minUMIs: 1000 + maxMitoPer: 20 + min_novelty: 0.85 + min_cells_per_gene: 10 + min_reads_per_cell: 1000 + max_nCount_ADT: !r Inf + min_nCount_ADT: 400 + max_nFeature_ADT: 135 + min_nFeature_ADT: 75 + max_PRF: !r Inf + min_PRF: 500 + max_FRiP: !r Inf + min_FRiP: 25 + min_TSS: 2 + max_NS: 2 + max_blacklistratio: 0.02 + data_dir: !r file.path("data") + results_dir: !r file.path("results") +output: + html_document: + code_folding: hide + df_print: paged + highlight: tango + number_sections: false + self_contained: true + theme: paper + toc: true + toc_float: + collapsed: true + smooth_scroll: false +--- + +```{r render} +# Set seed for reproducibility +set.seed(1454944673L) +library(knitr) +opts_chunk[["set"]]( + audodep = TRUE, + cache = FALSE, + cache.lazy = FALSE, + error = TRUE, + fig.height = 6L, + fig.width = 6L, + message = FALSE, + tidy = TRUE, + warning = FALSE +) +``` + +```{r setup, cache=FALSE, message=FALSE} +library(Seurat) +library(tidyverse) +library(ggplot2) +library(ggrepel) +library(cowplot) +library(Matrix) +library(data.table) +library(magrittr) +library(kableExtra) +library(Signac) +library(EnsDb.Hsapiens.v86) +library(qs) +library(bcbioR) +invisible(mapply( + FUN = dir.create, + path = c(params$data_dir, params$results_dir), + MoreArgs = list(showWarnings = FALSE, recursive = TRUE) +)) + +ggplot2::theme_set(theme_light(base_size = 14)) +``` + + +```{r functions, cache=FALSE, message=FALSE} +# Load functions +`%!in%` = Negate(`%in%`) +``` + +# Overview + +```{r} +# Run QC-01-load_data.R and then +# Run QC-02-run_analysis.R +stopifnot(file.exists('data/tea_seurat_unfiltered_clustered.qs')) +seurat <- qread('data/tea_seurat_unfiltered_clustered.qs') + +stopifnot(file.exists('data/hashtag_counts.csv')) +hashtag_counts <- read.csv('data/hashtag_counts.csv') +``` + +- Principal Investigator: +- Experiment: TEA-Seq of .... + +The downloaded data are cellranger-arc (for gene expression and ATAC-Seq) and cellranger (for gene expression and ADT) outputs. Alignment was done using reference genome GRCh38. We use the shared cell barcodes from both results to create a Seurat object that has all 3 modalities. + +First of all, we demultiplex the data so we know which cell belongs to which sample. Below shows the number of cells for each hashtag after demultiplexing. Doublets and Negatives are removed. Overall we have good number of cells per sample. + +```{r} +hashtag_counts <- hashtag_counts %>% plyr::rename(c('Hashtag'='hash.ID')) +# hashtag_counts %>% kable() %>% kable_styling() +``` + + +```{r} +# sample info +sampleinfo = c("Hashtag1: no drug (DMSO)=Control +Hashtag2: Romidepsin=RMD +Hashtag3: Bryostatin=BRY +Hashtag4: AZD=AZD +Hashtag5: RB=RMD+BRY +Hashtag6: RA=RMD+AZD +Hashtag7: PMAi=PMAi") + +sampleinfo <- as.data.frame(matrix(unlist(str_split(sampleinfo[1], '\n|: |=')), + ncol = 3, byrow = T)) %>% + setNames(c('hash.ID', 'Description', 'Sample')) + +sampleinfo %>% full_join(hashtag_counts, by = 'hash.ID') %>% kable() %>% kable_styling() +``` + +Then we normalize and cluster the data for each modality separately for QC purpose. + +# RNA {.tabset} + +Let's first check the gene expression data. + +## Number of cells per sample + + +```{r} +# Visualize the number of counts per sample +seurat@meta.data %>% + ggplot() + + geom_bar(aes(x=sample)) + + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1), + plot.title = element_text(hjust=0.5)) + + ggtitle("Number of Cells per Sample") +``` + +## Reads per sample + + +```{r warning=F, message=F} +seurat@meta.data %>% + group_by(sample) %>% + summarise(Reads = sum(nCount_RNA)) %>% + ggplot(aes(x=sample, y=Reads)) + + geom_bar(stat = "identity") + + theme(axis.text.x =element_text(angle = 90,vjust = 1, hjust=1)) + + ggtitle("Number of Reads per Sample") +``` + +## UMI counts per cell {.tabset} + +Now let's assess the distribution of unique molecular identifier (UMI)-deconvoluted counts per cell. The UMI counts per cell should be generally above 500. The cutoff is at `r params$minUMIs`. + +```{r warning=FALSE, message=FALSE, fig.height=8, fig.width=12, results='asis'} +cat('### Histogram \n\n') +seurat@meta.data %>% + ggplot(aes(x = nCount_RNA)) + + geom_histogram(binwidth = 50) + + xlab("nUMI") + + facet_wrap(~sample) + + geom_vline(xintercept = params$minUMIs) + +cat('\n### Violin plot \n\n') +VlnPlot(seurat, features="nCount_RNA", pt.size = 0) + + scale_fill_cb_friendly() + + geom_hline(yintercept = params$minUMIs) +``` + +## Genes detected per cell {.tabset} + +Here by "detected", we mean genes with a non-zero count measurement per cell. Seeing gene detection in the range of `500`-`5000` is normal for most single-cell experiments. +The cutoff is at `r params$minGenes`. + +```{r warning=FALSE, message=FALSE, fig.height=8, fig.width=12, results='asis'} +cat('### Histogram \n\n') +seurat@meta.data %>% + ggplot(aes(x = nFeature_RNA)) + + geom_histogram(binwidth = 50) + + xlab("nGene") + + facet_wrap(~sample) + + geom_vline(xintercept = params$minGenes) + +cat('\n### Violin plot \n\n') +VlnPlot(seurat, features="nFeature_RNA", pt.size = 0) + + scale_fill_cb_friendly() + + geom_hline(yintercept = params$minGenes) +``` + + +## Mitochondrial abundance {.tabset} + +We evaluate overall mitochondrial gene expression as a biomarker of cellular stress during sample preparation. The mitochondrial percentages are mostly below 20%. + + +```{r warning=F, message=F, fig.height=8, fig.width=12, results='asis'} +cat('### Histogram \n\n') +seurat@meta.data %>% + ggplot(aes(x = percent_mito)) + + geom_histogram() + + facet_wrap(~sample) + + geom_vline(xintercept = params$maxMitoPer) + +cat('\n### Violin plot \n\n') +VlnPlot(seurat, features="percent_mito", pt.size = 0) + + scale_fill_cb_friendly() + + geom_hline(yintercept = params$maxMitoPer) +``` + +## Novelty {.tabset} + +Another way to QC the data is to look for less novelty, that is cells that have less genes detected per count than other cells. We can see the samples where we sequenced each cell less have a higher overall novelty, that is because we have not started saturated the sequencing for any given gene for these samples. Outlier cells in these samples might be cells that we have a less complex RNA species than other cells. Sometimes we can detect contamination with low complexity cell types like red blood cells via this metric. + +All cells have high novelty scores (> 0.85). + +```{r novelty, fig.height=8, fig.width=12, results='asis'} +cat('### Histogram \n\n') +# Visualize the overall novelty of the gene expression by visualizing the genes detected per UMI +seurat@meta.data %>% + ggplot(aes(x=novelty)) + + geom_histogram(bins=50) + + facet_wrap(~sample) + +cat('\n### Violin plot \n\n') +VlnPlot(seurat, features="novelty", pt.size = 0) + + scale_fill_cb_friendly() +``` + +## UMIs vs. genes detected {.tabset} + +If we graph out the total number of UMI counts per cell vs. the genes detected per cell, we can assess whether there is a large population of low quality cells with low counts and/or gene detection. For some of the samples there is a cluster of cells that have increasing UMIs yet gene detection is low. We will want to filter out these low complexity cells. The data points are colored by different metrics to see if there is some correlation. + +### Data points colored by Percent mitochondrial genes + +```{r umis_vs_genes_mito, warning=F, message=F, fig.height=8, fig.width=12} +# Visualize the correlation between genes detected and number of UMIs and determine whether strong presence of cells with low numbers of genes/UMIs +seurat@meta.data %>% + ggplot(aes(x=nCount_RNA, y=nFeature_RNA, color=percent_mito)) + + geom_point() + + stat_smooth(method=lm) + + scale_x_log10() + + scale_y_log10() + + geom_vline(xintercept = params$minUMIs) + + geom_hline(yintercept = params$minGenes) + + xlab("nUMI") + ylab("nGene") + + facet_wrap(~sample) +``` + +### Data points colored by Novelty + +```{r umis_vs_genes_novelty, warning=F, message=F, fig.height=8, fig.width=12} +# Visualize the correlation between genes detected and number of UMIs and determine whether strong presence of cells with low numbers of genes/UMIs +seurat@meta.data %>% + ggplot(aes(x=nCount_RNA, y=nFeature_RNA, color=novelty)) + + geom_point() + + stat_smooth(method=lm) + + scale_x_log10() + + scale_y_log10() + + geom_vline(xintercept = params$minUMIs) + + geom_hline(yintercept = params$minGenes) + + xlab("nUMI") + ylab("nGene") + + facet_wrap(~sample) +``` + + +## UMAP plots {.tabset} + +### Color by sample + +Clustering of the cells is similar to the first dataset. + +```{r results='asis'} +DimPlot(object = seurat, reduction = "umap", group.by = "sample", order = T) + + scale_color_cb_friendly() + +cat('\n\n') +``` + +```{r results='asis', fig.width=12} +DimPlot(object = seurat, reduction = "umap", split.by = 'sample')+ + scale_color_cb_friendly() +cat('\n\n') +``` + +### Color by cell cycle + +```{r results='asis'} +DimPlot(object = seurat, reduction = "umap", group.by = "Phase", order = T) + + scale_color_cb_friendly() +cat('\n\n') +``` + +```{r results='asis', fig.width=12} +DimPlot(object = seurat, reduction = "umap", group.by = "Phase", split.by = 'sample') + + scale_color_cb_friendly() +cat('\n\n') +``` + +### Color by UMI counts + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umap", features = "nCount_RNA", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n\n') +``` +### Color by the number of detected genes + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umap", features = "nFeature_RNA", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n\n') +``` +### Color by mito percentage + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umap", features = "percent_mito", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n') +``` +### Color by novelty + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umap", features = "novelty", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n') +``` + +# ADT {.tabset} + +The numbers of detected proteins are more than the the first experiment. The cells are clustered slightly differently from gene expression and ATAC-Seq results on the UMAP and the first dataset. + +## UMI counts per cell + +```{r warning=FALSE, message=FALSE, results='asis'} +VlnPlot(seurat, features = "nCount_ADT", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") +cat('\n\n') +``` + +**Zoom in** (cutoff line is at `r params$min_nCount_ADT`): + +```{r warning=FALSE, message=FALSE, results='asis'} +VlnPlot(seurat, features = "nCount_ADT", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") + + ylim(0,3000) + + geom_hline(yintercept = params$min_nCount_ADT) +cat('\n\n') +``` + +## Detected proteins per cell + +Lines are drawn at `r params$min_nFeature_ADT` and `r params$max_nFeature_ADT`. + +```{r warning=FALSE, message=FALSE, results='asis'} +VlnPlot(seurat, features = "nFeature_ADT", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly()+ + xlab("") + + geom_hline(yintercept = params$max_nFeature_ADT) + + geom_hline(yintercept = params$min_nFeature_ADT) + + ylab("Number of detected proteins") +cat('\n\n') +``` + +## UMAP plots {.tabset} + +### Color by sample + +We can still see cells are clustered by sample, but not as clear as the first dataset. + +```{r results='asis'} +DimPlot(object = seurat, reduction = "umapADT", group.by = "sample", order = T) + + scale_color_cb_friendly() +cat('\n\n') +``` +```{r results='asis', fig.width=12} +DimPlot(object = seurat, reduction = "umapADT", split.by = 'sample') + + scale_color_cb_friendly() +cat('\n\n') +``` + + +### Color by UMI counts + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umapADT", features = "nCount_ADT", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n\n') +``` +### Color by the number of detected proteins + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umapADT", features = "nFeature_ADT", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n\n') +``` + + + +# ATAC-Seq {.tabset} + +We use some of the results from cellranger outputs and the peaks called using MACS2 for QCing the scATAC-Seq data. + +## UMI counts per cell + +```{r warning=FALSE, message=FALSE, results='asis'} +VlnPlot(seurat, features = "nCount_MACS2peaks", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") + +cat('\n\n') +``` +## Detected peaks per cell + +```{r warning=FALSE, message=FALSE} +VlnPlot(seurat, features = "nFeature_MACS2peaks", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") + +VlnPlot(seurat, features = "nFeature_CellRangerPeaks", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") +``` + +## QC metrics {.tabset} + +### Total number of fragments in peaks {.tabset} + +This metric represents the total number of fragments (= reads) mapping within a region of the genome that is predicted to be accessible (= a peak). It's a measure of cellular sequencing depth / complexity. Cells with very few reads may need to be excluded due to low sequencing depth. Cells with extremely high levels may represent doublets, nuclei clumps, or other artefacts. + + +```{r results='asis', warning=FALSE, message=FALSE} +DefaultAssay(seurat) <- "MACS2peaks" + +cat('#### Histogram \n\n') +seurat@meta.data %>% + ggplot(aes(x = atac_peak_region_fragments)) + + geom_histogram() + + facet_wrap(~sample) + + geom_vline(xintercept = params$min_PRF) + + +cat('\n#### Violin plot\n\n') +VlnPlot( + object = seurat, + features = 'atac_peak_region_fragments', + pt.size = 0 +) + ylab('Total number of fragments in peaks') + + NoLegend() + + geom_hline(yintercept = params$min_PRF) + xlab("") + + scale_fill_cb_friendly() + +cat('\n\n') +``` + +### Fraction of fragments in peaks + +It represents the fraction of all fragments that fall within ATAC-seq peaks. Cells with low values (i.e. <15-20%) often represent low-quality cells or technical artifacts that should be removed. Note that this value can be sensitive to the set of peaks used. + +```{r results='asis'} +VlnPlot( + object = seurat, + features = 'pct_reads_in_peaks', + pt.size = 0 +) + NoLegend() + xlab("") + + scale_fill_cb_friendly() + + geom_hline(yintercept = params$min_FRiP) + +cat('\n\n') +``` + + +### Transcriptional start site (TSS) enrichment score {.tabset} + +The ENCODE project has defined an ATAC-seq targeting score based on the ratio of fragments centered at the TSS to fragments in TSS-flanking regions (see https://www.encodeproject.org/data-standards/terms/). Poor ATAC-seq experiments typically will have a low TSS enrichment score. We can compute this metric for each cell with the TSSEnrichment() function, and the results are stored in metadata under the column name TSS.enrichment. + + +```{r results='asis'} +VlnPlot( + object = seurat, + features = 'TSS.enrichment', + pt.size = 0 +) + scale_fill_cb_friendly() + +NoLegend() + xlab("") +cat('\n\n') +``` +The following tabs show the TSS enrichment score distribution for each sample. Cells with high-quality ATAC-seq data should show a clear peak in reads at the TSS, with a decay the further we get from it. + +Each plot is split between cells with a high or low global TSS enrichment score (cuffoff at `r params$min_TSS`), to double-check whether cells with lowest enrichment scores still follow the expected pattern or rather need to be excluded. + +```{r results='asis'} +seurat$TSS.group <- ifelse(seurat$TSS.enrichment > params$min_TSS, "High", "Low") + +for (sample in sort(unique(seurat$sample))){ + cat('####', sample, '\n\n') + p <- TSSPlot(subset(x = seurat, idents = sample), group.by = "TSS.group") + NoLegend() + print(p) + cat('\n\n') +} +``` + +### Nucleosome signal + +The histogram of DNA fragment sizes (determined from the paired-end sequencing reads) should exhibit a strong nucleosome banding pattern corresponding to the length of DNA wrapped around a single nucleosome, i.e peaks at approximately 100bp (nucleosome-free), and mono, di and tri nucleosome-bound peaks at 200, 400 and 600bp. We calculate this per single cell, and quantify the approximate ratio of mononucleosomal to nucleosome-free fragments (stored as nucleosome_signal). Cells with lower nucleosome signal have a higher ratio of nucleosome-free fragments. + +```{r warning = FALSE} +seurat$nucleosome_group <- ifelse(seurat$nucleosome_signal > 1, "high NS", "low NS") +FragmentHistogram(seurat, group.by = 'nucleosome_group', cells = colnames(seurat[, seurat$sample == 'Control'])) +``` + +```{r } +VlnPlot( + object = seurat, + features = 'nucleosome_signal', + pt.size = 0 +) + scale_fill_cb_friendly() + +NoLegend() + xlab("") +``` + + + + +### Blacklist ratio + +tIt's he ratio of reads in genomic blacklist regions. The [ENCODE project](https://www.encodeproject.org/) has provided a list of [blacklist regions](https://github.com/Boyle-Lab/Blacklist), representing reads which are often associated with artefactual signal. Cells with a high proportion of reads mapping to these areas (compared to reads mapping to peaks) often represent technical artifacts and should be removed. ENCODE blacklist regions for human (hg19 and GRCh38), mouse (mm10), Drosophila (dm3), and C. elegans (ce10) are included in the Signac package. **Peaks overlapping with the balcklist regions were removed in the analysis, so we don't show blacklist fraction here**. + +Line is drawn at `r params$min_blacklistratio`. + +```{r} +VlnPlot( + object = seurat, + features = 'blacklist_fraction', + pt.size = 0 +) + scale_fill_cb_friendly() + + NoLegend() + + geom_hline(yintercept = params$min_blacklistratio) + +``` + + +## Normalization, linear dimensional reduction, and clustering + +* Normalization: Signac performs term frequency-inverse document frequency (TF-IDF) normalization. This is a two-step normalization procedure, that both normalizes across cells to correct for differences in cellular sequencing depth, and across peaks to give higher values to more rare peaks. + +* Feature selection: The low dynamic range of scATAC-seq data makes it challenging to perform variable feature selection, as we do for scRNA-seq. Instead, we can choose to use only the top n% of features (peaks) for dimensional reduction, or remove features present in less than n cells with the FindTopFeatures() function. Here, we will use all features, though we note that we see very similar results when using only a subset of features (try setting min.cutoff to ‘q75’ to use the top 25% all peaks), with faster runtimes. Features used for dimensional reduction are automatically set as VariableFeatures() for the Seurat object by this function. + +* Dimension reduction: We next run singular value decomposition (SVD) on the TD-IDF matrix, using the features (peaks) selected above. This returns a reduced dimension representation of the object (for users who are more familiar with scRNA-seq, you can think of this as analogous to the output of PCA). + +The combined steps of TF-IDF followed by SVD are known as latent semantic indexing (LSI), and were first introduced for the analysis of scATAC-seq data by [Cusanovich et al. 2015.](https://www.science.org/doi/10.1126/science.aax6234) + +The first LSI component often captures sequencing depth (techni ccal variation) rather than biological variation. If this is the case, the component should be removed from downstream analysis. We can assess the correlation between each LSI component and sequencing depth using the DepthCor() function (see below). +Here we see there is a very strong correlation between the first LSI component and the total number of counts for the cell, so we will perform downstream steps without this component. + +```{r results='asis'} +DepthCor(seurat, assay = 'MACS2peaks') +cat('\n\n') +``` + +## UMAP plots + +```{r results='asis'} +DimPlot(object = seurat, group.by = 'sample', reduction = "umapATAC") + + scale_color_cb_friendly() + +cat('\n\n') +``` + + + + +# Conclusion + +We will apply the following filters to **keep the high-quality cells**. For most of the criteria below, we don't have an upper limit, as we have removed doublets after demultiplexing. + +**RNA** + +* Number of UMIs (nCount_RNA) > `r params$minUMIs` +* Number of detected genes (nFeature_RNA) > `r params$minGenes` +* mitochondrial percentages (nFeature_RNA) < `r params$maxMitoPer` + +We don't apply any filter for complexity, as all the cells have relatively high complexity (> 0.85). + +**ADT** + +* Number of UMIs (nCount_ADT) > `r params$min_nCount_ADT` +* Number of detected proteins (nFeature_ADT) < `r params$max_nFeature_ADT` and > `r params$min_nFeature_ADT` + +**ATAC** + +* Total number of fragments in peaks (atac_peak_region_fragments) > `r params$min_PRF` +* Percentage of fragments in peaks (pct_reads_in_peaks) > `r params$min_FRiP` +* TSS enrichment (TSS.enrichment) > `r params$min_TSS` +* Nucleosome signal (nucleosome_signal) < `r params$max_NS` +* Blacklisted regions ratio (blacklist_fraction) < `r params$max_blacklistratio` + + +Number of cells before filtering: + +```{r} +table(seurat$sample) %>% as.data.frame() %>% set_colnames(c('sample', '#Cells')) %>% kable() %>% kable_styling() +``` + + +```{r} +# Please select your cutoffs +rna_keep <- seurat@meta.data[(seurat@meta.data$nCount_RNA > params$minUMIs & + seurat@meta.data$nFeature_RNA > params$minGenes & + seurat@meta.data$percent_mito < params$maxMitoPer), ] +rna_filt <- seurat@meta.data[rownames(seurat@meta.data) %!in% rownames(rna_keep),] + +adt_keep <- seurat@meta.data[(seurat@meta.data$nCount_ADT > params$min_nCount_ADT & + seurat@meta.data$nFeature_ADT > params$min_nFeature_ADT & + seurat@meta.data$nFeature_ADT < params$max_nFeature_ADT), ] + +adt_filt <- seurat@meta.data[rownames(seurat@meta.data) %!in% rownames(adt_keep),] + +atac_keep <- seurat@meta.data[(seurat@meta.data$atac_peak_region_fragments > params$min_PRF & + seurat@meta.data$pct_reads_in_peaks > params$min_FRiP & + seurat@meta.data$TSS.enrichment > params$min_TSS & + seurat@meta.data$nucleosome_signal < params$max_NS & + seurat@meta.data$blacklist_fraction < params$max_blacklistratio), ] + +atac_filt <- seurat@meta.data[rownames(seurat@meta.data) %!in% rownames(atac_keep),] + +library(ggvenn) +x = list( + RNA = rownames(rna_filt), + ADT = rownames(adt_filt), + ATAC = rownames(atac_filt) +) +ggvenn(x, names(x)) +``` + +```{r eval=FALSE} +# this is only run once before rendering +unfiltered_seurat <- seurat +seurat <- subset( + x = seurat, + subset = nCount_RNA > params$minUMIs & + nFeature_RNA > params$minGenes & + percent_mito < params$maxMitoPer & + nCount_ADT > params$min_nCount_ADT & + nFeature_ADT > params$min_nFeature_ADT & + nFeature_ADT < params$max_nFeature_ADT & + atac_peak_region_fragments > params$min_PRF & + TSS.enrichment > params$min_TSS & + nucleosome_signal < params$max_NS & + blacklist_fraction < params$max_blacklistratio + ) + +# reclustering +# RNA +DefaultAssay(seurat) <- 'RNA' +seurat <- SCTransform(seurat, conserve.memory=TRUE) +seurat <- RunPCA(object = seurat, verbose = T) +co <- 40 +seurat <- FindNeighbors(object = seurat, reduction = "pca", dims = 1:co) +seurat <- RunUMAP(object = seurat, dims=1:co ) + +# clustering by ADT data +seurat <- NormalizeData(seurat, assay = "ADT", normalization.method = "CLR", margin = 2) +seurat <- ScaleData(seurat, assay = "ADT") +seurat <- RunPCA(seurat, features = rownames(seurat@assays$ADT@counts), verbose = TRUE, assay= "ADT", reduction.name = "pcaADT", reduction.key = "pcaADT_") +seurat <- RunUMAP(seurat, dims = 1:20, reduction = "pcaADT", assay = "ADT", reduction.name = "umapADT", reduction.key = "umapADT_") + +# ATAC data normalization, dimension reduction, and clustering +seurat <- RunTFIDF(seurat, assay = "MACS2peaks") +seurat <- FindTopFeatures(seurat, min.cutoff = 'q0', assay = "MACS2peaks") +seurat <- RunSVD(seurat, assay = "MACS2peaks") +seurat <- RunUMAP(object = seurat, dims = 2:30, reduction = 'lsi', assay = "MACS2peaks", reduction.name = "umapATAC", reduction.key = "umapATAC_" ) + +qsave(seurat, 'data/tea_seurat_filtered_clustered.qs') +``` + +Number of cells **after filtering**: + +```{r} +# get the filtered seurat +stopifnot(file.exists('data/tea_seurat_filtered_clustered.qs')) +seurat <- qread('data/tea_seurat_filtered_clustered.qs') +table(seurat$sample) %>% as.data.frame() %>% set_colnames(c('sample', '#Cells')) %>% kable() %>% kable_styling() +``` + +**We take a look at the UMAPs after filtering out low quality cells for each modality.** + +```{r eval=FALSE, fig.width=12, fig.height=12} +plot_grid( + DimPlot(object = seurat, group.by = "sample", reduction = "umap") + ggtitle('RNA'), + DimPlot(object = seurat, group.by = "sample", reduction = "umapADT") + ggtitle('ADT'), + DimPlot(object = seurat, group.by = "sample", reduction = "umapATAC") + ggtitle('ATAC') +) +``` + + +# Session info + +```{r} +devtools::session_info() +``` + diff --git a/inst/rmarkdown/templates/singlecell/skeleton/scripts/fix_filenames.R b/inst/rmarkdown/templates/singlecell/skeleton/scripts/fix_filenames.R new file mode 100644 index 0000000..5ef12c2 --- /dev/null +++ b/inst/rmarkdown/templates/singlecell/skeleton/scripts/fix_filenames.R @@ -0,0 +1,40 @@ +library(stringr) +fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq/", pattern = "_S[1-9]") + + +dir.create("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq_fixed") +sapply(fsq, function(fn){ + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("20240417_GEX",ending) + fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq",fn)), + new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq_fixed",fnnew))) +}) + + +fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq", pattern = "_S[1-9]") +dir.create("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq_fixed") +sapply(fsq, function(fn){ + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("20240417_ATAC",ending) + fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq",fn)), + new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq_fixed",fnnew))) +}) + + +fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq/", pattern = "_S[1-9]") +dir.create("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq_fixed") +sapply(fsq, function(fn){ + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("20240417_ADT",ending) + fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq",fn)), + new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq_fixed",fnnew))) +}) + +fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq/", pattern = "_S[1-9]") +dir.create("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq_fixed") +sapply(fsq, function(fn){ + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("20240417_HTO",ending) + fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq",fn)), + new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq_fixed",fnnew))) +}) diff --git a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch new file mode 100644 index 0000000..e08a060 --- /dev/null +++ b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch @@ -0,0 +1,22 @@ +#!/bin/bash + +#SBATCH --job-name=CellrangerMulti # Job name +#SBATCH --partition=priority # Partition name +#SBATCH --time=1-23:59 # Runtime in D-HH:MM format +#SBATCH --nodes=1 # Number of nodes (keep at 1) +#SBATCH --ntasks=1 # Number of tasks per node (keep at 1) +#SBATCH --cpus-per-task=16 # CPU cores requested per task (change for threaded jobs) +#SBATCH --mem=128G # Memory needed per node (total) +#SBATCH --error=jobid_%j.err # File to which STDERR will be written, including job ID +#SBATCH --output=jobid_%j.out # File to which STDOUT will be written, including job ID +#SBATCH --mail-type=ALL # Type of email notification (BEGIN, END, FAIL, ALL) + +module load cellranger/7.0.0 + +localcores=$SLURM_CPUS_PER_TASK +localmem=$SLURM_MEM_PER_NODE + +cellranger multi --id=AtheTeaSeqMulti --csv=/n/data1/cores/bcbio/PIs/athe_tsibris/10x_TEA-Seq_Tsibris_human_HIV_ex-vivoCD4_hbc04730_ZZ/meta/multiconfig_batch3.csv + + + diff --git a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch new file mode 100644 index 0000000..b260015 --- /dev/null +++ b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch @@ -0,0 +1,24 @@ +#!/bin/bash + +#SBATCH --job-name=Cellranger-Arc # Job name +#SBATCH --partition=priority # Partition name +#SBATCH --time=1-23:59 # Runtime in D-HH:MM format +#SBATCH --nodes=1 # Number of nodes (keep at 1) +#SBATCH --ntasks=1 # Number of tasks per node (keep at 1) +#SBATCH --cpus-per-task=16 # CPU cores requested per task (change for threaded jobs) +#SBATCH --mem=128G # Memory needed per node (total) +#SBATCH --error=jobid_%j.err # File to which STDERR will be written, including job ID +#SBATCH --output=jobid_%j.out # File to which STDOUT will be written, including job ID +#SBATCH --mail-type=ALL # Type of email notification (BEGIN, END, FAIL, ALL) + +module load cellranger-ARC/2.0.0 + +localcores=$SLURM_CPUS_PER_TASK +localmem=$SLURM_MEM_PER_NODE + +cellranger-arc count --id=cellranger-arc_output \ + --reference=/n/shared_db/GRCh38/uk/cellranger-ARC/2.0.0/2.0.0/refdata-cellranger-arc-GRCh38-2020-A-2.0.0 \ + --libraries=/n/data1/cores/bcbio/PIs/athe_tsibris/10x_TEA-Seq_Tsibris_human_HIV_ex-vivoCD4_hbc04730_ZZ/meta/libraries_3rdbatch.csv \ + --localcores=16 \ + --localmem=128 + \ No newline at end of file From e5b69b123428cd45f63cfb9da978a12ff3016728 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Tue, 14 May 2024 16:32:37 -0400 Subject: [PATCH 16/55] add TEAseq templates --- .../meta/TEAseq/feature_reference.csv | 172 ++++++++++++++++++ .../skeleton/meta/TEAseq/libraries.csv | 4 + .../skeleton/meta/TEAseq/multiconfig.csv | 22 +++ .../skeleton/scripts/gtex_adt_hto.sbatch | 3 +- .../skeleton/scripts/gtex_atac.sbatch | 4 +- 5 files changed, 202 insertions(+), 3 deletions(-) create mode 100644 inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/feature_reference.csv create mode 100644 inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/libraries.csv create mode 100644 inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/multiconfig.csv diff --git a/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/feature_reference.csv b/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/feature_reference.csv new file mode 100644 index 0000000..974c4ec --- /dev/null +++ b/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/feature_reference.csv @@ -0,0 +1,172 @@ +id,name,read,pattern,sequence,feature_type +Hashtag1,Hashtag1,R2,5P(BC),GTCAACTCTTTAGCG,Multiplexing Capture +Hashtag2,Hashtag2,R2,5P(BC),TGATGGCCTATTGGG,Multiplexing Capture +Hashtag3,Hashtag3,R2,5P(BC),TTCCGCCTCTCTTTG,Multiplexing Capture +Hashtag4,Hashtag4,R2,5P(BC),AGTAAGTTCAGCGTA,Multiplexing Capture +Hashtag5,Hashtag5,R2,5P(BC),AAGTATCGTTTCGCA,Multiplexing Capture +Hashtag6,Hashtag6,R2,5P(BC),GGTTGCCAGATGTCA,Multiplexing Capture +Hashtag7,Hashtag7,R2,5P(BC),TGTCTTTCCTGCCAG,Multiplexing Capture +CD86_prot,CD86_prot,R2,5P(BC),GTCTTTGTCAGTGCA,Antibody Capture +CD274_or_B7-H1_PD-L1_prot,CD274_or_B7-H1_PD-L1_prot,R2,5P(BC),GTTGTCCGACAATAC,Antibody Capture +CD270_or_HVEM_TR2_prot,CD270_or_HVEM_TR2_prot,R2,5P(BC),TGATAGAAACAGACC,Antibody Capture +CD155_or_PVR_prot,CD155_or_PVR_prot,R2,5P(BC),ATCACATCGTTGCCA,Antibody Capture +CD112_or_Nectin-2_prot,CD112_or_Nectin-2_prot,R2,5P(BC),AACCTTCCGTCTAAG,Antibody Capture +CD47_prot,CD47_prot,R2,5P(BC),GCATTCTGTCACCTA,Antibody Capture +CD48_prot,CD48_prot,R2,5P(BC),CTACGACGTAGAAGA,Antibody Capture +CD40_prot,CD40_prot,R2,5P(BC),CTCAGATGGAGTATG,Antibody Capture +CD154_prot,CD154_prot,R2,5P(BC),GCTAGATAGATGCAA,Antibody Capture +CD52_prot,CD52_prot,R2,5P(BC),CTTTGTACGAGCAAA,Antibody Capture +CD3_prot,CD3_prot,R2,5P(BC),CTCATTGTAACTCCT,Antibody Capture +CD8_prot,CD8_prot,R2,5P(BC),GCGCAACTTGATGAT,Antibody Capture +CD56_or_NCAM_prot,CD56_or_NCAM_prot,R2,5P(BC),TCCTTTCCTGATAGG,Antibody Capture +CD19_prot,CD19_prot,R2,5P(BC),CTGGGCAATTACTCG,Antibody Capture +CD33_prot,CD33_prot,R2,5P(BC),TAACTCAGGGCCTAT,Antibody Capture +CD11c_prot,CD11c_prot,R2,5P(BC),TACGCCTATAACTTG,Antibody Capture +HLA-A_B_C_prot,HLA-A_B_C_prot,R2,5P(BC),TATGCGAGGCTTATC,Antibody Capture +CD45RA_prot,CD45RA_prot,R2,5P(BC),TCAATCCTTCCGCTT,Antibody Capture +CD123_prot,CD123_prot,R2,5P(BC),CTTCACTCTGTCAGG,Antibody Capture +CD7_prot,CD7_prot,R2,5P(BC),TGGATTCCCGGACTT,Antibody Capture +CD49f_prot,CD49f_prot,R2,5P(BC),TTCCGAGGATGATCT,Antibody Capture +CD194_or_CCR4_prot,CD194_or_CCR4_prot,R2,5P(BC),AGCTTACCTGCACGA,Antibody Capture +CD4_prot,CD4_prot,R2,5P(BC),TGTTCCCGCTCAACT,Antibody Capture +CD44_prot,CD44_prot,R2,5P(BC),TGGCTTCAGGTCCTA,Antibody Capture +CD14_prot,CD14_prot,R2,5P(BC),TCTCAGACCTCCGTA,Antibody Capture +CD16_prot,CD16_prot,R2,5P(BC),AAGTTCACTCTTTGC,Antibody Capture +CD25_prot,CD25_prot,R2,5P(BC),TTTGTCCTGTACGCC,Antibody Capture +CD45RO_prot,CD45RO_prot,R2,5P(BC),CTCCGAATCATGTTG,Antibody Capture +CD279_or_PD-1_prot,CD279_or_PD-1_prot,R2,5P(BC),ACAGCGCCGTATTTA,Antibody Capture +TIGIT_or_VSTM3_prot,TIGIT_or_VSTM3_prot,R2,5P(BC),TTGCTTACCGCCAGA,Antibody Capture +MouseIgG1_kisotypeCtrl_prot,MouseIgG1_kisotypeCtrl_prot,R2,5P(BC),GCCGGACGACATTAA,Antibody Capture +MouseIgG2a_kisotypeCtrl_prot,MouseIgG2a_kisotypeCtrl_prot,R2,5P(BC),CTCCTACCTAAACTG,Antibody Capture +MouseIgG2b_kisotypeCtrl_prot,MouseIgG2b_kisotypeCtrl_prot,R2,5P(BC),ATATGTATCACGCGA,Antibody Capture +RatIgG2b_kIsotypeCtrl_prot,RatIgG2b_kIsotypeCtrl_prot,R2,5P(BC),GATTCTTGACGACCT,Antibody Capture +CD20_prot,CD20_prot,R2,5P(BC),TTCTGGGTCCCTAGA,Antibody Capture +CD335_or_NKp46_prot,CD335_or_NKp46_prot,R2,5P(BC),ACAATTTGAACAGCG,Antibody Capture +CD31_prot,CD31_prot,R2,5P(BC),ACCTTTATGCCACGG,Antibody Capture +anti-HumanPodoplanin_prot,anti-HumanPodoplanin_prot,R2,5P(BC),GGTTACTCGTTGTGT,Antibody Capture +CD146_prot,CD146_prot,R2,5P(BC),CCTTGGATAACATCA,Antibody Capture +IgM_prot,IgM_prot,R2,5P(BC),TAGCGAGCCCGTATA,Antibody Capture +CD5_prot,CD5_prot,R2,5P(BC),CATTAACGGGATGCC,Antibody Capture +CD183_or_CXCR3_prot,CD183_or_CXCR3_prot,R2,5P(BC),GCGATGGTAGATTAT,Antibody Capture +CD195_or_CCR5_prot,CD195_or_CCR5_prot,R2,5P(BC),CCAAAGTAAGAGCCA,Antibody Capture +CD32_prot,CD32_prot,R2,5P(BC),GCTTCCGAATTACCG,Antibody Capture +CD196_or_CCR6_prot,CD196_or_CCR6_prot,R2,5P(BC),GATCCCTTTGTCACT,Antibody Capture +CD185_or_CXCR5_prot,CD185_or_CXCR5_prot,R2,5P(BC),AATTCAACCGTCGCC,Antibody Capture +HuCD103_or_IntegrinaE_prot,HuCD103_or_IntegrinaE_prot,R2,5P(BC),GACCTCATTGTGAAT,Antibody Capture +CD69_prot,CD69_prot,R2,5P(BC),GTCTCTTGGCTTAAA,Antibody Capture +CD62L_prot,CD62L_prot,R2,5P(BC),GTCCCTGCAACTTGA,Antibody Capture +CD161_prot,CD161_prot,R2,5P(BC),GTACGCAGTCCTTCT,Antibody Capture +CD152_or_CTLA-4_prot,CD152_or_CTLA-4_prot,R2,5P(BC),ATGGTTCACGTAATC,Antibody Capture +CD223_or_LAG-3_prot,CD223_or_LAG-3_prot,R2,5P(BC),CATTTGTCTGCCGGT,Antibody Capture +KLRG1_or_MAFA_prot,KLRG1_or_MAFA_prot,R2,5P(BC),CTTATTTCCTGCCCT,Antibody Capture +CD27_prot,CD27_prot,R2,5P(BC),GCACTCCTGCATGTA,Antibody Capture +CD107a_or_LAMP-1_prot,CD107a_or_LAMP-1_prot,R2,5P(BC),CAGCCCACTGCAATA,Antibody Capture +CD95_or_Fas_prot,CD95_or_Fas_prot,R2,5P(BC),CCAGCTCATTAGAGC,Antibody Capture +CD134_or_OX40_prot,CD134_or_OX40_prot,R2,5P(BC),AACCCACCGTTGTTA,Antibody Capture +HLA-DR_prot,HLA-DR_prot,R2,5P(BC),AATAGCGAGCAAGTA,Antibody Capture +CD1c_prot,CD1c_prot,R2,5P(BC),GAGCTACTTCACTCG,Antibody Capture +CD11b_prot,CD11b_prot,R2,5P(BC),GACAAGTGATCTGCA,Antibody Capture +CD64_prot,CD64_prot,R2,5P(BC),AAGTATGCCCTACGA,Antibody Capture +CD141_or_Thrombomodulin_prot,CD141_or_Thrombomodulin_prot,R2,5P(BC),GGATAACCGCGCTTT,Antibody Capture +HuCD314_or_NKG2D_prot,HuCD314_or_NKG2D_prot,R2,5P(BC),CGTGTTTGTTCCTCA,Antibody Capture +CD35_prot,CD35_prot,R2,5P(BC),ACTTCCGTCGATCTT,Antibody Capture +CD57Recombinant_prot,CD57Recombinant_prot,R2,5P(BC),AACTCCCTATGGAGG,Antibody Capture +CD272_or_BTLA_prot,CD272_or_BTLA_prot,R2,5P(BC),GTTATTGGACTAAGG,Antibody Capture +CD278_or_ICOS_prot,CD278_or_ICOS_prot,R2,5P(BC),CGCGCACCCATTAAA,Antibody Capture +CD275_or_B7-H2_B7-RP1_ICOSL_prot,CD275_or_B7-H2_B7-RP1_ICOSL_prot,R2,5P(BC),GTTAGTGTTAGCTTG,Antibody Capture +CD58_or_LFA-3_prot,CD58_or_LFA-3_prot,R2,5P(BC),GTTCCTATGGACGAC,Antibody Capture +CD39_prot,CD39_prot,R2,5P(BC),TTACCTGGTATCCGT,Antibody Capture +CX3CR1_prot,CX3CR1_prot,R2,5P(BC),AGTATCGTCTCTGGG,Antibody Capture +CD24_prot,CD24_prot,R2,5P(BC),AGATTCCTTCGTGTT,Antibody Capture +CD21_prot,CD21_prot,R2,5P(BC),AACCTAGTAGTTCGG,Antibody Capture +CD11a_prot,CD11a_prot,R2,5P(BC),TATATCCTTGTGAGC,Antibody Capture +CD79b_or_Igb_prot,CD79b_or_Igb_prot,R2,5P(BC),ATTCTTCAACCGAAG,Antibody Capture +CD244_or_2B4_prot,CD244_or_2B4_prot,R2,5P(BC),TCGCTTGGATGGTAG,Antibody Capture +CD169_or_Sialoadhesin_Siglec-1_prot,CD169_or_Sialoadhesin_Siglec-1_prot,R2,5P(BC),TACTCAGCGTGTTTG,Antibody Capture +integrinb7_prot,integrinb7_prot,R2,5P(BC),TCCTTGGATGTACCG,Antibody Capture +CD268_or_BAFF-R_prot,CD268_or_BAFF-R_prot,R2,5P(BC),CGAAGTCGATCCGTA,Antibody Capture +CD42b_prot,CD42b_prot,R2,5P(BC),TCCTAGTACCGAAGT,Antibody Capture +CD54_prot,CD54_prot,R2,5P(BC),CTGATAGACTTGAGT,Antibody Capture +CD62P_or_P-Selectin_prot,CD62P_or_P-Selectin_prot,R2,5P(BC),CCTTCCGTATCCCTT,Antibody Capture +CD119_or_IFN-gammaRachain_prot,CD119_or_IFN-gammaRachain_prot,R2,5P(BC),TGTGTATTCCCTTGT,Antibody Capture +TCRa_or_b_prot,TCRa_or_b_prot,R2,5P(BC),CGTAACGTAGAGCGA,Antibody Capture +RatIgG1_kisotypeCtrl_prot,RatIgG1_kisotypeCtrl_prot,R2,5P(BC),ATCAGATGCCCTCAT,Antibody Capture +RatIgG1_lambdaIsotypeCtrl_prot,RatIgG1_lambdaIsotypeCtrl_prot,R2,5P(BC),GGGAGCGATTCAACT,Antibody Capture +RatIgG2a_kIsotypeCtrl_prot,RatIgG2a_kIsotypeCtrl_prot,R2,5P(BC),AAGTCAGGTTCGTTT,Antibody Capture +RatIgG2c_kIsotypeCtrl_prot,RatIgG2c_kIsotypeCtrl_prot,R2,5P(BC),TCCAGGCTAGTCATT,Antibody Capture +ArmenianHamsterIgGIsotypeCtrl_prot,ArmenianHamsterIgGIsotypeCtrl_prot,R2,5P(BC),CCTGTCATTAAGACT,Antibody Capture +CD192_or_CCR2_prot,CD192_or_CCR2_prot,R2,5P(BC),GAGTTCCCTTACCTG,Antibody Capture +CD122_or_IL-2Rb_prot,CD122_or_IL-2Rb_prot,R2,5P(BC),TCATTTCCTCCGATT,Antibody Capture +CD267_or_TACI_prot,CD267_or_TACI_prot,R2,5P(BC),AGTGATGGAGCGAAC,Antibody Capture +FceRIa_prot,FceRIa_prot,R2,5P(BC),CTCGTTTCCGTATCG,Antibody Capture +CD41_prot,CD41_prot,R2,5P(BC),ACGTTGTGGCCTTGT,Antibody Capture +CD137_or_4-1BB_prot,CD137_or_4-1BB_prot,R2,5P(BC),CAGTAAGTTCGGGAC,Antibody Capture +CD43_prot,CD43_prot,R2,5P(BC),GATTAACCAGCTCAT,Antibody Capture +CD163_prot,CD163_prot,R2,5P(BC),GCTTCTCCTTCCTTA,Antibody Capture +CD83_prot,CD83_prot,R2,5P(BC),CCACTCATTTCCGGT,Antibody Capture +CD13_prot,CD13_prot,R2,5P(BC),TTTCAACGCCCTTTC,Antibody Capture +CD2_prot,CD2_prot,R2,5P(BC),TACGATTTGTCAGGG,Antibody Capture +CD226_or_DNAM-1_prot,CD226_or_DNAM-1_prot,R2,5P(BC),TCTCAGTGTTTGTGG,Antibody Capture +CD29_prot,CD29_prot,R2,5P(BC),GTATTCCCTCAGTCA,Antibody Capture +CD303_or_BDCA-2_prot,CD303_or_BDCA-2_prot,R2,5P(BC),GAGATGTCCGAATTT,Antibody Capture +CD49b_prot,CD49b_prot,R2,5P(BC),GCTTTCTTCAGTATG,Antibody Capture +CD61_prot,CD61_prot,R2,5P(BC),AGGTTGGAGTAGACT,Antibody Capture +CD81_or_TAPA-1_prot,CD81_or_TAPA-1_prot,R2,5P(BC),GTATCCTTCCTTGGC,Antibody Capture +CD55_prot,CD55_prot,R2,5P(BC),GCTCATTACCCATTA,Antibody Capture +IgD_prot,IgD_prot,R2,5P(BC),CAGTCTCCGTAGAGT,Antibody Capture +CD18_prot,CD18_prot,R2,5P(BC),TATTGGGACACTTCT,Antibody Capture +CD28_prot,CD28_prot,R2,5P(BC),TGAGAACGACCCTAA,Antibody Capture +CD38_prot,CD38_prot,R2,5P(BC),TGTACCCGCTTGTGA,Antibody Capture +CD127_or_IL-7Ra_prot,CD127_or_IL-7Ra_prot,R2,5P(BC),GTGTGTTGTCCTATG,Antibody Capture +CD45_prot,CD45_prot,R2,5P(BC),TGCAATTACCCGGAT,Antibody Capture +CD22_prot,CD22_prot,R2,5P(BC),GGGTTGTTGTCTTTG,Antibody Capture +CD71_prot,CD71_prot,R2,5P(BC),CCGTGTTCCTCATTA,Antibody Capture +CD26_prot,CD26_prot,R2,5P(BC),GGTGGCTAGATAATG,Antibody Capture +CD115_or_CSF-1R_prot,CD115_or_CSF-1R_prot,R2,5P(BC),AATCACGGTCCTTGT,Antibody Capture +CD63_prot,CD63_prot,R2,5P(BC),GAGATGTCTGCAACT,Antibody Capture +CD304_or_Neuropilin-1_prot,CD304_or_Neuropilin-1_prot,R2,5P(BC),GGACTAAGTTTCGTT,Antibody Capture +CD36_prot,CD36_prot,R2,5P(BC),TTCTTTGCCTTGCCA,Antibody Capture +CD172a_or_SIRPa_prot,CD172a_or_SIRPa_prot,R2,5P(BC),CGTGTTTAACTTGAG,Antibody Capture +CD72_prot,CD72_prot,R2,5P(BC),CAGTCGTGGTAGATA,Antibody Capture +CD158_or_KIR2DL1_or_S1_or_S3_or_S5_prot,CD158_or_KIR2DL1_or_S1_or_S3_or_S5_prot,R2,5P(BC),TATCAACCAACGCTT,Antibody Capture +CD93_prot,CD93_prot,R2,5P(BC),GCGCTACTTCCTTGA,Antibody Capture +CD200_or_OX2_prot,CD200_or_OX2_prot,R2,5P(BC),CACGTAGACCTTTGC,Antibody Capture +CD49a_prot,CD49a_prot,R2,5P(BC),ACTGATGGACTCAGA,Antibody Capture +CD49d_prot,CD49d_prot,R2,5P(BC),CCATTCAACTTCCGG,Antibody Capture +CD73_or_Ecto-5-nucleotidase_prot,CD73_or_Ecto-5-nucleotidase_prot,R2,5P(BC),CAGTTCCTCAGTTCG,Antibody Capture +CD9_prot,CD9_prot,R2,5P(BC),GAGTCACCAATCTGC,Antibody Capture +TCRVa7.2_prot,TCRVa7.2_prot,R2,5P(BC),TACGAGCAGTATTCA,Antibody Capture +TCRVdelta2_prot,TCRVdelta2_prot,R2,5P(BC),TCAGTCAGATGGTAT,Antibody Capture +CD354_or_TREM-1_prot,CD354_or_TREM-1_prot,R2,5P(BC),TAGCCGTTTCCTTTG,Antibody Capture +CD305_or_LAIR1_prot,CD305_or_LAIR1_prot,R2,5P(BC),ATTTCCATTCCCTGT,Antibody Capture +LOX-1_prot,LOX-1_prot,R2,5P(BC),ACCCTTTACCGAATA,Antibody Capture +CD158e1_or_KIR3DL1_NKB1_prot,CD158e1_or_KIR3DL1_NKB1_prot,R2,5P(BC),GGACGCTTTCCTTGA,Antibody Capture +CD109_prot,CD109_prot,R2,5P(BC),CACTTAACTCTGGGT,Antibody Capture +CD142_prot,CD142_prot,R2,5P(BC),CACTGCCGTCGATTA,Antibody Capture +CD319_or_CRACC_prot,CD319_or_CRACC_prot,R2,5P(BC),AGTATGCCATGTCTT,Antibody Capture +CD99_prot,CD99_prot,R2,5P(BC),ACCCGTCCCTAAGAA,Antibody Capture +CLEC12A_prot,CLEC12A_prot,R2,5P(BC),CATTAGAGTCTGCCA,Antibody Capture +CD151_or_PETA-3_prot,CD151_or_PETA-3_prot,R2,5P(BC),CTTACCTAGTCATTC,Antibody Capture +CD352_or_NTB-A_prot,CD352_or_NTB-A_prot,R2,5P(BC),AGTTTCCACTCAGGC,Antibody Capture +CLEC1B_or_CLEC2_prot,CLEC1B_or_CLEC2_prot,R2,5P(BC),TGCCAGTATCACGTA,Antibody Capture +CD94_prot,CD94_prot,R2,5P(BC),CTTTCCGGTCCTACA,Antibody Capture +IgE_prot,IgE_prot,R2,5P(BC),GGATGTACCGCGTAT,Antibody Capture +CD150_or_SLAM_prot,CD150_or_SLAM_prot,R2,5P(BC),GTCATTGTATGTCTG,Antibody Capture +CD162_prot,CD162_prot,R2,5P(BC),ATATGTCAGAGCACC,Antibody Capture +CD84_prot,CD84_prot,R2,5P(BC),CTCCCTAGTTCCTTT,Antibody Capture +Iglightchaink_prot,Iglightchaink_prot,R2,5P(BC),AGCTCAGCCAGTATG,Antibody Capture +CD85j_or_ILT2_prot,CD85j_or_ILT2_prot,R2,5P(BC),CCTTGTGAGGCTATG,Antibody Capture +CD23_prot,CD23_prot,R2,5P(BC),TCTGTATAACCGTCT,Antibody Capture +Iglightchainlambda_prot,Iglightchainlambda_prot,R2,5P(BC),CAGCCAGTAAGTCAC,Antibody Capture +CD328_or_Siglec-7_prot,CD328_or_Siglec-7_prot,R2,5P(BC),CTTAGCATTTCACTG,Antibody Capture +GPR56_prot,GPR56_prot,R2,5P(BC),GCCTAGTTTCCGTTT,Antibody Capture +CD82_prot,CD82_prot,R2,5P(BC),TCCCACTTCCGCTTT,Antibody Capture +NKp80_prot,NKp80_prot,R2,5P(BC),TATAGTTCCTCTGTG,Antibody Capture +CD131_prot,CD131_prot,R2,5P(BC),CTGCATGAGACCAAA,Antibody Capture +CD74_prot,CD74_prot,R2,5P(BC),CTGTAGCATTTCCCT,Antibody Capture +CD116_prot,CD116_prot,R2,5P(BC),ATGGACAGTTCGTGT,Antibody Capture +CD37_prot,CD37_prot,R2,5P(BC),ACAGTCACTGGGCAA,Antibody Capture +CD101_or_BB27_prot,CD101_or_BB27_prot,R2,5P(BC),CTACTTCCCTGTCAA,Antibody Capture +HLA-DR_DP_DQ_prot,HLA-DR_DP_DQ_prot,R2,5P(BC),AGCTACGAGCAGTAG,Antibody Capture +CD88_or_C5aR_prot,CD88_or_C5aR_prot,R2,5P(BC),GCCGCATGAGAAACA,Antibody Capture +CD197_or_CCR7_prot,CD197_or_CCR7_prot,R2,5P(BC),AGTTCAGTCAACCGA,Antibody Capture \ No newline at end of file diff --git a/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/libraries.csv b/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/libraries.csv new file mode 100644 index 0000000..c373175 --- /dev/null +++ b/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/libraries.csv @@ -0,0 +1,4 @@ +fastqs,sample,library_type +FASTQ_PATH_TO_GEX,Gene Expression +FASTQ_PATH_TO_ATAC,Chromatin Accessibility + diff --git a/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/multiconfig.csv b/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/multiconfig.csv new file mode 100644 index 0000000..a742df0 --- /dev/null +++ b/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/multiconfig.csv @@ -0,0 +1,22 @@ +[gene-expression] +reference, /n/shared_db/GRCh38/uk/cellranger/7.0.0/7.0.0/refdata-gex-GRCh38-2020-A +chemistry,ARC-v1 + +[libraries] +fastq_id,fastqs,feature_types +20240417_GEX,FASTQ_PATH_TO_GEX,Gene Expression +20240417_ADT,FASTQ_PATH_TO_ADT,Antibody Capture +20240417_HTO,FASTQ_PATH_TO_HTO,Multiplexing Capture + +[feature] +reference,PATH_TO_feature_reference.csv + +[samples] +sample_id,cmo_ids +no_drug,Hashtag1 +Romidepsin,Hashtag2 +Bryostatin,Hashtag3 +AZD,Hashtag4 +RB,Hashtag5 +RA,Hashtag6 +PMAi,Hashtag7 diff --git a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch index e08a060..55d25bd 100644 --- a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch +++ b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch @@ -16,7 +16,8 @@ module load cellranger/7.0.0 localcores=$SLURM_CPUS_PER_TASK localmem=$SLURM_MEM_PER_NODE -cellranger multi --id=AtheTeaSeqMulti --csv=/n/data1/cores/bcbio/PIs/athe_tsibris/10x_TEA-Seq_Tsibris_human_HIV_ex-vivoCD4_hbc04730_ZZ/meta/multiconfig_batch3.csv +#change CSV to full path +cellranger multi --id=AtheTeaSeqMulti --csv=meta/multiconfig_batch3.csv diff --git a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch index b260015..2af343c 100644 --- a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch +++ b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch @@ -16,9 +16,9 @@ module load cellranger-ARC/2.0.0 localcores=$SLURM_CPUS_PER_TASK localmem=$SLURM_MEM_PER_NODE +# Change libraries to full path cellranger-arc count --id=cellranger-arc_output \ --reference=/n/shared_db/GRCh38/uk/cellranger-ARC/2.0.0/2.0.0/refdata-cellranger-arc-GRCh38-2020-A-2.0.0 \ - --libraries=/n/data1/cores/bcbio/PIs/athe_tsibris/10x_TEA-Seq_Tsibris_human_HIV_ex-vivoCD4_hbc04730_ZZ/meta/libraries_3rdbatch.csv \ + --libraries=meta/libraries.csv \ --localcores=16 \ --localmem=128 - \ No newline at end of file From 7ebb8c73be244b55fb2245c4deddcb5ffd21a9cf Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Tue, 14 May 2024 16:34:43 -0400 Subject: [PATCH 17/55] reorg folders --- .../teaseq/skeleton/QC/QC-01-load_data.R | 49 ++ .../teaseq/skeleton/QC/QC-02-run_analysis.R | 100 +++ .../templates/teaseq/skeleton/QC/QC.Rmd | 725 ++++++++++++++++++ .../templates/teaseq/skeleton/README.md | 17 + .../templates/teaseq/skeleton/information.R | 8 + .../teaseq/skeleton/scripts/fix_filenames.R | 40 + .../skeleton/scripts/gtex_adt_hto.sbatch | 23 + .../teaseq/skeleton/scripts/gtex_atac.sbatch | 24 + .../templates/teaseq/skeleton/skeleton.Rmd | 25 + inst/rmarkdown/templates/teaseq/template.yml | 3 + 10 files changed, 1014 insertions(+) create mode 100644 inst/rmarkdown/templates/teaseq/skeleton/QC/QC-01-load_data.R create mode 100644 inst/rmarkdown/templates/teaseq/skeleton/QC/QC-02-run_analysis.R create mode 100644 inst/rmarkdown/templates/teaseq/skeleton/QC/QC.Rmd create mode 100644 inst/rmarkdown/templates/teaseq/skeleton/README.md create mode 100644 inst/rmarkdown/templates/teaseq/skeleton/information.R create mode 100644 inst/rmarkdown/templates/teaseq/skeleton/scripts/fix_filenames.R create mode 100644 inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_adt_hto.sbatch create mode 100644 inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_atac.sbatch create mode 100644 inst/rmarkdown/templates/teaseq/skeleton/skeleton.Rmd create mode 100644 inst/rmarkdown/templates/teaseq/template.yml diff --git a/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-01-load_data.R b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-01-load_data.R new file mode 100644 index 0000000..2e79160 --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-01-load_data.R @@ -0,0 +1,49 @@ +# prepare seurat object with all 3 modes + +# read cellranger-arc outs +#arc_h5 <- '../aspera_download_sep23/SN0288408/KW11249_atsibris/230628_10X_KW11252_bcl/cellranger-arc-2.0.0/GRCh38/BRI-2286/outs/filtered_feature_bc_matrix.h5' +arc_h5 <- '../final/batch3_atac/cellranger-arc_output/outs/filtered_feature_bc_matrix.h5' + +arc_data <- Read10X_h5(arc_h5) + +# read cellranger outs +#h5path <- '../aspera_download_sep23/SN0288408/KW11249_atsibris/230711_10X_KW11249_bcl/cellranger-7.1.0/GRCh38/BRI-2285_hashing/outs/filtered_feature_bc_matrix.h5' +h5path <- '../final/batch3_adt/AtheTeaSeqMulti/outs/multi/count/raw_feature_bc_matrix.h5' +counts <- Read10X_h5(h5path) + +# select cell barcodes detected by both ATAC and ADT +joint.bcs <- intersect(colnames(arc_data$Peaks), colnames(counts$`Antibody Capture`)) + +so <- CreateSeuratObject(counts = arc_data$`Gene Expression`[, joint.bcs]) + +# add cellranger ATAC +# fragpath <- '../aspera_download_sep23/SN0288408/KW11249_atsibris/230628_10X_KW11252_bcl/cellranger-arc-2.0.0/GRCh38/BRI-2286/outs/atac_fragments.tsv.gz' +fragpath <- '../final/batch3_atac/cellranger-arc_output/outs/atac_fragments.tsv.gz' +annotation <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86) +seqlevels(annotation) <- paste0('chr', seqlevels(annotation)) + +so[["CellRangerPeaks"]] <- CreateChromatinAssay( + counts = arc_data$Peaks[, joint.bcs], + sep = c(":", "-"), + fragments = fragpath, + annotation = annotation +) + +# add HTO +so[["HTO"]] <- CreateAssayObject(counts = counts$`Multiplexing Capture`[c('Hashtag1', 'Hashtag2','Hashtag3', 'Hashtag4', 'Hashtag5','Hashtag6','Hashtag7'), joint.bcs]) +# Normalize HTO data, here we use centered log-ratio (CLR) transformation +so <- NormalizeData(so, assay = "HTO", normalization.method = "CLR") +so <- HTODemux(so, assay = "HTO", positive.quantile = 0.99) + +# table(so@meta.data$orig.ident, so@meta.data$hash.ID) %>% as.matrix() %>% kable() %>% kable_styling() + +# add ADT data +so[["ADT"]] <- CreateAssayObject(counts = counts$`Antibody Capture`[, joint.bcs]) +so <- NormalizeData(so, assay = "ADT", normalization.method = "CLR", margin = 2) + +qsave(so, "data/tea_seurat.qs") + +hashtag_counts <- table(so@meta.data$hash.ID) %>% as.data.frame() %>% + set_colnames(c('Hashtag', 'Number of cells')) %>% dplyr::filter(grepl("Hash", Hashtag)) +write.csv(hashtag_counts, 'data/hashtag_counts.csv', row.names = FALSE) + diff --git a/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-02-run_analysis.R b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-02-run_analysis.R new file mode 100644 index 0000000..997d00d --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-02-run_analysis.R @@ -0,0 +1,100 @@ + +seurat <- qread("data/tea_seurat.qs") + +# keep singlet only +seurat <- subset(x = seurat, subset = HTO_classification.global == "Singlet") +# table(seurat@meta.data$hash.ID) + +# add sampleinfo to metadata +seurat@meta.data <- seurat@meta.data %>% + rownames_to_column("cellID") %>% + left_join(sampleinfo, by = "hash.ID") %>% + column_to_rownames("cellID") + +#Compute percent mito ratio +seurat$percent_mito <- PercentageFeatureSet(seurat, pattern = "^MT") +# Compute total reads +seurat$total_reads <- Matrix::colSums(GetAssayData(seurat, slot = "counts")) +# Compute novelty score +seurat$novelty <- log10(seurat@meta.data$nFeature_RNA)/log10(seurat@meta.data$nCount_RNA) + +seurat <- SCTransform(seurat, conserve.memory=TRUE) + +# cell cycle +cc_genes <- cc.genes.updated.2019 + +# Extract phase specific genes +s.genes <- cc_genes$s.genes +g2m.genes <- cc_genes$g2m.genes + +# Perform cell cycle scoring +seurat <- CellCycleScoring(object = seurat, + s.features = s.genes, + g2m.features = g2m.genes, + set.ident = TRUE) + +# clustering +seurat <- RunPCA(object = seurat, verbose = T) +pct <- seurat@reductions$pca@stdev/sum(seurat@reductions$pca@stdev) * 100 +co1 <- which(cumsum(pct) > 90 & pct < 5)[1] +co2 <- sort(which((pct[1:length(pct) - 1] - pct[2:length(pct)]) > 0.1), decreasing = T)[1] + 1 +# co <- min(co1, co2) +co <- 30 +seurat <- RunPCA(object = seurat, npcs = co, verbose = TRUE) +seurat <- FindNeighbors(object = seurat, reduction = "pca", dims = 1:co) +seurat <- RunUMAP(object = seurat, dims=1:co ) + +# clustering by ADT data # Error: didn't do CLR norm because copied the code from 1st dataset +seurat <- ScaleData(seurat, assay = "ADT") +seurat <- RunPCA(seurat, features = rownames(seurat@assays$ADT@counts), verbose = TRUE, assay= "ADT", reduction.name = "pcaADT", reduction.key = "pcaADT_") +seurat <- RunUMAP(seurat, dims = 1:20, reduction = "pcaADT", assay = "ADT", reduction.name = "umapADT", reduction.key = "umapADT_") + +# calling peaks using MACS2, following James's code +DefaultAssay(seurat)<-"CellRangerPeaks" +peaks <- CallPeaks(seurat) # took 16 min +peaks <- keepStandardChromosomes(peaks, pruning.mode = "coarse") +# peaks <- subsetByOverlaps(x = peaks, ranges = blacklist_hg38_unified, invert = TRUE) + + +# quantify counts in each peak +macs2_counts <- FeatureMatrix( + fragments = Fragments(seurat), + features = peaks, + cells = colnames(seurat) +) + +#qsave(seurat,"data/tmp.qs") +# ATAC data normalization, dimension reduction, and clustering +DefaultAssay(seurat)<-"MACS2peaks" +seurat <- RunTFIDF(seurat, assay = "MACS2peaks") +seurat <- FindTopFeatures(seurat, min.cutoff = 'q0', assay = "MACS2peaks") +seurat <- RunSVD(seurat, assay = "MACS2peaks") +seurat <- RunUMAP(object = seurat, dims = 2:30, reduction = 'lsi', assay = "MACS2peaks", reduction.name = "umapATAC", reduction.key = "umapATAC_" ) +seurat <- FindNeighbors(object = seurat, reduction = 'lsi', dims = 2:30) +seurat <- FindClusters(object = seurat, verbose = FALSE, algorithm = 3) + +seurat$blacklist_fraction <- FractionCountsInRegion( + object = seurat, + assay = 'MACS2peaks', + regions = blacklist_hg38_unified +) + +## add ATAC per barcode metrics +pbm <- read.csv( + file = "cellranger-arc_output/outs/per_barcode_metrics.csv", + header = TRUE, + row.names = 1 +) + +seurat@meta.data <- seurat@meta.data %>% + rownames_to_column("cellID") %>% + left_join(pbm %>% rownames_to_column('cellID'), by = "cellID") %>% + column_to_rownames("cellID") + +seurat$pct_reads_in_peaks <- seurat$atac_peak_region_fragments / seurat$atac_fragments * 100 + +# compute nucleosome signal score per cell +seurat <- NucleosomeSignal(object = seurat) # added "nucleosome_signal", "nucleosome_percentile" to metadata +# compute TSS enrichment score per cell +seurat <- TSSEnrichment(object = seurat, fast = FALSE) # added "TSS.enrichment", "TSS.percentile" to metadata +qsave(seurat, 'data/tea_seurat_unfiltered_clustered.qs') diff --git a/inst/rmarkdown/templates/teaseq/skeleton/QC/QC.Rmd b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC.Rmd new file mode 100644 index 0000000..9f1a302 --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC.Rmd @@ -0,0 +1,725 @@ +--- +title: "Quality Control TEAseq" +title: "Differential Expression" +author: "Harvard Chan Bioinformatics Core" +date: "`r Sys.Date()`" +output: + html_document: + code_folding: hide + df_print: paged + highlights: pygments + number_sections: true + self_contained: true + theme: default + toc: true + toc_float: + collapsed: true + smooth_scroll: true +editor_options: + chunk_output_type: console +params: + maxGenes: !r Inf + minGenes: 750 + maxUMIs: !r Inf + minUMIs: 1000 + maxMitoPer: 20 + min_novelty: 0.85 + min_cells_per_gene: 10 + min_reads_per_cell: 1000 + max_nCount_ADT: !r Inf + min_nCount_ADT: 400 + max_nFeature_ADT: 135 + min_nFeature_ADT: 75 + max_PRF: !r Inf + min_PRF: 500 + max_FRiP: !r Inf + min_FRiP: 25 + min_TSS: 2 + max_NS: 2 + max_blacklistratio: 0.02 + data_dir: !r file.path("data") + results_dir: !r file.path("results") +output: + html_document: + code_folding: hide + df_print: paged + highlight: tango + number_sections: false + self_contained: true + theme: paper + toc: true + toc_float: + collapsed: true + smooth_scroll: false +--- + +```{r render} +# Set seed for reproducibility +set.seed(1454944673L) +library(knitr) +opts_chunk[["set"]]( + audodep = TRUE, + cache = FALSE, + cache.lazy = FALSE, + error = TRUE, + fig.height = 6L, + fig.width = 6L, + message = FALSE, + tidy = TRUE, + warning = FALSE +) +``` + +```{r setup, cache=FALSE, message=FALSE} +library(Seurat) +library(tidyverse) +library(ggplot2) +library(ggrepel) +library(cowplot) +library(Matrix) +library(data.table) +library(magrittr) +library(kableExtra) +library(Signac) +library(EnsDb.Hsapiens.v86) +library(qs) +library(bcbioR) +invisible(mapply( + FUN = dir.create, + path = c(params$data_dir, params$results_dir), + MoreArgs = list(showWarnings = FALSE, recursive = TRUE) +)) + +ggplot2::theme_set(theme_light(base_size = 14)) +``` + + +```{r functions, cache=FALSE, message=FALSE} +# Load functions +`%!in%` = Negate(`%in%`) +``` + +# Overview + +```{r} +# Run QC-01-load_data.R and then +# Run QC-02-run_analysis.R +stopifnot(file.exists('data/tea_seurat_unfiltered_clustered.qs')) +seurat <- qread('data/tea_seurat_unfiltered_clustered.qs') + +stopifnot(file.exists('data/hashtag_counts.csv')) +hashtag_counts <- read.csv('data/hashtag_counts.csv') +``` + +- Principal Investigator: +- Experiment: TEA-Seq of .... + +The downloaded data are cellranger-arc (for gene expression and ATAC-Seq) and cellranger (for gene expression and ADT) outputs. Alignment was done using reference genome GRCh38. We use the shared cell barcodes from both results to create a Seurat object that has all 3 modalities. + +First of all, we demultiplex the data so we know which cell belongs to which sample. Below shows the number of cells for each hashtag after demultiplexing. Doublets and Negatives are removed. Overall we have good number of cells per sample. + +```{r} +hashtag_counts <- hashtag_counts %>% plyr::rename(c('Hashtag'='hash.ID')) +# hashtag_counts %>% kable() %>% kable_styling() +``` + + +```{r} +# sample info +sampleinfo = c("Hashtag1: no drug (DMSO)=Control +Hashtag2: Romidepsin=RMD +Hashtag3: Bryostatin=BRY +Hashtag4: AZD=AZD +Hashtag5: RB=RMD+BRY +Hashtag6: RA=RMD+AZD +Hashtag7: PMAi=PMAi") + +sampleinfo <- as.data.frame(matrix(unlist(str_split(sampleinfo[1], '\n|: |=')), + ncol = 3, byrow = T)) %>% + setNames(c('hash.ID', 'Description', 'Sample')) + +sampleinfo %>% full_join(hashtag_counts, by = 'hash.ID') %>% kable() %>% kable_styling() +``` + +Then we normalize and cluster the data for each modality separately for QC purpose. + +# RNA {.tabset} + +Let's first check the gene expression data. + +## Number of cells per sample + + +```{r} +# Visualize the number of counts per sample +seurat@meta.data %>% + ggplot() + + geom_bar(aes(x=sample)) + + theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1), + plot.title = element_text(hjust=0.5)) + + ggtitle("Number of Cells per Sample") +``` + +## Reads per sample + + +```{r warning=F, message=F} +seurat@meta.data %>% + group_by(sample) %>% + summarise(Reads = sum(nCount_RNA)) %>% + ggplot(aes(x=sample, y=Reads)) + + geom_bar(stat = "identity") + + theme(axis.text.x =element_text(angle = 90,vjust = 1, hjust=1)) + + ggtitle("Number of Reads per Sample") +``` + +## UMI counts per cell {.tabset} + +Now let's assess the distribution of unique molecular identifier (UMI)-deconvoluted counts per cell. The UMI counts per cell should be generally above 500. The cutoff is at `r params$minUMIs`. + +```{r warning=FALSE, message=FALSE, fig.height=8, fig.width=12, results='asis'} +cat('### Histogram \n\n') +seurat@meta.data %>% + ggplot(aes(x = nCount_RNA)) + + geom_histogram(binwidth = 50) + + xlab("nUMI") + + facet_wrap(~sample) + + geom_vline(xintercept = params$minUMIs) + +cat('\n### Violin plot \n\n') +VlnPlot(seurat, features="nCount_RNA", pt.size = 0) + + scale_fill_cb_friendly() + + geom_hline(yintercept = params$minUMIs) +``` + +## Genes detected per cell {.tabset} + +Here by "detected", we mean genes with a non-zero count measurement per cell. Seeing gene detection in the range of `500`-`5000` is normal for most single-cell experiments. +The cutoff is at `r params$minGenes`. + +```{r warning=FALSE, message=FALSE, fig.height=8, fig.width=12, results='asis'} +cat('### Histogram \n\n') +seurat@meta.data %>% + ggplot(aes(x = nFeature_RNA)) + + geom_histogram(binwidth = 50) + + xlab("nGene") + + facet_wrap(~sample) + + geom_vline(xintercept = params$minGenes) + +cat('\n### Violin plot \n\n') +VlnPlot(seurat, features="nFeature_RNA", pt.size = 0) + + scale_fill_cb_friendly() + + geom_hline(yintercept = params$minGenes) +``` + + +## Mitochondrial abundance {.tabset} + +We evaluate overall mitochondrial gene expression as a biomarker of cellular stress during sample preparation. The mitochondrial percentages are mostly below 20%. + + +```{r warning=F, message=F, fig.height=8, fig.width=12, results='asis'} +cat('### Histogram \n\n') +seurat@meta.data %>% + ggplot(aes(x = percent_mito)) + + geom_histogram() + + facet_wrap(~sample) + + geom_vline(xintercept = params$maxMitoPer) + +cat('\n### Violin plot \n\n') +VlnPlot(seurat, features="percent_mito", pt.size = 0) + + scale_fill_cb_friendly() + + geom_hline(yintercept = params$maxMitoPer) +``` + +## Novelty {.tabset} + +Another way to QC the data is to look for less novelty, that is cells that have less genes detected per count than other cells. We can see the samples where we sequenced each cell less have a higher overall novelty, that is because we have not started saturated the sequencing for any given gene for these samples. Outlier cells in these samples might be cells that we have a less complex RNA species than other cells. Sometimes we can detect contamination with low complexity cell types like red blood cells via this metric. + +All cells have high novelty scores (> 0.85). + +```{r novelty, fig.height=8, fig.width=12, results='asis'} +cat('### Histogram \n\n') +# Visualize the overall novelty of the gene expression by visualizing the genes detected per UMI +seurat@meta.data %>% + ggplot(aes(x=novelty)) + + geom_histogram(bins=50) + + facet_wrap(~sample) + +cat('\n### Violin plot \n\n') +VlnPlot(seurat, features="novelty", pt.size = 0) + + scale_fill_cb_friendly() +``` + +## UMIs vs. genes detected {.tabset} + +If we graph out the total number of UMI counts per cell vs. the genes detected per cell, we can assess whether there is a large population of low quality cells with low counts and/or gene detection. For some of the samples there is a cluster of cells that have increasing UMIs yet gene detection is low. We will want to filter out these low complexity cells. The data points are colored by different metrics to see if there is some correlation. + +### Data points colored by Percent mitochondrial genes + +```{r umis_vs_genes_mito, warning=F, message=F, fig.height=8, fig.width=12} +# Visualize the correlation between genes detected and number of UMIs and determine whether strong presence of cells with low numbers of genes/UMIs +seurat@meta.data %>% + ggplot(aes(x=nCount_RNA, y=nFeature_RNA, color=percent_mito)) + + geom_point() + + stat_smooth(method=lm) + + scale_x_log10() + + scale_y_log10() + + geom_vline(xintercept = params$minUMIs) + + geom_hline(yintercept = params$minGenes) + + xlab("nUMI") + ylab("nGene") + + facet_wrap(~sample) +``` + +### Data points colored by Novelty + +```{r umis_vs_genes_novelty, warning=F, message=F, fig.height=8, fig.width=12} +# Visualize the correlation between genes detected and number of UMIs and determine whether strong presence of cells with low numbers of genes/UMIs +seurat@meta.data %>% + ggplot(aes(x=nCount_RNA, y=nFeature_RNA, color=novelty)) + + geom_point() + + stat_smooth(method=lm) + + scale_x_log10() + + scale_y_log10() + + geom_vline(xintercept = params$minUMIs) + + geom_hline(yintercept = params$minGenes) + + xlab("nUMI") + ylab("nGene") + + facet_wrap(~sample) +``` + + +## UMAP plots {.tabset} + +### Color by sample + +Clustering of the cells is similar to the first dataset. + +```{r results='asis'} +DimPlot(object = seurat, reduction = "umap", group.by = "sample", order = T) + + scale_color_cb_friendly() + +cat('\n\n') +``` + +```{r results='asis', fig.width=12} +DimPlot(object = seurat, reduction = "umap", split.by = 'sample')+ + scale_color_cb_friendly() +cat('\n\n') +``` + +### Color by cell cycle + +```{r results='asis'} +DimPlot(object = seurat, reduction = "umap", group.by = "Phase", order = T) + + scale_color_cb_friendly() +cat('\n\n') +``` + +```{r results='asis', fig.width=12} +DimPlot(object = seurat, reduction = "umap", group.by = "Phase", split.by = 'sample') + + scale_color_cb_friendly() +cat('\n\n') +``` + +### Color by UMI counts + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umap", features = "nCount_RNA", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n\n') +``` +### Color by the number of detected genes + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umap", features = "nFeature_RNA", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n\n') +``` +### Color by mito percentage + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umap", features = "percent_mito", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n') +``` +### Color by novelty + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umap", features = "novelty", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n') +``` + +# ADT {.tabset} + +The numbers of detected proteins are more than the the first experiment. The cells are clustered slightly differently from gene expression and ATAC-Seq results on the UMAP and the first dataset. + +## UMI counts per cell + +```{r warning=FALSE, message=FALSE, results='asis'} +VlnPlot(seurat, features = "nCount_ADT", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") +cat('\n\n') +``` + +**Zoom in** (cutoff line is at `r params$min_nCount_ADT`): + +```{r warning=FALSE, message=FALSE, results='asis'} +VlnPlot(seurat, features = "nCount_ADT", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") + + ylim(0,3000) + + geom_hline(yintercept = params$min_nCount_ADT) +cat('\n\n') +``` + +## Detected proteins per cell + +Lines are drawn at `r params$min_nFeature_ADT` and `r params$max_nFeature_ADT`. + +```{r warning=FALSE, message=FALSE, results='asis'} +VlnPlot(seurat, features = "nFeature_ADT", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly()+ + xlab("") + + geom_hline(yintercept = params$max_nFeature_ADT) + + geom_hline(yintercept = params$min_nFeature_ADT) + + ylab("Number of detected proteins") +cat('\n\n') +``` + +## UMAP plots {.tabset} + +### Color by sample + +We can still see cells are clustered by sample, but not as clear as the first dataset. + +```{r results='asis'} +DimPlot(object = seurat, reduction = "umapADT", group.by = "sample", order = T) + + scale_color_cb_friendly() +cat('\n\n') +``` +```{r results='asis', fig.width=12} +DimPlot(object = seurat, reduction = "umapADT", split.by = 'sample') + + scale_color_cb_friendly() +cat('\n\n') +``` + + +### Color by UMI counts + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umapADT", features = "nCount_ADT", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n\n') +``` +### Color by the number of detected proteins + +```{r results='asis', fig.width=12} +FeaturePlot(seurat, reduction = "umapADT", features = "nFeature_ADT", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) +cat('\n\n') +``` + + + +# ATAC-Seq {.tabset} + +We use some of the results from cellranger outputs and the peaks called using MACS2 for QCing the scATAC-Seq data. + +## UMI counts per cell + +```{r warning=FALSE, message=FALSE, results='asis'} +VlnPlot(seurat, features = "nCount_MACS2peaks", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") + +cat('\n\n') +``` +## Detected peaks per cell + +```{r warning=FALSE, message=FALSE} +VlnPlot(seurat, features = "nFeature_MACS2peaks", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") + +VlnPlot(seurat, features = "nFeature_CellRangerPeaks", ncol = 1, pt.size = 0) + + scale_fill_cb_friendly() + + xlab("") + + ylab("UMI") +``` + +## QC metrics {.tabset} + +### Total number of fragments in peaks {.tabset} + +This metric represents the total number of fragments (= reads) mapping within a region of the genome that is predicted to be accessible (= a peak). It's a measure of cellular sequencing depth / complexity. Cells with very few reads may need to be excluded due to low sequencing depth. Cells with extremely high levels may represent doublets, nuclei clumps, or other artefacts. + + +```{r results='asis', warning=FALSE, message=FALSE} +DefaultAssay(seurat) <- "MACS2peaks" + +cat('#### Histogram \n\n') +seurat@meta.data %>% + ggplot(aes(x = atac_peak_region_fragments)) + + geom_histogram() + + facet_wrap(~sample) + + geom_vline(xintercept = params$min_PRF) + + +cat('\n#### Violin plot\n\n') +VlnPlot( + object = seurat, + features = 'atac_peak_region_fragments', + pt.size = 0 +) + ylab('Total number of fragments in peaks') + + NoLegend() + + geom_hline(yintercept = params$min_PRF) + xlab("") + + scale_fill_cb_friendly() + +cat('\n\n') +``` + +### Fraction of fragments in peaks + +It represents the fraction of all fragments that fall within ATAC-seq peaks. Cells with low values (i.e. <15-20%) often represent low-quality cells or technical artifacts that should be removed. Note that this value can be sensitive to the set of peaks used. + +```{r results='asis'} +VlnPlot( + object = seurat, + features = 'pct_reads_in_peaks', + pt.size = 0 +) + NoLegend() + xlab("") + + scale_fill_cb_friendly() + + geom_hline(yintercept = params$min_FRiP) + +cat('\n\n') +``` + + +### Transcriptional start site (TSS) enrichment score {.tabset} + +The ENCODE project has defined an ATAC-seq targeting score based on the ratio of fragments centered at the TSS to fragments in TSS-flanking regions (see https://www.encodeproject.org/data-standards/terms/). Poor ATAC-seq experiments typically will have a low TSS enrichment score. We can compute this metric for each cell with the TSSEnrichment() function, and the results are stored in metadata under the column name TSS.enrichment. + + +```{r results='asis'} +VlnPlot( + object = seurat, + features = 'TSS.enrichment', + pt.size = 0 +) + scale_fill_cb_friendly() + +NoLegend() + xlab("") +cat('\n\n') +``` +The following tabs show the TSS enrichment score distribution for each sample. Cells with high-quality ATAC-seq data should show a clear peak in reads at the TSS, with a decay the further we get from it. + +Each plot is split between cells with a high or low global TSS enrichment score (cuffoff at `r params$min_TSS`), to double-check whether cells with lowest enrichment scores still follow the expected pattern or rather need to be excluded. + +```{r results='asis'} +seurat$TSS.group <- ifelse(seurat$TSS.enrichment > params$min_TSS, "High", "Low") + +for (sample in sort(unique(seurat$sample))){ + cat('####', sample, '\n\n') + p <- TSSPlot(subset(x = seurat, idents = sample), group.by = "TSS.group") + NoLegend() + print(p) + cat('\n\n') +} +``` + +### Nucleosome signal + +The histogram of DNA fragment sizes (determined from the paired-end sequencing reads) should exhibit a strong nucleosome banding pattern corresponding to the length of DNA wrapped around a single nucleosome, i.e peaks at approximately 100bp (nucleosome-free), and mono, di and tri nucleosome-bound peaks at 200, 400 and 600bp. We calculate this per single cell, and quantify the approximate ratio of mononucleosomal to nucleosome-free fragments (stored as nucleosome_signal). Cells with lower nucleosome signal have a higher ratio of nucleosome-free fragments. + +```{r warning = FALSE} +seurat$nucleosome_group <- ifelse(seurat$nucleosome_signal > 1, "high NS", "low NS") +FragmentHistogram(seurat, group.by = 'nucleosome_group', cells = colnames(seurat[, seurat$sample == 'Control'])) +``` + +```{r } +VlnPlot( + object = seurat, + features = 'nucleosome_signal', + pt.size = 0 +) + scale_fill_cb_friendly() + +NoLegend() + xlab("") +``` + + + + +### Blacklist ratio + +tIt's he ratio of reads in genomic blacklist regions. The [ENCODE project](https://www.encodeproject.org/) has provided a list of [blacklist regions](https://github.com/Boyle-Lab/Blacklist), representing reads which are often associated with artefactual signal. Cells with a high proportion of reads mapping to these areas (compared to reads mapping to peaks) often represent technical artifacts and should be removed. ENCODE blacklist regions for human (hg19 and GRCh38), mouse (mm10), Drosophila (dm3), and C. elegans (ce10) are included in the Signac package. **Peaks overlapping with the balcklist regions were removed in the analysis, so we don't show blacklist fraction here**. + +Line is drawn at `r params$min_blacklistratio`. + +```{r} +VlnPlot( + object = seurat, + features = 'blacklist_fraction', + pt.size = 0 +) + scale_fill_cb_friendly() + + NoLegend() + + geom_hline(yintercept = params$min_blacklistratio) + +``` + + +## Normalization, linear dimensional reduction, and clustering + +* Normalization: Signac performs term frequency-inverse document frequency (TF-IDF) normalization. This is a two-step normalization procedure, that both normalizes across cells to correct for differences in cellular sequencing depth, and across peaks to give higher values to more rare peaks. + +* Feature selection: The low dynamic range of scATAC-seq data makes it challenging to perform variable feature selection, as we do for scRNA-seq. Instead, we can choose to use only the top n% of features (peaks) for dimensional reduction, or remove features present in less than n cells with the FindTopFeatures() function. Here, we will use all features, though we note that we see very similar results when using only a subset of features (try setting min.cutoff to ‘q75’ to use the top 25% all peaks), with faster runtimes. Features used for dimensional reduction are automatically set as VariableFeatures() for the Seurat object by this function. + +* Dimension reduction: We next run singular value decomposition (SVD) on the TD-IDF matrix, using the features (peaks) selected above. This returns a reduced dimension representation of the object (for users who are more familiar with scRNA-seq, you can think of this as analogous to the output of PCA). + +The combined steps of TF-IDF followed by SVD are known as latent semantic indexing (LSI), and were first introduced for the analysis of scATAC-seq data by [Cusanovich et al. 2015.](https://www.science.org/doi/10.1126/science.aax6234) + +The first LSI component often captures sequencing depth (techni ccal variation) rather than biological variation. If this is the case, the component should be removed from downstream analysis. We can assess the correlation between each LSI component and sequencing depth using the DepthCor() function (see below). +Here we see there is a very strong correlation between the first LSI component and the total number of counts for the cell, so we will perform downstream steps without this component. + +```{r results='asis'} +DepthCor(seurat, assay = 'MACS2peaks') +cat('\n\n') +``` + +## UMAP plots + +```{r results='asis'} +DimPlot(object = seurat, group.by = 'sample', reduction = "umapATAC") + + scale_color_cb_friendly() + +cat('\n\n') +``` + + + + +# Conclusion + +We will apply the following filters to **keep the high-quality cells**. For most of the criteria below, we don't have an upper limit, as we have removed doublets after demultiplexing. + +**RNA** + +* Number of UMIs (nCount_RNA) > `r params$minUMIs` +* Number of detected genes (nFeature_RNA) > `r params$minGenes` +* mitochondrial percentages (nFeature_RNA) < `r params$maxMitoPer` + +We don't apply any filter for complexity, as all the cells have relatively high complexity (> 0.85). + +**ADT** + +* Number of UMIs (nCount_ADT) > `r params$min_nCount_ADT` +* Number of detected proteins (nFeature_ADT) < `r params$max_nFeature_ADT` and > `r params$min_nFeature_ADT` + +**ATAC** + +* Total number of fragments in peaks (atac_peak_region_fragments) > `r params$min_PRF` +* Percentage of fragments in peaks (pct_reads_in_peaks) > `r params$min_FRiP` +* TSS enrichment (TSS.enrichment) > `r params$min_TSS` +* Nucleosome signal (nucleosome_signal) < `r params$max_NS` +* Blacklisted regions ratio (blacklist_fraction) < `r params$max_blacklistratio` + + +Number of cells before filtering: + +```{r} +table(seurat$sample) %>% as.data.frame() %>% set_colnames(c('sample', '#Cells')) %>% kable() %>% kable_styling() +``` + + +```{r} +# Please select your cutoffs +rna_keep <- seurat@meta.data[(seurat@meta.data$nCount_RNA > params$minUMIs & + seurat@meta.data$nFeature_RNA > params$minGenes & + seurat@meta.data$percent_mito < params$maxMitoPer), ] +rna_filt <- seurat@meta.data[rownames(seurat@meta.data) %!in% rownames(rna_keep),] + +adt_keep <- seurat@meta.data[(seurat@meta.data$nCount_ADT > params$min_nCount_ADT & + seurat@meta.data$nFeature_ADT > params$min_nFeature_ADT & + seurat@meta.data$nFeature_ADT < params$max_nFeature_ADT), ] + +adt_filt <- seurat@meta.data[rownames(seurat@meta.data) %!in% rownames(adt_keep),] + +atac_keep <- seurat@meta.data[(seurat@meta.data$atac_peak_region_fragments > params$min_PRF & + seurat@meta.data$pct_reads_in_peaks > params$min_FRiP & + seurat@meta.data$TSS.enrichment > params$min_TSS & + seurat@meta.data$nucleosome_signal < params$max_NS & + seurat@meta.data$blacklist_fraction < params$max_blacklistratio), ] + +atac_filt <- seurat@meta.data[rownames(seurat@meta.data) %!in% rownames(atac_keep),] + +library(ggvenn) +x = list( + RNA = rownames(rna_filt), + ADT = rownames(adt_filt), + ATAC = rownames(atac_filt) +) +ggvenn(x, names(x)) +``` + +```{r eval=FALSE} +# this is only run once before rendering +unfiltered_seurat <- seurat +seurat <- subset( + x = seurat, + subset = nCount_RNA > params$minUMIs & + nFeature_RNA > params$minGenes & + percent_mito < params$maxMitoPer & + nCount_ADT > params$min_nCount_ADT & + nFeature_ADT > params$min_nFeature_ADT & + nFeature_ADT < params$max_nFeature_ADT & + atac_peak_region_fragments > params$min_PRF & + TSS.enrichment > params$min_TSS & + nucleosome_signal < params$max_NS & + blacklist_fraction < params$max_blacklistratio + ) + +# reclustering +# RNA +DefaultAssay(seurat) <- 'RNA' +seurat <- SCTransform(seurat, conserve.memory=TRUE) +seurat <- RunPCA(object = seurat, verbose = T) +co <- 40 +seurat <- FindNeighbors(object = seurat, reduction = "pca", dims = 1:co) +seurat <- RunUMAP(object = seurat, dims=1:co ) + +# clustering by ADT data +seurat <- NormalizeData(seurat, assay = "ADT", normalization.method = "CLR", margin = 2) +seurat <- ScaleData(seurat, assay = "ADT") +seurat <- RunPCA(seurat, features = rownames(seurat@assays$ADT@counts), verbose = TRUE, assay= "ADT", reduction.name = "pcaADT", reduction.key = "pcaADT_") +seurat <- RunUMAP(seurat, dims = 1:20, reduction = "pcaADT", assay = "ADT", reduction.name = "umapADT", reduction.key = "umapADT_") + +# ATAC data normalization, dimension reduction, and clustering +seurat <- RunTFIDF(seurat, assay = "MACS2peaks") +seurat <- FindTopFeatures(seurat, min.cutoff = 'q0', assay = "MACS2peaks") +seurat <- RunSVD(seurat, assay = "MACS2peaks") +seurat <- RunUMAP(object = seurat, dims = 2:30, reduction = 'lsi', assay = "MACS2peaks", reduction.name = "umapATAC", reduction.key = "umapATAC_" ) + +qsave(seurat, 'data/tea_seurat_filtered_clustered.qs') +``` + +Number of cells **after filtering**: + +```{r} +# get the filtered seurat +stopifnot(file.exists('data/tea_seurat_filtered_clustered.qs')) +seurat <- qread('data/tea_seurat_filtered_clustered.qs') +table(seurat$sample) %>% as.data.frame() %>% set_colnames(c('sample', '#Cells')) %>% kable() %>% kable_styling() +``` + +**We take a look at the UMAPs after filtering out low quality cells for each modality.** + +```{r eval=FALSE, fig.width=12, fig.height=12} +plot_grid( + DimPlot(object = seurat, group.by = "sample", reduction = "umap") + ggtitle('RNA'), + DimPlot(object = seurat, group.by = "sample", reduction = "umapADT") + ggtitle('ADT'), + DimPlot(object = seurat, group.by = "sample", reduction = "umapATAC") + ggtitle('ATAC') +) +``` + + +# Session info + +```{r} +devtools::session_info() +``` + diff --git a/inst/rmarkdown/templates/teaseq/skeleton/README.md b/inst/rmarkdown/templates/teaseq/skeleton/README.md new file mode 100644 index 0000000..0a6a5b2 --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/skeleton/README.md @@ -0,0 +1,17 @@ +# Tipical steps for scRNAseq downstream analysis + +# DropBox + +- In `reports/QC` + - [ ] copy QC `Rmd/R/html/figures` +- In `reports/Clusters` + - [ ] the analysis of `SCTransform`, ,`RunPCA` ,`FindNeighbors`, ,`FindClusters`, `RunUMAP` + - [ ] the analysis of `FindMarkers` and `Cell Identification` +- In `reports/DE`, for *each analysis*: + - TBD + +# GitHub + +- [ ] Push all `*Rmd` `*R` files used for the *QC* and *DE* analysis respecting folder structure. + +Please, ignore `*html/figures/csv` and any output of the code. diff --git a/inst/rmarkdown/templates/teaseq/skeleton/information.R b/inst/rmarkdown/templates/teaseq/skeleton/information.R new file mode 100644 index 0000000..3444196 --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/skeleton/information.R @@ -0,0 +1,8 @@ +# project params +root = "../" +date = "YYYYMMDD" +column = "treatment" +subset_column = 'cell' +metadata_fn = "../meta/samplesheet.csv" +counts_fn = '../data/tximport-counts.csv' +basedir <- 'reports' diff --git a/inst/rmarkdown/templates/teaseq/skeleton/scripts/fix_filenames.R b/inst/rmarkdown/templates/teaseq/skeleton/scripts/fix_filenames.R new file mode 100644 index 0000000..5ef12c2 --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/skeleton/scripts/fix_filenames.R @@ -0,0 +1,40 @@ +library(stringr) +fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq/", pattern = "_S[1-9]") + + +dir.create("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq_fixed") +sapply(fsq, function(fn){ + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("20240417_GEX",ending) + fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq",fn)), + new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq_fixed",fnnew))) +}) + + +fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq", pattern = "_S[1-9]") +dir.create("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq_fixed") +sapply(fsq, function(fn){ + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("20240417_ATAC",ending) + fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq",fn)), + new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq_fixed",fnnew))) +}) + + +fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq/", pattern = "_S[1-9]") +dir.create("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq_fixed") +sapply(fsq, function(fn){ + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("20240417_ADT",ending) + fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq",fn)), + new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq_fixed",fnnew))) +}) + +fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq/", pattern = "_S[1-9]") +dir.create("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq_fixed") +sapply(fsq, function(fn){ + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("20240417_HTO",ending) + fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq",fn)), + new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq_fixed",fnnew))) +}) diff --git a/inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_adt_hto.sbatch b/inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_adt_hto.sbatch new file mode 100644 index 0000000..55d25bd --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_adt_hto.sbatch @@ -0,0 +1,23 @@ +#!/bin/bash + +#SBATCH --job-name=CellrangerMulti # Job name +#SBATCH --partition=priority # Partition name +#SBATCH --time=1-23:59 # Runtime in D-HH:MM format +#SBATCH --nodes=1 # Number of nodes (keep at 1) +#SBATCH --ntasks=1 # Number of tasks per node (keep at 1) +#SBATCH --cpus-per-task=16 # CPU cores requested per task (change for threaded jobs) +#SBATCH --mem=128G # Memory needed per node (total) +#SBATCH --error=jobid_%j.err # File to which STDERR will be written, including job ID +#SBATCH --output=jobid_%j.out # File to which STDOUT will be written, including job ID +#SBATCH --mail-type=ALL # Type of email notification (BEGIN, END, FAIL, ALL) + +module load cellranger/7.0.0 + +localcores=$SLURM_CPUS_PER_TASK +localmem=$SLURM_MEM_PER_NODE + +#change CSV to full path +cellranger multi --id=AtheTeaSeqMulti --csv=meta/multiconfig_batch3.csv + + + diff --git a/inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_atac.sbatch b/inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_atac.sbatch new file mode 100644 index 0000000..2af343c --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_atac.sbatch @@ -0,0 +1,24 @@ +#!/bin/bash + +#SBATCH --job-name=Cellranger-Arc # Job name +#SBATCH --partition=priority # Partition name +#SBATCH --time=1-23:59 # Runtime in D-HH:MM format +#SBATCH --nodes=1 # Number of nodes (keep at 1) +#SBATCH --ntasks=1 # Number of tasks per node (keep at 1) +#SBATCH --cpus-per-task=16 # CPU cores requested per task (change for threaded jobs) +#SBATCH --mem=128G # Memory needed per node (total) +#SBATCH --error=jobid_%j.err # File to which STDERR will be written, including job ID +#SBATCH --output=jobid_%j.out # File to which STDOUT will be written, including job ID +#SBATCH --mail-type=ALL # Type of email notification (BEGIN, END, FAIL, ALL) + +module load cellranger-ARC/2.0.0 + +localcores=$SLURM_CPUS_PER_TASK +localmem=$SLURM_MEM_PER_NODE + +# Change libraries to full path +cellranger-arc count --id=cellranger-arc_output \ + --reference=/n/shared_db/GRCh38/uk/cellranger-ARC/2.0.0/2.0.0/refdata-cellranger-arc-GRCh38-2020-A-2.0.0 \ + --libraries=meta/libraries.csv \ + --localcores=16 \ + --localmem=128 diff --git a/inst/rmarkdown/templates/teaseq/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/teaseq/skeleton/skeleton.Rmd new file mode 100644 index 0000000..dc4bbf5 --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/skeleton/skeleton.Rmd @@ -0,0 +1,25 @@ +--- +title: "General Project Information" +author: "Harvard Chan Bioinformatics Core" +date: "`r Sys.Date()`" +output: + html_document: + code_folding: hide + df_print: paged + highlights: pygments + number_sections: true + self_contained: true + theme: default + toc: true + toc_float: + collapsed: true + smooth_scroll: true +editor_options: + chunk_output_type: console +params: + params_file: information.R +--- + +```{r echo = F} +source(params$params_file) +``` diff --git a/inst/rmarkdown/templates/teaseq/template.yml b/inst/rmarkdown/templates/teaseq/template.yml new file mode 100644 index 0000000..6838f13 --- /dev/null +++ b/inst/rmarkdown/templates/teaseq/template.yml @@ -0,0 +1,3 @@ +name: bcbio TEAseq +description: Standard TEAseq down-stream analyses +create_dir: false From 87c1908a337bfcad05a054b482dd9cfaa7a0ba6a Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 15 May 2024 15:46:45 -0400 Subject: [PATCH 18/55] fix paths --- inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd | 5 +++-- .../templates/rnaseq/skeleton/DE/run_markdown.R | 11 ++++++----- .../templates/rnaseq/skeleton/QC/run_markdown.R | 4 ++-- 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index ae22b14..7e50c0d 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -87,6 +87,7 @@ set.seed(1234567890L) coldata <- load_coldata(coldata_fn, column, numerator, denominator, subset_column, subset_value) +coldata$sample=row.names(coldata) metrics <- load_metrics(se_object, multiqc_data_dir) %>% left_join(coldata, by = c('sample' = 'description')) %>% as.data.frame() @@ -288,7 +289,7 @@ EnhancedVolcano(res_mod, ```{r heapmap} ### Run pheatmap using the metadata data frame for the annotation ma=norm_matrix[res_sig$gene_id,] -colma=coldata[,c("sample_type"), drop=FALSE] +colma=coldata[,c(column), drop=FALSE] colors=lapply(colnames(colma), function(c){ l.col=colors[1:length(unique(colma[[c]]))] names(l.col)=unique(colma[[c]]) @@ -326,7 +327,7 @@ top_n_exp <- norm_matrix %>% as.data.frame() %>% # dplyr::select(-group, -group_name) %>% pivot_longer(!gene_id, names_to = 'sample', values_to = 'log2_expression') %>% right_join(top_n, relationship = "many-to-many") %>% - left_join(coldata, by = c('sample' = 'description')) + left_join(coldata, by = 'sample') ggplot(top_n_exp, aes_string(x = column, y = 'log2_expression')) + geom_boxplot(outlier.shape = NA, linewidth=0.5, color="grey") + diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R index 18d6398..e209d6b 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R @@ -1,18 +1,19 @@ library(rmarkdown) # set working directory to this file before using the function -render_de <- function(numerator, denominator, subset_value = NA, +render_de <- function(column,numerator, denominator, subset_value = NA, params_file = 'params_de.R'){ - rmarkdown::render(input = "./inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd", - output_dir = "./inst/rmarkdown/templates/rnaseq/skeleton/DE/", + rmarkdown::render(input = "DEG.Rmd", + output_dir = ".", output_format = "html_document", - output_file = ifelse(!is.na(subset_value), + output_file = ifelse(!is.null(subset_value), paste0('DE_', subset_value, '_', numerator, '_vs_', denominator, '.html'), paste0('DE_', numerator, '_vs_', denominator, '.html') ), clean = TRUE, envir = new.env(), params = list( + column = column, subset_value = subset_value, numerator = numerator, denominator = denominator, @@ -23,4 +24,4 @@ render_de <- function(numerator, denominator, subset_value = NA, ) } -render_de("tumor", "normal") +render_de("sample_type","tumor", "normal") diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/run_markdown.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/run_markdown.R index a2a2ec5..bb07111 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/run_markdown.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/run_markdown.R @@ -1,7 +1,7 @@ library(rmarkdown) -rmarkdown::render("./inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd", - output_dir = "./inst/rmarkdown/templates/rnaseq/skeleton/QC/", +rmarkdown::render("QC_nf-core.Rmd", + output_dir = ".", clean = TRUE, output_format = "html_document", params = list( From 044634b559ea9a5fa1e628f04610504923c455f0 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 15 May 2024 16:43:15 -0400 Subject: [PATCH 19/55] cleanup singlecell --- .../singlecell/skeleton/QC/QC-01-load_data.R | 49 -- .../skeleton/QC/QC-02-run_analysis.R | 100 --- .../singlecell/skeleton/QC/TEAseq.Rmd | 725 ------------------ .../meta/TEAseq/feature_reference.csv | 172 ----- .../skeleton/meta/TEAseq/libraries.csv | 4 - .../skeleton/meta/TEAseq/multiconfig.csv | 22 - .../skeleton/scripts/fix_filenames.R | 40 - .../skeleton/scripts/gtex_adt_hto.sbatch | 23 - .../skeleton/scripts/gtex_atac.sbatch | 24 - 9 files changed, 1159 deletions(-) delete mode 100644 inst/rmarkdown/templates/singlecell/skeleton/QC/QC-01-load_data.R delete mode 100644 inst/rmarkdown/templates/singlecell/skeleton/QC/QC-02-run_analysis.R delete mode 100644 inst/rmarkdown/templates/singlecell/skeleton/QC/TEAseq.Rmd delete mode 100644 inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/feature_reference.csv delete mode 100644 inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/libraries.csv delete mode 100644 inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/multiconfig.csv delete mode 100644 inst/rmarkdown/templates/singlecell/skeleton/scripts/fix_filenames.R delete mode 100644 inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch delete mode 100644 inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch diff --git a/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-01-load_data.R b/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-01-load_data.R deleted file mode 100644 index 2e79160..0000000 --- a/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-01-load_data.R +++ /dev/null @@ -1,49 +0,0 @@ -# prepare seurat object with all 3 modes - -# read cellranger-arc outs -#arc_h5 <- '../aspera_download_sep23/SN0288408/KW11249_atsibris/230628_10X_KW11252_bcl/cellranger-arc-2.0.0/GRCh38/BRI-2286/outs/filtered_feature_bc_matrix.h5' -arc_h5 <- '../final/batch3_atac/cellranger-arc_output/outs/filtered_feature_bc_matrix.h5' - -arc_data <- Read10X_h5(arc_h5) - -# read cellranger outs -#h5path <- '../aspera_download_sep23/SN0288408/KW11249_atsibris/230711_10X_KW11249_bcl/cellranger-7.1.0/GRCh38/BRI-2285_hashing/outs/filtered_feature_bc_matrix.h5' -h5path <- '../final/batch3_adt/AtheTeaSeqMulti/outs/multi/count/raw_feature_bc_matrix.h5' -counts <- Read10X_h5(h5path) - -# select cell barcodes detected by both ATAC and ADT -joint.bcs <- intersect(colnames(arc_data$Peaks), colnames(counts$`Antibody Capture`)) - -so <- CreateSeuratObject(counts = arc_data$`Gene Expression`[, joint.bcs]) - -# add cellranger ATAC -# fragpath <- '../aspera_download_sep23/SN0288408/KW11249_atsibris/230628_10X_KW11252_bcl/cellranger-arc-2.0.0/GRCh38/BRI-2286/outs/atac_fragments.tsv.gz' -fragpath <- '../final/batch3_atac/cellranger-arc_output/outs/atac_fragments.tsv.gz' -annotation <- GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86) -seqlevels(annotation) <- paste0('chr', seqlevels(annotation)) - -so[["CellRangerPeaks"]] <- CreateChromatinAssay( - counts = arc_data$Peaks[, joint.bcs], - sep = c(":", "-"), - fragments = fragpath, - annotation = annotation -) - -# add HTO -so[["HTO"]] <- CreateAssayObject(counts = counts$`Multiplexing Capture`[c('Hashtag1', 'Hashtag2','Hashtag3', 'Hashtag4', 'Hashtag5','Hashtag6','Hashtag7'), joint.bcs]) -# Normalize HTO data, here we use centered log-ratio (CLR) transformation -so <- NormalizeData(so, assay = "HTO", normalization.method = "CLR") -so <- HTODemux(so, assay = "HTO", positive.quantile = 0.99) - -# table(so@meta.data$orig.ident, so@meta.data$hash.ID) %>% as.matrix() %>% kable() %>% kable_styling() - -# add ADT data -so[["ADT"]] <- CreateAssayObject(counts = counts$`Antibody Capture`[, joint.bcs]) -so <- NormalizeData(so, assay = "ADT", normalization.method = "CLR", margin = 2) - -qsave(so, "data/tea_seurat.qs") - -hashtag_counts <- table(so@meta.data$hash.ID) %>% as.data.frame() %>% - set_colnames(c('Hashtag', 'Number of cells')) %>% dplyr::filter(grepl("Hash", Hashtag)) -write.csv(hashtag_counts, 'data/hashtag_counts.csv', row.names = FALSE) - diff --git a/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-02-run_analysis.R b/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-02-run_analysis.R deleted file mode 100644 index 997d00d..0000000 --- a/inst/rmarkdown/templates/singlecell/skeleton/QC/QC-02-run_analysis.R +++ /dev/null @@ -1,100 +0,0 @@ - -seurat <- qread("data/tea_seurat.qs") - -# keep singlet only -seurat <- subset(x = seurat, subset = HTO_classification.global == "Singlet") -# table(seurat@meta.data$hash.ID) - -# add sampleinfo to metadata -seurat@meta.data <- seurat@meta.data %>% - rownames_to_column("cellID") %>% - left_join(sampleinfo, by = "hash.ID") %>% - column_to_rownames("cellID") - -#Compute percent mito ratio -seurat$percent_mito <- PercentageFeatureSet(seurat, pattern = "^MT") -# Compute total reads -seurat$total_reads <- Matrix::colSums(GetAssayData(seurat, slot = "counts")) -# Compute novelty score -seurat$novelty <- log10(seurat@meta.data$nFeature_RNA)/log10(seurat@meta.data$nCount_RNA) - -seurat <- SCTransform(seurat, conserve.memory=TRUE) - -# cell cycle -cc_genes <- cc.genes.updated.2019 - -# Extract phase specific genes -s.genes <- cc_genes$s.genes -g2m.genes <- cc_genes$g2m.genes - -# Perform cell cycle scoring -seurat <- CellCycleScoring(object = seurat, - s.features = s.genes, - g2m.features = g2m.genes, - set.ident = TRUE) - -# clustering -seurat <- RunPCA(object = seurat, verbose = T) -pct <- seurat@reductions$pca@stdev/sum(seurat@reductions$pca@stdev) * 100 -co1 <- which(cumsum(pct) > 90 & pct < 5)[1] -co2 <- sort(which((pct[1:length(pct) - 1] - pct[2:length(pct)]) > 0.1), decreasing = T)[1] + 1 -# co <- min(co1, co2) -co <- 30 -seurat <- RunPCA(object = seurat, npcs = co, verbose = TRUE) -seurat <- FindNeighbors(object = seurat, reduction = "pca", dims = 1:co) -seurat <- RunUMAP(object = seurat, dims=1:co ) - -# clustering by ADT data # Error: didn't do CLR norm because copied the code from 1st dataset -seurat <- ScaleData(seurat, assay = "ADT") -seurat <- RunPCA(seurat, features = rownames(seurat@assays$ADT@counts), verbose = TRUE, assay= "ADT", reduction.name = "pcaADT", reduction.key = "pcaADT_") -seurat <- RunUMAP(seurat, dims = 1:20, reduction = "pcaADT", assay = "ADT", reduction.name = "umapADT", reduction.key = "umapADT_") - -# calling peaks using MACS2, following James's code -DefaultAssay(seurat)<-"CellRangerPeaks" -peaks <- CallPeaks(seurat) # took 16 min -peaks <- keepStandardChromosomes(peaks, pruning.mode = "coarse") -# peaks <- subsetByOverlaps(x = peaks, ranges = blacklist_hg38_unified, invert = TRUE) - - -# quantify counts in each peak -macs2_counts <- FeatureMatrix( - fragments = Fragments(seurat), - features = peaks, - cells = colnames(seurat) -) - -#qsave(seurat,"data/tmp.qs") -# ATAC data normalization, dimension reduction, and clustering -DefaultAssay(seurat)<-"MACS2peaks" -seurat <- RunTFIDF(seurat, assay = "MACS2peaks") -seurat <- FindTopFeatures(seurat, min.cutoff = 'q0', assay = "MACS2peaks") -seurat <- RunSVD(seurat, assay = "MACS2peaks") -seurat <- RunUMAP(object = seurat, dims = 2:30, reduction = 'lsi', assay = "MACS2peaks", reduction.name = "umapATAC", reduction.key = "umapATAC_" ) -seurat <- FindNeighbors(object = seurat, reduction = 'lsi', dims = 2:30) -seurat <- FindClusters(object = seurat, verbose = FALSE, algorithm = 3) - -seurat$blacklist_fraction <- FractionCountsInRegion( - object = seurat, - assay = 'MACS2peaks', - regions = blacklist_hg38_unified -) - -## add ATAC per barcode metrics -pbm <- read.csv( - file = "cellranger-arc_output/outs/per_barcode_metrics.csv", - header = TRUE, - row.names = 1 -) - -seurat@meta.data <- seurat@meta.data %>% - rownames_to_column("cellID") %>% - left_join(pbm %>% rownames_to_column('cellID'), by = "cellID") %>% - column_to_rownames("cellID") - -seurat$pct_reads_in_peaks <- seurat$atac_peak_region_fragments / seurat$atac_fragments * 100 - -# compute nucleosome signal score per cell -seurat <- NucleosomeSignal(object = seurat) # added "nucleosome_signal", "nucleosome_percentile" to metadata -# compute TSS enrichment score per cell -seurat <- TSSEnrichment(object = seurat, fast = FALSE) # added "TSS.enrichment", "TSS.percentile" to metadata -qsave(seurat, 'data/tea_seurat_unfiltered_clustered.qs') diff --git a/inst/rmarkdown/templates/singlecell/skeleton/QC/TEAseq.Rmd b/inst/rmarkdown/templates/singlecell/skeleton/QC/TEAseq.Rmd deleted file mode 100644 index 9f1a302..0000000 --- a/inst/rmarkdown/templates/singlecell/skeleton/QC/TEAseq.Rmd +++ /dev/null @@ -1,725 +0,0 @@ ---- -title: "Quality Control TEAseq" -title: "Differential Expression" -author: "Harvard Chan Bioinformatics Core" -date: "`r Sys.Date()`" -output: - html_document: - code_folding: hide - df_print: paged - highlights: pygments - number_sections: true - self_contained: true - theme: default - toc: true - toc_float: - collapsed: true - smooth_scroll: true -editor_options: - chunk_output_type: console -params: - maxGenes: !r Inf - minGenes: 750 - maxUMIs: !r Inf - minUMIs: 1000 - maxMitoPer: 20 - min_novelty: 0.85 - min_cells_per_gene: 10 - min_reads_per_cell: 1000 - max_nCount_ADT: !r Inf - min_nCount_ADT: 400 - max_nFeature_ADT: 135 - min_nFeature_ADT: 75 - max_PRF: !r Inf - min_PRF: 500 - max_FRiP: !r Inf - min_FRiP: 25 - min_TSS: 2 - max_NS: 2 - max_blacklistratio: 0.02 - data_dir: !r file.path("data") - results_dir: !r file.path("results") -output: - html_document: - code_folding: hide - df_print: paged - highlight: tango - number_sections: false - self_contained: true - theme: paper - toc: true - toc_float: - collapsed: true - smooth_scroll: false ---- - -```{r render} -# Set seed for reproducibility -set.seed(1454944673L) -library(knitr) -opts_chunk[["set"]]( - audodep = TRUE, - cache = FALSE, - cache.lazy = FALSE, - error = TRUE, - fig.height = 6L, - fig.width = 6L, - message = FALSE, - tidy = TRUE, - warning = FALSE -) -``` - -```{r setup, cache=FALSE, message=FALSE} -library(Seurat) -library(tidyverse) -library(ggplot2) -library(ggrepel) -library(cowplot) -library(Matrix) -library(data.table) -library(magrittr) -library(kableExtra) -library(Signac) -library(EnsDb.Hsapiens.v86) -library(qs) -library(bcbioR) -invisible(mapply( - FUN = dir.create, - path = c(params$data_dir, params$results_dir), - MoreArgs = list(showWarnings = FALSE, recursive = TRUE) -)) - -ggplot2::theme_set(theme_light(base_size = 14)) -``` - - -```{r functions, cache=FALSE, message=FALSE} -# Load functions -`%!in%` = Negate(`%in%`) -``` - -# Overview - -```{r} -# Run QC-01-load_data.R and then -# Run QC-02-run_analysis.R -stopifnot(file.exists('data/tea_seurat_unfiltered_clustered.qs')) -seurat <- qread('data/tea_seurat_unfiltered_clustered.qs') - -stopifnot(file.exists('data/hashtag_counts.csv')) -hashtag_counts <- read.csv('data/hashtag_counts.csv') -``` - -- Principal Investigator: -- Experiment: TEA-Seq of .... - -The downloaded data are cellranger-arc (for gene expression and ATAC-Seq) and cellranger (for gene expression and ADT) outputs. Alignment was done using reference genome GRCh38. We use the shared cell barcodes from both results to create a Seurat object that has all 3 modalities. - -First of all, we demultiplex the data so we know which cell belongs to which sample. Below shows the number of cells for each hashtag after demultiplexing. Doublets and Negatives are removed. Overall we have good number of cells per sample. - -```{r} -hashtag_counts <- hashtag_counts %>% plyr::rename(c('Hashtag'='hash.ID')) -# hashtag_counts %>% kable() %>% kable_styling() -``` - - -```{r} -# sample info -sampleinfo = c("Hashtag1: no drug (DMSO)=Control -Hashtag2: Romidepsin=RMD -Hashtag3: Bryostatin=BRY -Hashtag4: AZD=AZD -Hashtag5: RB=RMD+BRY -Hashtag6: RA=RMD+AZD -Hashtag7: PMAi=PMAi") - -sampleinfo <- as.data.frame(matrix(unlist(str_split(sampleinfo[1], '\n|: |=')), - ncol = 3, byrow = T)) %>% - setNames(c('hash.ID', 'Description', 'Sample')) - -sampleinfo %>% full_join(hashtag_counts, by = 'hash.ID') %>% kable() %>% kable_styling() -``` - -Then we normalize and cluster the data for each modality separately for QC purpose. - -# RNA {.tabset} - -Let's first check the gene expression data. - -## Number of cells per sample - - -```{r} -# Visualize the number of counts per sample -seurat@meta.data %>% - ggplot() + - geom_bar(aes(x=sample)) + - theme(axis.text.x = element_text(angle = 90, vjust = 1, hjust=1), - plot.title = element_text(hjust=0.5)) + - ggtitle("Number of Cells per Sample") -``` - -## Reads per sample - - -```{r warning=F, message=F} -seurat@meta.data %>% - group_by(sample) %>% - summarise(Reads = sum(nCount_RNA)) %>% - ggplot(aes(x=sample, y=Reads)) + - geom_bar(stat = "identity") + - theme(axis.text.x =element_text(angle = 90,vjust = 1, hjust=1)) + - ggtitle("Number of Reads per Sample") -``` - -## UMI counts per cell {.tabset} - -Now let's assess the distribution of unique molecular identifier (UMI)-deconvoluted counts per cell. The UMI counts per cell should be generally above 500. The cutoff is at `r params$minUMIs`. - -```{r warning=FALSE, message=FALSE, fig.height=8, fig.width=12, results='asis'} -cat('### Histogram \n\n') -seurat@meta.data %>% - ggplot(aes(x = nCount_RNA)) + - geom_histogram(binwidth = 50) + - xlab("nUMI") + - facet_wrap(~sample) + - geom_vline(xintercept = params$minUMIs) - -cat('\n### Violin plot \n\n') -VlnPlot(seurat, features="nCount_RNA", pt.size = 0) + - scale_fill_cb_friendly() + - geom_hline(yintercept = params$minUMIs) -``` - -## Genes detected per cell {.tabset} - -Here by "detected", we mean genes with a non-zero count measurement per cell. Seeing gene detection in the range of `500`-`5000` is normal for most single-cell experiments. -The cutoff is at `r params$minGenes`. - -```{r warning=FALSE, message=FALSE, fig.height=8, fig.width=12, results='asis'} -cat('### Histogram \n\n') -seurat@meta.data %>% - ggplot(aes(x = nFeature_RNA)) + - geom_histogram(binwidth = 50) + - xlab("nGene") + - facet_wrap(~sample) + - geom_vline(xintercept = params$minGenes) - -cat('\n### Violin plot \n\n') -VlnPlot(seurat, features="nFeature_RNA", pt.size = 0) + - scale_fill_cb_friendly() + - geom_hline(yintercept = params$minGenes) -``` - - -## Mitochondrial abundance {.tabset} - -We evaluate overall mitochondrial gene expression as a biomarker of cellular stress during sample preparation. The mitochondrial percentages are mostly below 20%. - - -```{r warning=F, message=F, fig.height=8, fig.width=12, results='asis'} -cat('### Histogram \n\n') -seurat@meta.data %>% - ggplot(aes(x = percent_mito)) + - geom_histogram() + - facet_wrap(~sample) + - geom_vline(xintercept = params$maxMitoPer) - -cat('\n### Violin plot \n\n') -VlnPlot(seurat, features="percent_mito", pt.size = 0) + - scale_fill_cb_friendly() + - geom_hline(yintercept = params$maxMitoPer) -``` - -## Novelty {.tabset} - -Another way to QC the data is to look for less novelty, that is cells that have less genes detected per count than other cells. We can see the samples where we sequenced each cell less have a higher overall novelty, that is because we have not started saturated the sequencing for any given gene for these samples. Outlier cells in these samples might be cells that we have a less complex RNA species than other cells. Sometimes we can detect contamination with low complexity cell types like red blood cells via this metric. - -All cells have high novelty scores (> 0.85). - -```{r novelty, fig.height=8, fig.width=12, results='asis'} -cat('### Histogram \n\n') -# Visualize the overall novelty of the gene expression by visualizing the genes detected per UMI -seurat@meta.data %>% - ggplot(aes(x=novelty)) + - geom_histogram(bins=50) + - facet_wrap(~sample) - -cat('\n### Violin plot \n\n') -VlnPlot(seurat, features="novelty", pt.size = 0) + - scale_fill_cb_friendly() -``` - -## UMIs vs. genes detected {.tabset} - -If we graph out the total number of UMI counts per cell vs. the genes detected per cell, we can assess whether there is a large population of low quality cells with low counts and/or gene detection. For some of the samples there is a cluster of cells that have increasing UMIs yet gene detection is low. We will want to filter out these low complexity cells. The data points are colored by different metrics to see if there is some correlation. - -### Data points colored by Percent mitochondrial genes - -```{r umis_vs_genes_mito, warning=F, message=F, fig.height=8, fig.width=12} -# Visualize the correlation between genes detected and number of UMIs and determine whether strong presence of cells with low numbers of genes/UMIs -seurat@meta.data %>% - ggplot(aes(x=nCount_RNA, y=nFeature_RNA, color=percent_mito)) + - geom_point() + - stat_smooth(method=lm) + - scale_x_log10() + - scale_y_log10() + - geom_vline(xintercept = params$minUMIs) + - geom_hline(yintercept = params$minGenes) + - xlab("nUMI") + ylab("nGene") + - facet_wrap(~sample) -``` - -### Data points colored by Novelty - -```{r umis_vs_genes_novelty, warning=F, message=F, fig.height=8, fig.width=12} -# Visualize the correlation between genes detected and number of UMIs and determine whether strong presence of cells with low numbers of genes/UMIs -seurat@meta.data %>% - ggplot(aes(x=nCount_RNA, y=nFeature_RNA, color=novelty)) + - geom_point() + - stat_smooth(method=lm) + - scale_x_log10() + - scale_y_log10() + - geom_vline(xintercept = params$minUMIs) + - geom_hline(yintercept = params$minGenes) + - xlab("nUMI") + ylab("nGene") + - facet_wrap(~sample) -``` - - -## UMAP plots {.tabset} - -### Color by sample - -Clustering of the cells is similar to the first dataset. - -```{r results='asis'} -DimPlot(object = seurat, reduction = "umap", group.by = "sample", order = T) + - scale_color_cb_friendly() - -cat('\n\n') -``` - -```{r results='asis', fig.width=12} -DimPlot(object = seurat, reduction = "umap", split.by = 'sample')+ - scale_color_cb_friendly() -cat('\n\n') -``` - -### Color by cell cycle - -```{r results='asis'} -DimPlot(object = seurat, reduction = "umap", group.by = "Phase", order = T) + - scale_color_cb_friendly() -cat('\n\n') -``` - -```{r results='asis', fig.width=12} -DimPlot(object = seurat, reduction = "umap", group.by = "Phase", split.by = 'sample') + - scale_color_cb_friendly() -cat('\n\n') -``` - -### Color by UMI counts - -```{r results='asis', fig.width=12} -FeaturePlot(seurat, reduction = "umap", features = "nCount_RNA", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) -cat('\n\n') -``` -### Color by the number of detected genes - -```{r results='asis', fig.width=12} -FeaturePlot(seurat, reduction = "umap", features = "nFeature_RNA", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) -cat('\n\n') -``` -### Color by mito percentage - -```{r results='asis', fig.width=12} -FeaturePlot(seurat, reduction = "umap", features = "percent_mito", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) -cat('\n') -``` -### Color by novelty - -```{r results='asis', fig.width=12} -FeaturePlot(seurat, reduction = "umap", features = "novelty", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) -cat('\n') -``` - -# ADT {.tabset} - -The numbers of detected proteins are more than the the first experiment. The cells are clustered slightly differently from gene expression and ATAC-Seq results on the UMAP and the first dataset. - -## UMI counts per cell - -```{r warning=FALSE, message=FALSE, results='asis'} -VlnPlot(seurat, features = "nCount_ADT", ncol = 1, pt.size = 0) + - scale_fill_cb_friendly() + - xlab("") + - ylab("UMI") -cat('\n\n') -``` - -**Zoom in** (cutoff line is at `r params$min_nCount_ADT`): - -```{r warning=FALSE, message=FALSE, results='asis'} -VlnPlot(seurat, features = "nCount_ADT", ncol = 1, pt.size = 0) + - scale_fill_cb_friendly() + - xlab("") + - ylab("UMI") + - ylim(0,3000) + - geom_hline(yintercept = params$min_nCount_ADT) -cat('\n\n') -``` - -## Detected proteins per cell - -Lines are drawn at `r params$min_nFeature_ADT` and `r params$max_nFeature_ADT`. - -```{r warning=FALSE, message=FALSE, results='asis'} -VlnPlot(seurat, features = "nFeature_ADT", ncol = 1, pt.size = 0) + - scale_fill_cb_friendly()+ - xlab("") + - geom_hline(yintercept = params$max_nFeature_ADT) + - geom_hline(yintercept = params$min_nFeature_ADT) + - ylab("Number of detected proteins") -cat('\n\n') -``` - -## UMAP plots {.tabset} - -### Color by sample - -We can still see cells are clustered by sample, but not as clear as the first dataset. - -```{r results='asis'} -DimPlot(object = seurat, reduction = "umapADT", group.by = "sample", order = T) + - scale_color_cb_friendly() -cat('\n\n') -``` -```{r results='asis', fig.width=12} -DimPlot(object = seurat, reduction = "umapADT", split.by = 'sample') + - scale_color_cb_friendly() -cat('\n\n') -``` - - -### Color by UMI counts - -```{r results='asis', fig.width=12} -FeaturePlot(seurat, reduction = "umapADT", features = "nCount_ADT", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) -cat('\n\n') -``` -### Color by the number of detected proteins - -```{r results='asis', fig.width=12} -FeaturePlot(seurat, reduction = "umapADT", features = "nFeature_ADT", split.by = "sample", cols = c("light blue", "red")) & theme(legend.position = c(0.2,0.2)) -cat('\n\n') -``` - - - -# ATAC-Seq {.tabset} - -We use some of the results from cellranger outputs and the peaks called using MACS2 for QCing the scATAC-Seq data. - -## UMI counts per cell - -```{r warning=FALSE, message=FALSE, results='asis'} -VlnPlot(seurat, features = "nCount_MACS2peaks", ncol = 1, pt.size = 0) + - scale_fill_cb_friendly() + - xlab("") + - ylab("UMI") - -cat('\n\n') -``` -## Detected peaks per cell - -```{r warning=FALSE, message=FALSE} -VlnPlot(seurat, features = "nFeature_MACS2peaks", ncol = 1, pt.size = 0) + - scale_fill_cb_friendly() + - xlab("") + - ylab("UMI") - -VlnPlot(seurat, features = "nFeature_CellRangerPeaks", ncol = 1, pt.size = 0) + - scale_fill_cb_friendly() + - xlab("") + - ylab("UMI") -``` - -## QC metrics {.tabset} - -### Total number of fragments in peaks {.tabset} - -This metric represents the total number of fragments (= reads) mapping within a region of the genome that is predicted to be accessible (= a peak). It's a measure of cellular sequencing depth / complexity. Cells with very few reads may need to be excluded due to low sequencing depth. Cells with extremely high levels may represent doublets, nuclei clumps, or other artefacts. - - -```{r results='asis', warning=FALSE, message=FALSE} -DefaultAssay(seurat) <- "MACS2peaks" - -cat('#### Histogram \n\n') -seurat@meta.data %>% - ggplot(aes(x = atac_peak_region_fragments)) + - geom_histogram() + - facet_wrap(~sample) + - geom_vline(xintercept = params$min_PRF) - - -cat('\n#### Violin plot\n\n') -VlnPlot( - object = seurat, - features = 'atac_peak_region_fragments', - pt.size = 0 -) + ylab('Total number of fragments in peaks') + - NoLegend() + - geom_hline(yintercept = params$min_PRF) + xlab("") + - scale_fill_cb_friendly() - -cat('\n\n') -``` - -### Fraction of fragments in peaks - -It represents the fraction of all fragments that fall within ATAC-seq peaks. Cells with low values (i.e. <15-20%) often represent low-quality cells or technical artifacts that should be removed. Note that this value can be sensitive to the set of peaks used. - -```{r results='asis'} -VlnPlot( - object = seurat, - features = 'pct_reads_in_peaks', - pt.size = 0 -) + NoLegend() + xlab("") + - scale_fill_cb_friendly() + - geom_hline(yintercept = params$min_FRiP) - -cat('\n\n') -``` - - -### Transcriptional start site (TSS) enrichment score {.tabset} - -The ENCODE project has defined an ATAC-seq targeting score based on the ratio of fragments centered at the TSS to fragments in TSS-flanking regions (see https://www.encodeproject.org/data-standards/terms/). Poor ATAC-seq experiments typically will have a low TSS enrichment score. We can compute this metric for each cell with the TSSEnrichment() function, and the results are stored in metadata under the column name TSS.enrichment. - - -```{r results='asis'} -VlnPlot( - object = seurat, - features = 'TSS.enrichment', - pt.size = 0 -) + scale_fill_cb_friendly() + -NoLegend() + xlab("") -cat('\n\n') -``` -The following tabs show the TSS enrichment score distribution for each sample. Cells with high-quality ATAC-seq data should show a clear peak in reads at the TSS, with a decay the further we get from it. - -Each plot is split between cells with a high or low global TSS enrichment score (cuffoff at `r params$min_TSS`), to double-check whether cells with lowest enrichment scores still follow the expected pattern or rather need to be excluded. - -```{r results='asis'} -seurat$TSS.group <- ifelse(seurat$TSS.enrichment > params$min_TSS, "High", "Low") - -for (sample in sort(unique(seurat$sample))){ - cat('####', sample, '\n\n') - p <- TSSPlot(subset(x = seurat, idents = sample), group.by = "TSS.group") + NoLegend() - print(p) - cat('\n\n') -} -``` - -### Nucleosome signal - -The histogram of DNA fragment sizes (determined from the paired-end sequencing reads) should exhibit a strong nucleosome banding pattern corresponding to the length of DNA wrapped around a single nucleosome, i.e peaks at approximately 100bp (nucleosome-free), and mono, di and tri nucleosome-bound peaks at 200, 400 and 600bp. We calculate this per single cell, and quantify the approximate ratio of mononucleosomal to nucleosome-free fragments (stored as nucleosome_signal). Cells with lower nucleosome signal have a higher ratio of nucleosome-free fragments. - -```{r warning = FALSE} -seurat$nucleosome_group <- ifelse(seurat$nucleosome_signal > 1, "high NS", "low NS") -FragmentHistogram(seurat, group.by = 'nucleosome_group', cells = colnames(seurat[, seurat$sample == 'Control'])) -``` - -```{r } -VlnPlot( - object = seurat, - features = 'nucleosome_signal', - pt.size = 0 -) + scale_fill_cb_friendly() + -NoLegend() + xlab("") -``` - - - - -### Blacklist ratio - -tIt's he ratio of reads in genomic blacklist regions. The [ENCODE project](https://www.encodeproject.org/) has provided a list of [blacklist regions](https://github.com/Boyle-Lab/Blacklist), representing reads which are often associated with artefactual signal. Cells with a high proportion of reads mapping to these areas (compared to reads mapping to peaks) often represent technical artifacts and should be removed. ENCODE blacklist regions for human (hg19 and GRCh38), mouse (mm10), Drosophila (dm3), and C. elegans (ce10) are included in the Signac package. **Peaks overlapping with the balcklist regions were removed in the analysis, so we don't show blacklist fraction here**. - -Line is drawn at `r params$min_blacklistratio`. - -```{r} -VlnPlot( - object = seurat, - features = 'blacklist_fraction', - pt.size = 0 -) + scale_fill_cb_friendly() + - NoLegend() + - geom_hline(yintercept = params$min_blacklistratio) - -``` - - -## Normalization, linear dimensional reduction, and clustering - -* Normalization: Signac performs term frequency-inverse document frequency (TF-IDF) normalization. This is a two-step normalization procedure, that both normalizes across cells to correct for differences in cellular sequencing depth, and across peaks to give higher values to more rare peaks. - -* Feature selection: The low dynamic range of scATAC-seq data makes it challenging to perform variable feature selection, as we do for scRNA-seq. Instead, we can choose to use only the top n% of features (peaks) for dimensional reduction, or remove features present in less than n cells with the FindTopFeatures() function. Here, we will use all features, though we note that we see very similar results when using only a subset of features (try setting min.cutoff to ‘q75’ to use the top 25% all peaks), with faster runtimes. Features used for dimensional reduction are automatically set as VariableFeatures() for the Seurat object by this function. - -* Dimension reduction: We next run singular value decomposition (SVD) on the TD-IDF matrix, using the features (peaks) selected above. This returns a reduced dimension representation of the object (for users who are more familiar with scRNA-seq, you can think of this as analogous to the output of PCA). - -The combined steps of TF-IDF followed by SVD are known as latent semantic indexing (LSI), and were first introduced for the analysis of scATAC-seq data by [Cusanovich et al. 2015.](https://www.science.org/doi/10.1126/science.aax6234) - -The first LSI component often captures sequencing depth (techni ccal variation) rather than biological variation. If this is the case, the component should be removed from downstream analysis. We can assess the correlation between each LSI component and sequencing depth using the DepthCor() function (see below). -Here we see there is a very strong correlation between the first LSI component and the total number of counts for the cell, so we will perform downstream steps without this component. - -```{r results='asis'} -DepthCor(seurat, assay = 'MACS2peaks') -cat('\n\n') -``` - -## UMAP plots - -```{r results='asis'} -DimPlot(object = seurat, group.by = 'sample', reduction = "umapATAC") + - scale_color_cb_friendly() - -cat('\n\n') -``` - - - - -# Conclusion - -We will apply the following filters to **keep the high-quality cells**. For most of the criteria below, we don't have an upper limit, as we have removed doublets after demultiplexing. - -**RNA** - -* Number of UMIs (nCount_RNA) > `r params$minUMIs` -* Number of detected genes (nFeature_RNA) > `r params$minGenes` -* mitochondrial percentages (nFeature_RNA) < `r params$maxMitoPer` - -We don't apply any filter for complexity, as all the cells have relatively high complexity (> 0.85). - -**ADT** - -* Number of UMIs (nCount_ADT) > `r params$min_nCount_ADT` -* Number of detected proteins (nFeature_ADT) < `r params$max_nFeature_ADT` and > `r params$min_nFeature_ADT` - -**ATAC** - -* Total number of fragments in peaks (atac_peak_region_fragments) > `r params$min_PRF` -* Percentage of fragments in peaks (pct_reads_in_peaks) > `r params$min_FRiP` -* TSS enrichment (TSS.enrichment) > `r params$min_TSS` -* Nucleosome signal (nucleosome_signal) < `r params$max_NS` -* Blacklisted regions ratio (blacklist_fraction) < `r params$max_blacklistratio` - - -Number of cells before filtering: - -```{r} -table(seurat$sample) %>% as.data.frame() %>% set_colnames(c('sample', '#Cells')) %>% kable() %>% kable_styling() -``` - - -```{r} -# Please select your cutoffs -rna_keep <- seurat@meta.data[(seurat@meta.data$nCount_RNA > params$minUMIs & - seurat@meta.data$nFeature_RNA > params$minGenes & - seurat@meta.data$percent_mito < params$maxMitoPer), ] -rna_filt <- seurat@meta.data[rownames(seurat@meta.data) %!in% rownames(rna_keep),] - -adt_keep <- seurat@meta.data[(seurat@meta.data$nCount_ADT > params$min_nCount_ADT & - seurat@meta.data$nFeature_ADT > params$min_nFeature_ADT & - seurat@meta.data$nFeature_ADT < params$max_nFeature_ADT), ] - -adt_filt <- seurat@meta.data[rownames(seurat@meta.data) %!in% rownames(adt_keep),] - -atac_keep <- seurat@meta.data[(seurat@meta.data$atac_peak_region_fragments > params$min_PRF & - seurat@meta.data$pct_reads_in_peaks > params$min_FRiP & - seurat@meta.data$TSS.enrichment > params$min_TSS & - seurat@meta.data$nucleosome_signal < params$max_NS & - seurat@meta.data$blacklist_fraction < params$max_blacklistratio), ] - -atac_filt <- seurat@meta.data[rownames(seurat@meta.data) %!in% rownames(atac_keep),] - -library(ggvenn) -x = list( - RNA = rownames(rna_filt), - ADT = rownames(adt_filt), - ATAC = rownames(atac_filt) -) -ggvenn(x, names(x)) -``` - -```{r eval=FALSE} -# this is only run once before rendering -unfiltered_seurat <- seurat -seurat <- subset( - x = seurat, - subset = nCount_RNA > params$minUMIs & - nFeature_RNA > params$minGenes & - percent_mito < params$maxMitoPer & - nCount_ADT > params$min_nCount_ADT & - nFeature_ADT > params$min_nFeature_ADT & - nFeature_ADT < params$max_nFeature_ADT & - atac_peak_region_fragments > params$min_PRF & - TSS.enrichment > params$min_TSS & - nucleosome_signal < params$max_NS & - blacklist_fraction < params$max_blacklistratio - ) - -# reclustering -# RNA -DefaultAssay(seurat) <- 'RNA' -seurat <- SCTransform(seurat, conserve.memory=TRUE) -seurat <- RunPCA(object = seurat, verbose = T) -co <- 40 -seurat <- FindNeighbors(object = seurat, reduction = "pca", dims = 1:co) -seurat <- RunUMAP(object = seurat, dims=1:co ) - -# clustering by ADT data -seurat <- NormalizeData(seurat, assay = "ADT", normalization.method = "CLR", margin = 2) -seurat <- ScaleData(seurat, assay = "ADT") -seurat <- RunPCA(seurat, features = rownames(seurat@assays$ADT@counts), verbose = TRUE, assay= "ADT", reduction.name = "pcaADT", reduction.key = "pcaADT_") -seurat <- RunUMAP(seurat, dims = 1:20, reduction = "pcaADT", assay = "ADT", reduction.name = "umapADT", reduction.key = "umapADT_") - -# ATAC data normalization, dimension reduction, and clustering -seurat <- RunTFIDF(seurat, assay = "MACS2peaks") -seurat <- FindTopFeatures(seurat, min.cutoff = 'q0', assay = "MACS2peaks") -seurat <- RunSVD(seurat, assay = "MACS2peaks") -seurat <- RunUMAP(object = seurat, dims = 2:30, reduction = 'lsi', assay = "MACS2peaks", reduction.name = "umapATAC", reduction.key = "umapATAC_" ) - -qsave(seurat, 'data/tea_seurat_filtered_clustered.qs') -``` - -Number of cells **after filtering**: - -```{r} -# get the filtered seurat -stopifnot(file.exists('data/tea_seurat_filtered_clustered.qs')) -seurat <- qread('data/tea_seurat_filtered_clustered.qs') -table(seurat$sample) %>% as.data.frame() %>% set_colnames(c('sample', '#Cells')) %>% kable() %>% kable_styling() -``` - -**We take a look at the UMAPs after filtering out low quality cells for each modality.** - -```{r eval=FALSE, fig.width=12, fig.height=12} -plot_grid( - DimPlot(object = seurat, group.by = "sample", reduction = "umap") + ggtitle('RNA'), - DimPlot(object = seurat, group.by = "sample", reduction = "umapADT") + ggtitle('ADT'), - DimPlot(object = seurat, group.by = "sample", reduction = "umapATAC") + ggtitle('ATAC') -) -``` - - -# Session info - -```{r} -devtools::session_info() -``` - diff --git a/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/feature_reference.csv b/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/feature_reference.csv deleted file mode 100644 index 974c4ec..0000000 --- a/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/feature_reference.csv +++ /dev/null @@ -1,172 +0,0 @@ -id,name,read,pattern,sequence,feature_type -Hashtag1,Hashtag1,R2,5P(BC),GTCAACTCTTTAGCG,Multiplexing Capture -Hashtag2,Hashtag2,R2,5P(BC),TGATGGCCTATTGGG,Multiplexing Capture -Hashtag3,Hashtag3,R2,5P(BC),TTCCGCCTCTCTTTG,Multiplexing Capture -Hashtag4,Hashtag4,R2,5P(BC),AGTAAGTTCAGCGTA,Multiplexing Capture -Hashtag5,Hashtag5,R2,5P(BC),AAGTATCGTTTCGCA,Multiplexing Capture -Hashtag6,Hashtag6,R2,5P(BC),GGTTGCCAGATGTCA,Multiplexing Capture -Hashtag7,Hashtag7,R2,5P(BC),TGTCTTTCCTGCCAG,Multiplexing Capture -CD86_prot,CD86_prot,R2,5P(BC),GTCTTTGTCAGTGCA,Antibody Capture -CD274_or_B7-H1_PD-L1_prot,CD274_or_B7-H1_PD-L1_prot,R2,5P(BC),GTTGTCCGACAATAC,Antibody Capture -CD270_or_HVEM_TR2_prot,CD270_or_HVEM_TR2_prot,R2,5P(BC),TGATAGAAACAGACC,Antibody Capture -CD155_or_PVR_prot,CD155_or_PVR_prot,R2,5P(BC),ATCACATCGTTGCCA,Antibody Capture -CD112_or_Nectin-2_prot,CD112_or_Nectin-2_prot,R2,5P(BC),AACCTTCCGTCTAAG,Antibody Capture -CD47_prot,CD47_prot,R2,5P(BC),GCATTCTGTCACCTA,Antibody Capture -CD48_prot,CD48_prot,R2,5P(BC),CTACGACGTAGAAGA,Antibody Capture -CD40_prot,CD40_prot,R2,5P(BC),CTCAGATGGAGTATG,Antibody Capture -CD154_prot,CD154_prot,R2,5P(BC),GCTAGATAGATGCAA,Antibody Capture -CD52_prot,CD52_prot,R2,5P(BC),CTTTGTACGAGCAAA,Antibody Capture -CD3_prot,CD3_prot,R2,5P(BC),CTCATTGTAACTCCT,Antibody Capture -CD8_prot,CD8_prot,R2,5P(BC),GCGCAACTTGATGAT,Antibody Capture -CD56_or_NCAM_prot,CD56_or_NCAM_prot,R2,5P(BC),TCCTTTCCTGATAGG,Antibody Capture -CD19_prot,CD19_prot,R2,5P(BC),CTGGGCAATTACTCG,Antibody Capture -CD33_prot,CD33_prot,R2,5P(BC),TAACTCAGGGCCTAT,Antibody Capture -CD11c_prot,CD11c_prot,R2,5P(BC),TACGCCTATAACTTG,Antibody Capture -HLA-A_B_C_prot,HLA-A_B_C_prot,R2,5P(BC),TATGCGAGGCTTATC,Antibody Capture -CD45RA_prot,CD45RA_prot,R2,5P(BC),TCAATCCTTCCGCTT,Antibody Capture -CD123_prot,CD123_prot,R2,5P(BC),CTTCACTCTGTCAGG,Antibody Capture -CD7_prot,CD7_prot,R2,5P(BC),TGGATTCCCGGACTT,Antibody Capture -CD49f_prot,CD49f_prot,R2,5P(BC),TTCCGAGGATGATCT,Antibody Capture -CD194_or_CCR4_prot,CD194_or_CCR4_prot,R2,5P(BC),AGCTTACCTGCACGA,Antibody Capture -CD4_prot,CD4_prot,R2,5P(BC),TGTTCCCGCTCAACT,Antibody Capture -CD44_prot,CD44_prot,R2,5P(BC),TGGCTTCAGGTCCTA,Antibody Capture -CD14_prot,CD14_prot,R2,5P(BC),TCTCAGACCTCCGTA,Antibody Capture -CD16_prot,CD16_prot,R2,5P(BC),AAGTTCACTCTTTGC,Antibody Capture -CD25_prot,CD25_prot,R2,5P(BC),TTTGTCCTGTACGCC,Antibody Capture -CD45RO_prot,CD45RO_prot,R2,5P(BC),CTCCGAATCATGTTG,Antibody Capture -CD279_or_PD-1_prot,CD279_or_PD-1_prot,R2,5P(BC),ACAGCGCCGTATTTA,Antibody Capture -TIGIT_or_VSTM3_prot,TIGIT_or_VSTM3_prot,R2,5P(BC),TTGCTTACCGCCAGA,Antibody Capture -MouseIgG1_kisotypeCtrl_prot,MouseIgG1_kisotypeCtrl_prot,R2,5P(BC),GCCGGACGACATTAA,Antibody Capture -MouseIgG2a_kisotypeCtrl_prot,MouseIgG2a_kisotypeCtrl_prot,R2,5P(BC),CTCCTACCTAAACTG,Antibody Capture -MouseIgG2b_kisotypeCtrl_prot,MouseIgG2b_kisotypeCtrl_prot,R2,5P(BC),ATATGTATCACGCGA,Antibody Capture -RatIgG2b_kIsotypeCtrl_prot,RatIgG2b_kIsotypeCtrl_prot,R2,5P(BC),GATTCTTGACGACCT,Antibody Capture -CD20_prot,CD20_prot,R2,5P(BC),TTCTGGGTCCCTAGA,Antibody Capture -CD335_or_NKp46_prot,CD335_or_NKp46_prot,R2,5P(BC),ACAATTTGAACAGCG,Antibody Capture -CD31_prot,CD31_prot,R2,5P(BC),ACCTTTATGCCACGG,Antibody Capture -anti-HumanPodoplanin_prot,anti-HumanPodoplanin_prot,R2,5P(BC),GGTTACTCGTTGTGT,Antibody Capture -CD146_prot,CD146_prot,R2,5P(BC),CCTTGGATAACATCA,Antibody Capture -IgM_prot,IgM_prot,R2,5P(BC),TAGCGAGCCCGTATA,Antibody Capture -CD5_prot,CD5_prot,R2,5P(BC),CATTAACGGGATGCC,Antibody Capture -CD183_or_CXCR3_prot,CD183_or_CXCR3_prot,R2,5P(BC),GCGATGGTAGATTAT,Antibody Capture -CD195_or_CCR5_prot,CD195_or_CCR5_prot,R2,5P(BC),CCAAAGTAAGAGCCA,Antibody Capture -CD32_prot,CD32_prot,R2,5P(BC),GCTTCCGAATTACCG,Antibody Capture -CD196_or_CCR6_prot,CD196_or_CCR6_prot,R2,5P(BC),GATCCCTTTGTCACT,Antibody Capture -CD185_or_CXCR5_prot,CD185_or_CXCR5_prot,R2,5P(BC),AATTCAACCGTCGCC,Antibody Capture -HuCD103_or_IntegrinaE_prot,HuCD103_or_IntegrinaE_prot,R2,5P(BC),GACCTCATTGTGAAT,Antibody Capture -CD69_prot,CD69_prot,R2,5P(BC),GTCTCTTGGCTTAAA,Antibody Capture -CD62L_prot,CD62L_prot,R2,5P(BC),GTCCCTGCAACTTGA,Antibody Capture -CD161_prot,CD161_prot,R2,5P(BC),GTACGCAGTCCTTCT,Antibody Capture -CD152_or_CTLA-4_prot,CD152_or_CTLA-4_prot,R2,5P(BC),ATGGTTCACGTAATC,Antibody Capture -CD223_or_LAG-3_prot,CD223_or_LAG-3_prot,R2,5P(BC),CATTTGTCTGCCGGT,Antibody Capture -KLRG1_or_MAFA_prot,KLRG1_or_MAFA_prot,R2,5P(BC),CTTATTTCCTGCCCT,Antibody Capture -CD27_prot,CD27_prot,R2,5P(BC),GCACTCCTGCATGTA,Antibody Capture -CD107a_or_LAMP-1_prot,CD107a_or_LAMP-1_prot,R2,5P(BC),CAGCCCACTGCAATA,Antibody Capture -CD95_or_Fas_prot,CD95_or_Fas_prot,R2,5P(BC),CCAGCTCATTAGAGC,Antibody Capture -CD134_or_OX40_prot,CD134_or_OX40_prot,R2,5P(BC),AACCCACCGTTGTTA,Antibody Capture -HLA-DR_prot,HLA-DR_prot,R2,5P(BC),AATAGCGAGCAAGTA,Antibody Capture -CD1c_prot,CD1c_prot,R2,5P(BC),GAGCTACTTCACTCG,Antibody Capture -CD11b_prot,CD11b_prot,R2,5P(BC),GACAAGTGATCTGCA,Antibody Capture -CD64_prot,CD64_prot,R2,5P(BC),AAGTATGCCCTACGA,Antibody Capture -CD141_or_Thrombomodulin_prot,CD141_or_Thrombomodulin_prot,R2,5P(BC),GGATAACCGCGCTTT,Antibody Capture -HuCD314_or_NKG2D_prot,HuCD314_or_NKG2D_prot,R2,5P(BC),CGTGTTTGTTCCTCA,Antibody Capture -CD35_prot,CD35_prot,R2,5P(BC),ACTTCCGTCGATCTT,Antibody Capture -CD57Recombinant_prot,CD57Recombinant_prot,R2,5P(BC),AACTCCCTATGGAGG,Antibody Capture -CD272_or_BTLA_prot,CD272_or_BTLA_prot,R2,5P(BC),GTTATTGGACTAAGG,Antibody Capture -CD278_or_ICOS_prot,CD278_or_ICOS_prot,R2,5P(BC),CGCGCACCCATTAAA,Antibody Capture -CD275_or_B7-H2_B7-RP1_ICOSL_prot,CD275_or_B7-H2_B7-RP1_ICOSL_prot,R2,5P(BC),GTTAGTGTTAGCTTG,Antibody Capture -CD58_or_LFA-3_prot,CD58_or_LFA-3_prot,R2,5P(BC),GTTCCTATGGACGAC,Antibody Capture -CD39_prot,CD39_prot,R2,5P(BC),TTACCTGGTATCCGT,Antibody Capture -CX3CR1_prot,CX3CR1_prot,R2,5P(BC),AGTATCGTCTCTGGG,Antibody Capture -CD24_prot,CD24_prot,R2,5P(BC),AGATTCCTTCGTGTT,Antibody Capture -CD21_prot,CD21_prot,R2,5P(BC),AACCTAGTAGTTCGG,Antibody Capture -CD11a_prot,CD11a_prot,R2,5P(BC),TATATCCTTGTGAGC,Antibody Capture -CD79b_or_Igb_prot,CD79b_or_Igb_prot,R2,5P(BC),ATTCTTCAACCGAAG,Antibody Capture -CD244_or_2B4_prot,CD244_or_2B4_prot,R2,5P(BC),TCGCTTGGATGGTAG,Antibody Capture -CD169_or_Sialoadhesin_Siglec-1_prot,CD169_or_Sialoadhesin_Siglec-1_prot,R2,5P(BC),TACTCAGCGTGTTTG,Antibody Capture -integrinb7_prot,integrinb7_prot,R2,5P(BC),TCCTTGGATGTACCG,Antibody Capture -CD268_or_BAFF-R_prot,CD268_or_BAFF-R_prot,R2,5P(BC),CGAAGTCGATCCGTA,Antibody Capture -CD42b_prot,CD42b_prot,R2,5P(BC),TCCTAGTACCGAAGT,Antibody Capture -CD54_prot,CD54_prot,R2,5P(BC),CTGATAGACTTGAGT,Antibody Capture -CD62P_or_P-Selectin_prot,CD62P_or_P-Selectin_prot,R2,5P(BC),CCTTCCGTATCCCTT,Antibody Capture -CD119_or_IFN-gammaRachain_prot,CD119_or_IFN-gammaRachain_prot,R2,5P(BC),TGTGTATTCCCTTGT,Antibody Capture -TCRa_or_b_prot,TCRa_or_b_prot,R2,5P(BC),CGTAACGTAGAGCGA,Antibody Capture -RatIgG1_kisotypeCtrl_prot,RatIgG1_kisotypeCtrl_prot,R2,5P(BC),ATCAGATGCCCTCAT,Antibody Capture -RatIgG1_lambdaIsotypeCtrl_prot,RatIgG1_lambdaIsotypeCtrl_prot,R2,5P(BC),GGGAGCGATTCAACT,Antibody Capture -RatIgG2a_kIsotypeCtrl_prot,RatIgG2a_kIsotypeCtrl_prot,R2,5P(BC),AAGTCAGGTTCGTTT,Antibody Capture -RatIgG2c_kIsotypeCtrl_prot,RatIgG2c_kIsotypeCtrl_prot,R2,5P(BC),TCCAGGCTAGTCATT,Antibody Capture -ArmenianHamsterIgGIsotypeCtrl_prot,ArmenianHamsterIgGIsotypeCtrl_prot,R2,5P(BC),CCTGTCATTAAGACT,Antibody Capture -CD192_or_CCR2_prot,CD192_or_CCR2_prot,R2,5P(BC),GAGTTCCCTTACCTG,Antibody Capture -CD122_or_IL-2Rb_prot,CD122_or_IL-2Rb_prot,R2,5P(BC),TCATTTCCTCCGATT,Antibody Capture -CD267_or_TACI_prot,CD267_or_TACI_prot,R2,5P(BC),AGTGATGGAGCGAAC,Antibody Capture -FceRIa_prot,FceRIa_prot,R2,5P(BC),CTCGTTTCCGTATCG,Antibody Capture -CD41_prot,CD41_prot,R2,5P(BC),ACGTTGTGGCCTTGT,Antibody Capture -CD137_or_4-1BB_prot,CD137_or_4-1BB_prot,R2,5P(BC),CAGTAAGTTCGGGAC,Antibody Capture -CD43_prot,CD43_prot,R2,5P(BC),GATTAACCAGCTCAT,Antibody Capture -CD163_prot,CD163_prot,R2,5P(BC),GCTTCTCCTTCCTTA,Antibody Capture -CD83_prot,CD83_prot,R2,5P(BC),CCACTCATTTCCGGT,Antibody Capture -CD13_prot,CD13_prot,R2,5P(BC),TTTCAACGCCCTTTC,Antibody Capture -CD2_prot,CD2_prot,R2,5P(BC),TACGATTTGTCAGGG,Antibody Capture -CD226_or_DNAM-1_prot,CD226_or_DNAM-1_prot,R2,5P(BC),TCTCAGTGTTTGTGG,Antibody Capture -CD29_prot,CD29_prot,R2,5P(BC),GTATTCCCTCAGTCA,Antibody Capture -CD303_or_BDCA-2_prot,CD303_or_BDCA-2_prot,R2,5P(BC),GAGATGTCCGAATTT,Antibody Capture -CD49b_prot,CD49b_prot,R2,5P(BC),GCTTTCTTCAGTATG,Antibody Capture -CD61_prot,CD61_prot,R2,5P(BC),AGGTTGGAGTAGACT,Antibody Capture -CD81_or_TAPA-1_prot,CD81_or_TAPA-1_prot,R2,5P(BC),GTATCCTTCCTTGGC,Antibody Capture -CD55_prot,CD55_prot,R2,5P(BC),GCTCATTACCCATTA,Antibody Capture -IgD_prot,IgD_prot,R2,5P(BC),CAGTCTCCGTAGAGT,Antibody Capture -CD18_prot,CD18_prot,R2,5P(BC),TATTGGGACACTTCT,Antibody Capture -CD28_prot,CD28_prot,R2,5P(BC),TGAGAACGACCCTAA,Antibody Capture -CD38_prot,CD38_prot,R2,5P(BC),TGTACCCGCTTGTGA,Antibody Capture -CD127_or_IL-7Ra_prot,CD127_or_IL-7Ra_prot,R2,5P(BC),GTGTGTTGTCCTATG,Antibody Capture -CD45_prot,CD45_prot,R2,5P(BC),TGCAATTACCCGGAT,Antibody Capture -CD22_prot,CD22_prot,R2,5P(BC),GGGTTGTTGTCTTTG,Antibody Capture -CD71_prot,CD71_prot,R2,5P(BC),CCGTGTTCCTCATTA,Antibody Capture -CD26_prot,CD26_prot,R2,5P(BC),GGTGGCTAGATAATG,Antibody Capture -CD115_or_CSF-1R_prot,CD115_or_CSF-1R_prot,R2,5P(BC),AATCACGGTCCTTGT,Antibody Capture -CD63_prot,CD63_prot,R2,5P(BC),GAGATGTCTGCAACT,Antibody Capture -CD304_or_Neuropilin-1_prot,CD304_or_Neuropilin-1_prot,R2,5P(BC),GGACTAAGTTTCGTT,Antibody Capture -CD36_prot,CD36_prot,R2,5P(BC),TTCTTTGCCTTGCCA,Antibody Capture -CD172a_or_SIRPa_prot,CD172a_or_SIRPa_prot,R2,5P(BC),CGTGTTTAACTTGAG,Antibody Capture -CD72_prot,CD72_prot,R2,5P(BC),CAGTCGTGGTAGATA,Antibody Capture -CD158_or_KIR2DL1_or_S1_or_S3_or_S5_prot,CD158_or_KIR2DL1_or_S1_or_S3_or_S5_prot,R2,5P(BC),TATCAACCAACGCTT,Antibody Capture -CD93_prot,CD93_prot,R2,5P(BC),GCGCTACTTCCTTGA,Antibody Capture -CD200_or_OX2_prot,CD200_or_OX2_prot,R2,5P(BC),CACGTAGACCTTTGC,Antibody Capture -CD49a_prot,CD49a_prot,R2,5P(BC),ACTGATGGACTCAGA,Antibody Capture -CD49d_prot,CD49d_prot,R2,5P(BC),CCATTCAACTTCCGG,Antibody Capture -CD73_or_Ecto-5-nucleotidase_prot,CD73_or_Ecto-5-nucleotidase_prot,R2,5P(BC),CAGTTCCTCAGTTCG,Antibody Capture -CD9_prot,CD9_prot,R2,5P(BC),GAGTCACCAATCTGC,Antibody Capture -TCRVa7.2_prot,TCRVa7.2_prot,R2,5P(BC),TACGAGCAGTATTCA,Antibody Capture -TCRVdelta2_prot,TCRVdelta2_prot,R2,5P(BC),TCAGTCAGATGGTAT,Antibody Capture -CD354_or_TREM-1_prot,CD354_or_TREM-1_prot,R2,5P(BC),TAGCCGTTTCCTTTG,Antibody Capture -CD305_or_LAIR1_prot,CD305_or_LAIR1_prot,R2,5P(BC),ATTTCCATTCCCTGT,Antibody Capture -LOX-1_prot,LOX-1_prot,R2,5P(BC),ACCCTTTACCGAATA,Antibody Capture -CD158e1_or_KIR3DL1_NKB1_prot,CD158e1_or_KIR3DL1_NKB1_prot,R2,5P(BC),GGACGCTTTCCTTGA,Antibody Capture -CD109_prot,CD109_prot,R2,5P(BC),CACTTAACTCTGGGT,Antibody Capture -CD142_prot,CD142_prot,R2,5P(BC),CACTGCCGTCGATTA,Antibody Capture -CD319_or_CRACC_prot,CD319_or_CRACC_prot,R2,5P(BC),AGTATGCCATGTCTT,Antibody Capture -CD99_prot,CD99_prot,R2,5P(BC),ACCCGTCCCTAAGAA,Antibody Capture -CLEC12A_prot,CLEC12A_prot,R2,5P(BC),CATTAGAGTCTGCCA,Antibody Capture -CD151_or_PETA-3_prot,CD151_or_PETA-3_prot,R2,5P(BC),CTTACCTAGTCATTC,Antibody Capture -CD352_or_NTB-A_prot,CD352_or_NTB-A_prot,R2,5P(BC),AGTTTCCACTCAGGC,Antibody Capture -CLEC1B_or_CLEC2_prot,CLEC1B_or_CLEC2_prot,R2,5P(BC),TGCCAGTATCACGTA,Antibody Capture -CD94_prot,CD94_prot,R2,5P(BC),CTTTCCGGTCCTACA,Antibody Capture -IgE_prot,IgE_prot,R2,5P(BC),GGATGTACCGCGTAT,Antibody Capture -CD150_or_SLAM_prot,CD150_or_SLAM_prot,R2,5P(BC),GTCATTGTATGTCTG,Antibody Capture -CD162_prot,CD162_prot,R2,5P(BC),ATATGTCAGAGCACC,Antibody Capture -CD84_prot,CD84_prot,R2,5P(BC),CTCCCTAGTTCCTTT,Antibody Capture -Iglightchaink_prot,Iglightchaink_prot,R2,5P(BC),AGCTCAGCCAGTATG,Antibody Capture -CD85j_or_ILT2_prot,CD85j_or_ILT2_prot,R2,5P(BC),CCTTGTGAGGCTATG,Antibody Capture -CD23_prot,CD23_prot,R2,5P(BC),TCTGTATAACCGTCT,Antibody Capture -Iglightchainlambda_prot,Iglightchainlambda_prot,R2,5P(BC),CAGCCAGTAAGTCAC,Antibody Capture -CD328_or_Siglec-7_prot,CD328_or_Siglec-7_prot,R2,5P(BC),CTTAGCATTTCACTG,Antibody Capture -GPR56_prot,GPR56_prot,R2,5P(BC),GCCTAGTTTCCGTTT,Antibody Capture -CD82_prot,CD82_prot,R2,5P(BC),TCCCACTTCCGCTTT,Antibody Capture -NKp80_prot,NKp80_prot,R2,5P(BC),TATAGTTCCTCTGTG,Antibody Capture -CD131_prot,CD131_prot,R2,5P(BC),CTGCATGAGACCAAA,Antibody Capture -CD74_prot,CD74_prot,R2,5P(BC),CTGTAGCATTTCCCT,Antibody Capture -CD116_prot,CD116_prot,R2,5P(BC),ATGGACAGTTCGTGT,Antibody Capture -CD37_prot,CD37_prot,R2,5P(BC),ACAGTCACTGGGCAA,Antibody Capture -CD101_or_BB27_prot,CD101_or_BB27_prot,R2,5P(BC),CTACTTCCCTGTCAA,Antibody Capture -HLA-DR_DP_DQ_prot,HLA-DR_DP_DQ_prot,R2,5P(BC),AGCTACGAGCAGTAG,Antibody Capture -CD88_or_C5aR_prot,CD88_or_C5aR_prot,R2,5P(BC),GCCGCATGAGAAACA,Antibody Capture -CD197_or_CCR7_prot,CD197_or_CCR7_prot,R2,5P(BC),AGTTCAGTCAACCGA,Antibody Capture \ No newline at end of file diff --git a/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/libraries.csv b/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/libraries.csv deleted file mode 100644 index c373175..0000000 --- a/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/libraries.csv +++ /dev/null @@ -1,4 +0,0 @@ -fastqs,sample,library_type -FASTQ_PATH_TO_GEX,Gene Expression -FASTQ_PATH_TO_ATAC,Chromatin Accessibility - diff --git a/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/multiconfig.csv b/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/multiconfig.csv deleted file mode 100644 index a742df0..0000000 --- a/inst/rmarkdown/templates/singlecell/skeleton/meta/TEAseq/multiconfig.csv +++ /dev/null @@ -1,22 +0,0 @@ -[gene-expression] -reference, /n/shared_db/GRCh38/uk/cellranger/7.0.0/7.0.0/refdata-gex-GRCh38-2020-A -chemistry,ARC-v1 - -[libraries] -fastq_id,fastqs,feature_types -20240417_GEX,FASTQ_PATH_TO_GEX,Gene Expression -20240417_ADT,FASTQ_PATH_TO_ADT,Antibody Capture -20240417_HTO,FASTQ_PATH_TO_HTO,Multiplexing Capture - -[feature] -reference,PATH_TO_feature_reference.csv - -[samples] -sample_id,cmo_ids -no_drug,Hashtag1 -Romidepsin,Hashtag2 -Bryostatin,Hashtag3 -AZD,Hashtag4 -RB,Hashtag5 -RA,Hashtag6 -PMAi,Hashtag7 diff --git a/inst/rmarkdown/templates/singlecell/skeleton/scripts/fix_filenames.R b/inst/rmarkdown/templates/singlecell/skeleton/scripts/fix_filenames.R deleted file mode 100644 index 5ef12c2..0000000 --- a/inst/rmarkdown/templates/singlecell/skeleton/scripts/fix_filenames.R +++ /dev/null @@ -1,40 +0,0 @@ -library(stringr) -fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq/", pattern = "_S[1-9]") - - -dir.create("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq_fixed") -sapply(fsq, function(fn){ - ending=str_extract(fn, "_S.*.gz") - fnnew=paste0("20240417_GEX",ending) - fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq",fn)), - new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq_fixed",fnnew))) -}) - - -fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq", pattern = "_S[1-9]") -dir.create("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq_fixed") -sapply(fsq, function(fn){ - ending=str_extract(fn, "_S.*.gz") - fnnew=paste0("20240417_ATAC",ending) - fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq",fn)), - new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq_fixed",fnnew))) -}) - - -fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq/", pattern = "_S[1-9]") -dir.create("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq_fixed") -sapply(fsq, function(fn){ - ending=str_extract(fn, "_S.*.gz") - fnnew=paste0("20240417_ADT",ending) - fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq",fn)), - new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq_fixed",fnnew))) -}) - -fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq/", pattern = "_S[1-9]") -dir.create("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq_fixed") -sapply(fsq, function(fn){ - ending=str_extract(fn, "_S.*.gz") - fnnew=paste0("20240417_HTO",ending) - fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq",fn)), - new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq_fixed",fnnew))) -}) diff --git a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch deleted file mode 100644 index 55d25bd..0000000 --- a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_adt_hto.sbatch +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -#SBATCH --job-name=CellrangerMulti # Job name -#SBATCH --partition=priority # Partition name -#SBATCH --time=1-23:59 # Runtime in D-HH:MM format -#SBATCH --nodes=1 # Number of nodes (keep at 1) -#SBATCH --ntasks=1 # Number of tasks per node (keep at 1) -#SBATCH --cpus-per-task=16 # CPU cores requested per task (change for threaded jobs) -#SBATCH --mem=128G # Memory needed per node (total) -#SBATCH --error=jobid_%j.err # File to which STDERR will be written, including job ID -#SBATCH --output=jobid_%j.out # File to which STDOUT will be written, including job ID -#SBATCH --mail-type=ALL # Type of email notification (BEGIN, END, FAIL, ALL) - -module load cellranger/7.0.0 - -localcores=$SLURM_CPUS_PER_TASK -localmem=$SLURM_MEM_PER_NODE - -#change CSV to full path -cellranger multi --id=AtheTeaSeqMulti --csv=meta/multiconfig_batch3.csv - - - diff --git a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch b/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch deleted file mode 100644 index 2af343c..0000000 --- a/inst/rmarkdown/templates/singlecell/skeleton/scripts/gtex_atac.sbatch +++ /dev/null @@ -1,24 +0,0 @@ -#!/bin/bash - -#SBATCH --job-name=Cellranger-Arc # Job name -#SBATCH --partition=priority # Partition name -#SBATCH --time=1-23:59 # Runtime in D-HH:MM format -#SBATCH --nodes=1 # Number of nodes (keep at 1) -#SBATCH --ntasks=1 # Number of tasks per node (keep at 1) -#SBATCH --cpus-per-task=16 # CPU cores requested per task (change for threaded jobs) -#SBATCH --mem=128G # Memory needed per node (total) -#SBATCH --error=jobid_%j.err # File to which STDERR will be written, including job ID -#SBATCH --output=jobid_%j.out # File to which STDOUT will be written, including job ID -#SBATCH --mail-type=ALL # Type of email notification (BEGIN, END, FAIL, ALL) - -module load cellranger-ARC/2.0.0 - -localcores=$SLURM_CPUS_PER_TASK -localmem=$SLURM_MEM_PER_NODE - -# Change libraries to full path -cellranger-arc count --id=cellranger-arc_output \ - --reference=/n/shared_db/GRCh38/uk/cellranger-ARC/2.0.0/2.0.0/refdata-cellranger-arc-GRCh38-2020-A-2.0.0 \ - --libraries=meta/libraries.csv \ - --localcores=16 \ - --localmem=128 From c3c1aa56d8776ae897a837b1fe5d5648aeb09373 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 15 May 2024 16:59:49 -0400 Subject: [PATCH 20/55] readme for teaseq --- .../templates/teaseq/skeleton/README.md | 34 +++++++++++++ .../teaseq/skeleton/scripts/fix_filenames.R | 48 +++++++++---------- ...gtex_adt_hto.sbatch => gex_adt_hto.sbatch} | 2 +- .../{gtex_atac.sbatch => gex_atac.sbatch} | 0 4 files changed, 59 insertions(+), 25 deletions(-) rename inst/rmarkdown/templates/teaseq/skeleton/scripts/{gtex_adt_hto.sbatch => gex_adt_hto.sbatch} (92%) rename inst/rmarkdown/templates/teaseq/skeleton/scripts/{gtex_atac.sbatch => gex_atac.sbatch} (100%) diff --git a/inst/rmarkdown/templates/teaseq/skeleton/README.md b/inst/rmarkdown/templates/teaseq/skeleton/README.md index 0a6a5b2..d158ad9 100644 --- a/inst/rmarkdown/templates/teaseq/skeleton/README.md +++ b/inst/rmarkdown/templates/teaseq/skeleton/README.md @@ -1,5 +1,39 @@ # Tipical steps for scRNAseq downstream analysis +# Set up + +This template assumes there is 4 folders with FASTQ files. + +- GEX +- ATAC +- ADT +- HTO + +**NOTE** use absolute paths inside all these files to avoid errors. + +## Check file names + +FASTQ files should all start with the same sufix, cellrange will identify that as FASTQ from same samples. You may not have that, and you need to fix the filenames to looks like: + +`20240417_GEX1_6_AT12013_S41_L001_R2_001.fastq.gz` -> `20240417_GEX1_AT12013_S41_L001_R2_001.fastq.gz` + +The R scripts `scripts/fix_filenames.R` has some code to do that. + +## Analysis of scRNAseq and scATACseq + +`cellrange-arc` is used for this analysis. It needs a `libraries.csv` file. In the `meta` folder you will find a template. It needs the FASTQ_PATH to scRNAseq and scATACseq with filenames corrected. + +`scripts/gex_atac.sbatch` is an example of how to run this. It is assuming using HMS O2 cluster. + +## Analysis of scADT with Hashing + +`cellranger multi` is used for this analysis. It needs two config files: + +- `feature_reference.csv` is a list of barcodes used to identify the proteins and the hashing. The example file in `meta` folder shows how to add 7 different hashing barcodes. +- `multiconfig.csv` has three sections, to point to reference genome, input files (scRNAseq, scADT, HTO), the `feature_reference.csv` and samples. The example file in `meta` folder shows an example with 7 hasjing barcodes. + +`scripts/gex_adt_hto.sbatch` is an example of how to run this. It is assuming using HMS O2 cluster. + # DropBox - In `reports/QC` diff --git a/inst/rmarkdown/templates/teaseq/skeleton/scripts/fix_filenames.R b/inst/rmarkdown/templates/teaseq/skeleton/scripts/fix_filenames.R index 5ef12c2..4d3727a 100644 --- a/inst/rmarkdown/templates/teaseq/skeleton/scripts/fix_filenames.R +++ b/inst/rmarkdown/templates/teaseq/skeleton/scripts/fix_filenames.R @@ -1,40 +1,40 @@ library(stringr) -fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq/", pattern = "_S[1-9]") +fsq=list.files("data/GEX_fastq/", pattern = "_S[1-9]") -dir.create("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq_fixed") +dir.create("data/GEX_fastq_fixed") sapply(fsq, function(fn){ - ending=str_extract(fn, "_S.*.gz") - fnnew=paste0("20240417_GEX",ending) - fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq",fn)), - new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_GEX_AT12013_fastq_fixed",fnnew))) + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("GEX",ending) + fs::link_create(path = fs::path_abs(file.path("data/GEX_fastq",fn)), + new_path = fs::path_abs(file.path("data/GEX_fastq_fixed",fnnew))) }) -fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq", pattern = "_S[1-9]") -dir.create("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq_fixed") +fsq=list.files("data/ATAC_fastq", pattern = "_S[1-9]") +dir.create("data/ATAC_fastq_fixed") sapply(fsq, function(fn){ - ending=str_extract(fn, "_S.*.gz") - fnnew=paste0("20240417_ATAC",ending) - fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq",fn)), - new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ATAC_AT12013_fastq_fixed",fnnew))) + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("ATAC",ending) + fs::link_create(path = fs::path_abs(file.path("data/ATAC_fastq",fn)), + new_path = fs::path_abs(file.path("data/ATAC_fastq_fixed",fnnew))) }) -fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq/", pattern = "_S[1-9]") -dir.create("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq_fixed") +fsq=list.files("data/ADT_fastq/", pattern = "_S[1-9]") +dir.create("data/ADT_fastq_fixed") sapply(fsq, function(fn){ - ending=str_extract(fn, "_S.*.gz") - fnnew=paste0("20240417_ADT",ending) - fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq",fn)), - new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_ADT_AT12013_fastq_fixed",fnnew))) + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("ADT",ending) + fs::link_create(path = fs::path_abs(file.path("data/ADT_fastq",fn)), + new_path = fs::path_abs(file.path("data/ADT_fastq_fixed",fnnew))) }) -fsq=list.files("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq/", pattern = "_S[1-9]") -dir.create("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq_fixed") +fsq=list.files("data/HTO_fastq/", pattern = "_S[1-9]") +dir.create("data/HTO_fastq_fixed") sapply(fsq, function(fn){ - ending=str_extract(fn, "_S.*.gz") - fnnew=paste0("20240417_HTO",ending) - fs::link_create(path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq",fn)), - new_path = fs::path_abs(file.path("data/Athe_TEA_Seq_3rdbatch/240417_HTO_AT12013_fastq_fixed",fnnew))) + ending=str_extract(fn, "_S.*.gz") + fnnew=paste0("HTO",ending) + fs::link_create(path = fs::path_abs(file.path("data/HTO_fastq",fn)), + new_path = fs::path_abs(file.path("data/HTO_fastq_fixed",fnnew))) }) diff --git a/inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_adt_hto.sbatch b/inst/rmarkdown/templates/teaseq/skeleton/scripts/gex_adt_hto.sbatch similarity index 92% rename from inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_adt_hto.sbatch rename to inst/rmarkdown/templates/teaseq/skeleton/scripts/gex_adt_hto.sbatch index 55d25bd..cd86c5b 100644 --- a/inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_adt_hto.sbatch +++ b/inst/rmarkdown/templates/teaseq/skeleton/scripts/gex_adt_hto.sbatch @@ -17,7 +17,7 @@ localcores=$SLURM_CPUS_PER_TASK localmem=$SLURM_MEM_PER_NODE #change CSV to full path -cellranger multi --id=AtheTeaSeqMulti --csv=meta/multiconfig_batch3.csv +cellranger multi --id=AtheTeaSeqMulti --csv=meta/multiconfig.csv diff --git a/inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_atac.sbatch b/inst/rmarkdown/templates/teaseq/skeleton/scripts/gex_atac.sbatch similarity index 100% rename from inst/rmarkdown/templates/teaseq/skeleton/scripts/gtex_atac.sbatch rename to inst/rmarkdown/templates/teaseq/skeleton/scripts/gex_atac.sbatch From 13428b993b864c867f5277b71f9affa7cd384369 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 15 May 2024 17:02:09 -0400 Subject: [PATCH 21/55] adding QC Rmd in readme --- inst/rmarkdown/templates/teaseq/skeleton/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/inst/rmarkdown/templates/teaseq/skeleton/README.md b/inst/rmarkdown/templates/teaseq/skeleton/README.md index d158ad9..de1ce6c 100644 --- a/inst/rmarkdown/templates/teaseq/skeleton/README.md +++ b/inst/rmarkdown/templates/teaseq/skeleton/README.md @@ -34,6 +34,15 @@ The R scripts `scripts/fix_filenames.R` has some code to do that. `scripts/gex_adt_hto.sbatch` is an example of how to run this. It is assuming using HMS O2 cluster. +# QC analysis + +The file `QC-01-load_data.R` helps loading the output of `cellranger` and creating the right object. It stores R objects in `.qs` format under a `data` folder. + +The file `QC-02-run_analysis.R` helps analyzing the object for the three type of data. + +The file `QC.Rmd` helps visualizing the analyzed data. It stores R objects in `.qs` format under a `data` folder. + + # DropBox - In `reports/QC` From 646ed1a6df32e8bab7483f3173f46d32627986a9 Mon Sep 17 00:00:00 2001 From: Alex Bartlett Date: Thu, 16 May 2024 16:24:15 -0400 Subject: [PATCH 22/55] add cosmx "template" --- .gitignore | 1 + .../templates/cosmx/skeleton/QC/QC.Rmd | 362 ++++++++++++++++++ .../cosmx/skeleton/QC/run_markdown.R | 14 + .../templates/cosmx/skeleton/information.R | 6 + inst/rmarkdown/templates/cosmx/template.yml | 3 + 5 files changed, 386 insertions(+) create mode 100644 inst/rmarkdown/templates/cosmx/skeleton/QC/QC.Rmd create mode 100644 inst/rmarkdown/templates/cosmx/skeleton/QC/run_markdown.R create mode 100644 inst/rmarkdown/templates/cosmx/skeleton/information.R create mode 100644 inst/rmarkdown/templates/cosmx/template.yml diff --git a/.gitignore b/.gitignore index 80e3c2f..607f2ff 100644 --- a/.gitignore +++ b/.gitignore @@ -10,3 +10,4 @@ inst/doc docs /doc/ /Meta/ +.DS* \ No newline at end of file diff --git a/inst/rmarkdown/templates/cosmx/skeleton/QC/QC.Rmd b/inst/rmarkdown/templates/cosmx/skeleton/QC/QC.Rmd new file mode 100644 index 0000000..556b0e3 --- /dev/null +++ b/inst/rmarkdown/templates/cosmx/skeleton/QC/QC.Rmd @@ -0,0 +1,362 @@ +--- +title: "QC" +author: "Harvard Chan Bioinformatics Core" +date: "`r Sys.Date()`" +output: + html_document: + code_folding: hide + df_print: paged + highlights: pygments + number_sections: false + self_contained: true + theme: default + toc: true + toc_float: + collapsed: true + smooth_scroll: true +editor_options: + chunk_output_type: console +params: + project_file: ./reports/information.R + seurat_fn: ./data/CTCL_PAT8_DEV3_LVL7.RDS + results_dir: ./results + min_transcripts: 100 + min_genes: 20 + min_novelty: .7 + umap_dim: approximateumap_8c6f278e.b9f4.4535.aeca.8955c1dff614_1 + image_fn: ./reports/LVL7.png +--- + +```{r load_params, echo = F} +source(params$project_file) +``` + +```{r load_libraries, cache = FALSE, message = FALSE, warning=FALSE, echo=FALSE,} + +library(tidyverse) +library(Seurat) +library(bcbioR) +library(ggprism) +library(knitr) +library(tools) +library(qs) + +colors=cb_friendly_cols(1:15) +ggplot2::theme_set(theme_prism(base_size = 14)) +opts_chunk[["set"]]( + cache = F, + cache.lazy = FALSE, + dev = c("png", "pdf"), + error = TRUE, + highlight = TRUE, + message = FALSE, + prompt = FALSE, + tidy = FALSE, + warning = FALSE, + echo = T, + fig.height = 4) + +# set seed for reproducibility +set.seed(1234567890L) +``` + +```{r sanitize_datatable} +sanitize_datatable = function(df, ...) { + # remove dashes which cause wrapping + DT::datatable(df, ..., rownames=gsub("-", "_", rownames(df)), + colnames=gsub("-", "_", colnames(df)), + filter = 'top') +} +``` + +# Overview + +- Project: `r project` +- PI: `r PI` +- Analyst: `r analyst` +- Experiment: `r experiment` +- Aim: `r aim` +- Sample: `r params$seurat_fn` + +```{r read rds} + +seurat_qs_fn <- paste0(file_path_sans_ext(params$seurat_fn), '.qs') + +if (!file.exists(seurat_qs_fn)){ + seurat <- readRDS(params$seurat_fn) + qsave(seurat, seurat_qs_fn, preset = 'fast') +} else { + seurat <- qread(seurat_qs_fn) +} + +centroids <- data.frame(x = seurat$x_slide_mm, y = seurat$y_slide_mm, cell = seurat$cell_id) +cents <- CreateCentroids(centroids) +coords <- CreateFOV(coords = list(centroids = cents), type = "centroids") +# segs <- CreateSegmentation(centroids) +# segsFOV <- CreateFOV(segs, type = 'segmentation') +# seurat[['segsFOV']] <- segsFOV +``` + +```{r plot tissue} + +ggplot(seurat@meta.data, aes(y = x_slide_mm, x = y_slide_mm)) + + geom_point(alpha = 0.05, size = 0.01) + facet_wrap(~Run_Tissue_name) + coord_equal() + + labs(title = "Cell coordinates in XY space") + + +``` +

+ +![](`r params$image_fn`){width=50%} + +
+ +```{r check segmentation} + +# seg.crop <- Crop(coords, x = c(2, 2.1), y = c(3, 3.1)) +# +# ImageDimPlot(seurat_filtered, axes = TRUE, crop = TRUE, combine = TRUE) + xlim(c(3, 3.1)) + ylim(c(2, 2.1)) + +``` + +# QC Plots {.tabset} + +## nGenes + +```{r plot n_genes hist} +ggplot(data = seurat[[]], aes(x = nFeature_RNA)) + geom_histogram(binwidth = 50) + + geom_vline(xintercept = params$min_genes, col = "red", linetype = "dashed") + + xlab("nGenes") +``` + +## nUMIs + +```{r plot n_umis hist} +ggplot(data = seurat[[]], aes(x = nCount_RNA)) + geom_histogram(binwidth = 50) + + geom_vline(xintercept = params$min_transcripts, col = "red", linetype = "dashed") + + xlab("nUMIs") +``` + +# Filtering Low-Quality Cells + +We discard cells that have less than `r params$min_genes` features and genes present in less than 3 cells. Additionally, we apply the following AtoMx QC flags to select/filter cells: + +qcFlagsCellComplex = Pass. Filtering for complexity (nCount_RNA / nFeature_RNA) qcFlagsCellArea = Pass. Cell areas flagged as outliers by Grubbs test + +We don’t use the qcFlagsFOV flag as it was discarding too many of the FOVs in a one contiguous cluster near the edge of the device. + + + +```{r plot n_umis ranked} +nUMIs_df <- seurat[[]] %>% + as.data.frame() %>% + rownames_to_column(var = "barcode") %>% + dplyr::select(c(barcode, nCount_RNA)) %>% + dplyr::arrange(-nCount_RNA) + +nUMIs_df$ranked_bc <- as.integer(rownames(nUMIs_df)) + +# ggplot() + geom_line(data = nUMIs_df, aes(x = ranked_bc, y = nCount_RNA), col = "red") + +# scale_y_continuous(trans = "log10") + +# geom_hline(yintercept = params$min_transcripts, linetype = "dashed") + +# ylab("nUMIs") + xlab("barcode") + +``` + +```{r plot novelty} +seurat$novelty <- log10(seurat@meta.data$nFeature_RNA) / log10(seurat@meta.data$nCount_RNA) + +novelty_df <- seurat[[]] %>% + as.data.frame() %>% + rownames_to_column(var = "barcode") %>% + dplyr::select(c(barcode, novelty)) %>% + dplyr::arrange(-novelty) + +novelty_df$ranked_bc <- as.integer(rownames(novelty_df)) + +ggplot() + geom_line(data = novelty_df, aes(x = ranked_bc, y = novelty), col = "red") + + scale_y_continuous(trans = "log10") + + geom_hline(yintercept = params$min_novelty, linetype = "dashed") + + ylab("novelty") + xlab("barcode") + +``` + + +```{r qc filtering} +counts <- LayerData(seurat, layer = "counts", assay = "RNA") + +seurat_filtered <- CreateSeuratObject(counts = counts, meta.data = seurat@meta.data, min.cells = 3, + min.features = params$min_genes) + +selected_cells <- seurat_filtered[[]] %>% + as.data.frame() %>% + dplyr::filter(qcFlagsCellComplex == "Pass", + qcFlagsCellArea == "Pass", + novelty > params$min_novelty + # qcFlagsFOV == "Pass" + ) %>% + pull(cell_id) + +seurat_filtered <- subset(seurat_filtered, cells = selected_cells) + +n_before <- nrow(seurat[[]]) +n_after <- nrow(seurat_filtered[[]]) +``` + +There were `r n_before` cells before filtering and `r n_after` afterwards, for a total of +`r n_after/n_before * 100`% remaining + +```{r plot pre/post qc} + +pre <- ImageDimPlot(seurat) + NoLegend() + ggtitle("Pre-QC") +seurat_filtered[["FOV"]] <- subset(coords, cell = Cells(seurat_filtered)) +post <- ImageDimPlot(seurat_filtered) + NoLegend() + ggtitle("Post-QC") + +discarded_cells <- colnames(seurat)[!colnames(seurat) %in% colnames(seurat_filtered)] +seurat$selected <- seurat[[]] %>% + as.data.frame() %>% + dplyr::mutate( + selected = case_when( + cell_id %in% discarded_cells ~ "discarded", + TRUE ~ "selected") + ) %>% + pull(selected) + +seurat$selected <- factor(seurat$selected, levels = c("selected", "discarded")) +discarded <- ImageDimPlot(seurat, group.by = "selected") + NoLegend() + ggtitle("Blue - discarded") +print(pre + post + discarded) + +``` + +# Processing + +```{r processing} + +processed_seurat_fn <- paste0(file_path_sans_ext(params$seurat_fn), '_processed.qs') + +if (!file.exists(processed_seurat_fn)) { + seurat_filtered <- SCTransform(seurat_filtered, assay = "RNA", clip.range = c(-10,10), verbose = FALSE) + seurat_filtered <- NormalizeData(seurat_filtered, assay = "RNA") + seurat_filtered <- RunPCA(seurat_filtered) + seurat_filtered <- FindNeighbors(seurat_filtered, dims = 1:30) + seurat_filtered <- RunUMAP(seurat_filtered, dims = 1:30) + seurat_filtered <- FindClusters(seurat_filtered, resolution = 0.1, verbose = FALSE) + qsave(seurat_filtered, processed_seurat_fn, preset = 'fast') +} else { + seurat_filtered <- qread(processed_seurat_fn) +} + +``` + +```{r plot umap before} +DimPlot(seurat, reduction = paste(params$umap_dim), + pt.size = 0.6) + labs(x = "umap_1", y = "umap_2", title = "Pre-QC") + +``` + +```{r plot umap after} +DimPlot(seurat_filtered, reduction = "umap", pt.size = 0.6) + + ggtitle("Post-QC") + +``` + +```{r plot image clusters} +ImageDimPlot(seurat_filtered, axes = TRUE, crop = TRUE, combine = TRUE) + +``` + +# Markers + +## Cell Type Markers {.tabset} + +```{r markers of interest, results = 'asis'} +markers_of_interest <- c('CD4', 'CD8A', 'CD8B', 'CD63', 'CD69', 'HBB') + +for (marker in markers_of_interest) { + cat("### ", marker, "\n") + + FeaturePlot(seurat_filtered, features = marker, max.cutoff = "q95", min.cutoff = "q05", + reduction = "umap", pt.size = 0.6, order = T) %>% + print() + + if(marker != 'HBA1/2'){ + p <- ImageFeaturePlot(seurat_filtered, features = marker, max.cutoff = "q95", + size = 1, crop = TRUE, combine = FALSE) + print(p) + cat('\n') + + } + +} +``` + +## Apoptosis Markers {.tabset} + +```{r apoptosis markers, results = 'asis'} +apoptosis_markers <- c('BCL2', 'BCL2L1', 'CASP3', 'CASP8') + +for (marker in apoptosis_markers) { + cat("### ", marker, "\n") + + FeaturePlot(seurat_filtered, features = marker, max.cutoff = "q95", min.cutoff = "q05", + reduction = "umap", pt.size = 0.6, order = T) %>% + print() + + p <- ImageFeaturePlot(seurat_filtered, features = marker, max.cutoff = "q95", + size = 1, crop = TRUE, combine = FALSE) + print(p) + + cat('\n') +} +``` + + +# Cell Type Identification + +## Azimuth + +```{r} +azimuth_seurat_fn <- paste0(file_path_sans_ext(params$seurat_fn), '_azimuth.qs') +if (!file.exists(azimuth_seurat_fn)) { + seurat_filtered_ann_pbmc <- RunAzimuth(seurat_filtered, assay = "RNA", reference = "pbmcref") + qsave(seurat_filtered_ann_pbmc, azimuth_seurat_fn, preset = 'fast') + +} else { + seurat_filtered_ann_pbmc <- qread(azimuth_seurat_fn) +} +DimPlot(seurat_filtered_ann_pbmc, group.by = 'predicted.celltype.l1') +ImageDimPlot(seurat_filtered_ann_pbmc, axes = TRUE, crop = TRUE, combine = TRUE, group.by = 'predicted.celltype.l1') + +``` + +## Seurat and GPT4 +```{r} +markers <- FindAllMarkers(seurat_filtered) +# +# markers %>% +# dplyr::filter(avg_log2FC > 0) %>% +# select(c("cluster", "gene")) %>% +# group_by(cluster) %>% +# slice(1:20) %>% +# summarise(gene = paste(gene, collapse = ", ")) + +markers %>% sanitize_datatable() +if(grepl('LVL8', params$seurat_fn)){ + cluster_ids <- data.frame(cluster = as.factor(c(0:7)), + cell_type = as.factor(c('B-cells', 'Monocytes', 'Fibroblasts', + 'Cytotoxic T-cells', 'Endothelial Cells', + 'Keratinocytes', 'Plasma Cells', 'Mast Cells'))) +} else if (grepl('LVL7', params$seurat_fn)){ + cluster_ids <- data.frame(cluster = as.factor(c(0:6)), + cell_type = as.factor(c('B-cells', 'Fibroblasts', 'Monocytes', + 'Cytotoxic T-cells', 'Endothelial Cells', + 'T-cells', 'Keratinocytes'))) +} + +seurat_filtered@meta.data <- left_join(seurat_filtered@meta.data, cluster_ids, by = c('seurat_clusters' = 'cluster')) +rownames(seurat_filtered@meta.data) <- seurat_filtered@meta.data$cell_id + +ImageDimPlot(seurat_filtered, group.by = 'cell_type', axes = TRUE, crop = TRUE) + + +``` \ No newline at end of file diff --git a/inst/rmarkdown/templates/cosmx/skeleton/QC/run_markdown.R b/inst/rmarkdown/templates/cosmx/skeleton/QC/run_markdown.R new file mode 100644 index 0000000..6c518b6 --- /dev/null +++ b/inst/rmarkdown/templates/cosmx/skeleton/QC/run_markdown.R @@ -0,0 +1,14 @@ +library(rmarkdown) + +rmarkdown::render("reports/QC.Rmd", + output_dir = "reports", + clean = TRUE, + output_format = "html_document", + output_file = "QC.html", + params = list( + seurat_fn = '../data/CTCL_PAT8_DEV3_LVL7.RDS', + project_file = './information.R', + umap_dim = 'approximateumap_8c6f278e.b9f4.4535.aeca.8955c1dff614_1', + image_fn = 'LVL7.png' + ) +) \ No newline at end of file diff --git a/inst/rmarkdown/templates/cosmx/skeleton/information.R b/inst/rmarkdown/templates/cosmx/skeleton/information.R new file mode 100644 index 0000000..6e15eef --- /dev/null +++ b/inst/rmarkdown/templates/cosmx/skeleton/information.R @@ -0,0 +1,6 @@ +# info params +project = "name_hbcXXXXX" +PI = 'person name' +experiment = 'short description' +aim = 'short description' +analyst = 'person in the core' diff --git a/inst/rmarkdown/templates/cosmx/template.yml b/inst/rmarkdown/templates/cosmx/template.yml new file mode 100644 index 0000000..12712ff --- /dev/null +++ b/inst/rmarkdown/templates/cosmx/template.yml @@ -0,0 +1,3 @@ +name: bcbio CosMx +description: Standard CoxMx down-stream analyses +create_dir: false From 15e3e16ac8dab7d5cfd289d78c3465015ed7b373 Mon Sep 17 00:00:00 2001 From: Alex Bartlett Date: Fri, 17 May 2024 11:38:15 -0400 Subject: [PATCH 23/55] clean up cosmx template --- .../templates/cosmx/skeleton/QC/QC.Rmd | 103 +++++------------- .../cosmx/skeleton/QC/run_markdown.R | 3 +- 2 files changed, 30 insertions(+), 76 deletions(-) diff --git a/inst/rmarkdown/templates/cosmx/skeleton/QC/QC.Rmd b/inst/rmarkdown/templates/cosmx/skeleton/QC/QC.Rmd index 556b0e3..4271822 100644 --- a/inst/rmarkdown/templates/cosmx/skeleton/QC/QC.Rmd +++ b/inst/rmarkdown/templates/cosmx/skeleton/QC/QC.Rmd @@ -24,7 +24,6 @@ params: min_genes: 20 min_novelty: .7 umap_dim: approximateumap_8c6f278e.b9f4.4535.aeca.8955c1dff614_1 - image_fn: ./reports/LVL7.png --- ```{r load_params, echo = F} @@ -80,6 +79,7 @@ sanitize_datatable = function(df, ...) { ```{r read rds} +# resave RDS as QS for faster reading/writing seurat_qs_fn <- paste0(file_path_sans_ext(params$seurat_fn), '.qs') if (!file.exists(seurat_qs_fn)){ @@ -92,9 +92,7 @@ if (!file.exists(seurat_qs_fn)){ centroids <- data.frame(x = seurat$x_slide_mm, y = seurat$y_slide_mm, cell = seurat$cell_id) cents <- CreateCentroids(centroids) coords <- CreateFOV(coords = list(centroids = cents), type = "centroids") -# segs <- CreateSegmentation(centroids) -# segsFOV <- CreateFOV(segs, type = 'segmentation') -# seurat[['segsFOV']] <- segsFOV + ``` ```{r plot tissue} @@ -104,19 +102,6 @@ ggplot(seurat@meta.data, aes(y = x_slide_mm, x = y_slide_mm)) + labs(title = "Cell coordinates in XY space") -``` -
- -![](`r params$image_fn`){width=50%} - -
- -```{r check segmentation} - -# seg.crop <- Crop(coords, x = c(2, 2.1), y = c(3, 3.1)) -# -# ImageDimPlot(seurat_filtered, axes = TRUE, crop = TRUE, combine = TRUE) + xlim(c(3, 3.1)) + ylim(c(2, 2.1)) - ``` # QC Plots {.tabset} @@ -137,16 +122,7 @@ ggplot(data = seurat[[]], aes(x = nCount_RNA)) + geom_histogram(binwidth = 50) + xlab("nUMIs") ``` -# Filtering Low-Quality Cells - -We discard cells that have less than `r params$min_genes` features and genes present in less than 3 cells. Additionally, we apply the following AtoMx QC flags to select/filter cells: - -qcFlagsCellComplex = Pass. Filtering for complexity (nCount_RNA / nFeature_RNA) qcFlagsCellArea = Pass. Cell areas flagged as outliers by Grubbs test - -We don’t use the qcFlagsFOV flag as it was discarding too many of the FOVs in a one contiguous cluster near the edge of the device. - - - +## nUMIs ranked ```{r plot n_umis ranked} nUMIs_df <- seurat[[]] %>% as.data.frame() %>% @@ -156,13 +132,9 @@ nUMIs_df <- seurat[[]] %>% nUMIs_df$ranked_bc <- as.integer(rownames(nUMIs_df)) -# ggplot() + geom_line(data = nUMIs_df, aes(x = ranked_bc, y = nCount_RNA), col = "red") + -# scale_y_continuous(trans = "log10") + -# geom_hline(yintercept = params$min_transcripts, linetype = "dashed") + -# ylab("nUMIs") + xlab("barcode") - ``` +## novelty ```{r plot novelty} seurat$novelty <- log10(seurat@meta.data$nFeature_RNA) / log10(seurat@meta.data$nCount_RNA) @@ -181,6 +153,12 @@ ggplot() + geom_line(data = novelty_df, aes(x = ranked_bc, y = novelty), col = " ``` +# Filtering Low-Quality Cells + +We discard cells that have less than `r params$min_genes` features and genes present in less than 3 cells. Additionally, we apply the following AtoMx QC flags to select/filter cells: + +qcFlagsCellComplex = Pass. Filtering for complexity (nCount_RNA / nFeature_RNA) qcFlagsCellArea = Pass. Cell areas flagged as outliers by Grubbs test + ```{r qc filtering} counts <- LayerData(seurat, layer = "counts", assay = "RNA") @@ -208,9 +186,9 @@ There were `r n_before` cells before filtering and `r n_after` afterwards, for a ```{r plot pre/post qc} -pre <- ImageDimPlot(seurat) + NoLegend() + ggtitle("Pre-QC") +pre <- ImageDimPlot(seurat) + NoLegend() + ggtitle("Pre-Filtering") seurat_filtered[["FOV"]] <- subset(coords, cell = Cells(seurat_filtered)) -post <- ImageDimPlot(seurat_filtered) + NoLegend() + ggtitle("Post-QC") +post <- ImageDimPlot(seurat_filtered) + NoLegend() + ggtitle("Post-Filtering") discarded_cells <- colnames(seurat)[!colnames(seurat) %in% colnames(seurat_filtered)] seurat$selected <- seurat[[]] %>% @@ -232,8 +210,8 @@ print(pre + post + discarded) ```{r processing} +# perform processing (one time). if already done previously, load from file processed_seurat_fn <- paste0(file_path_sans_ext(params$seurat_fn), '_processed.qs') - if (!file.exists(processed_seurat_fn)) { seurat_filtered <- SCTransform(seurat_filtered, assay = "RNA", clip.range = c(-10,10), verbose = FALSE) seurat_filtered <- NormalizeData(seurat_filtered, assay = "RNA") @@ -249,14 +227,16 @@ if (!file.exists(processed_seurat_fn)) { ``` ```{r plot umap before} + +# TODO find colname of pre-filtering umap data in seurat object, use as params$umap_dim at top of file DimPlot(seurat, reduction = paste(params$umap_dim), - pt.size = 0.6) + labs(x = "umap_1", y = "umap_2", title = "Pre-QC") + pt.size = 0.6) + labs(x = "umap_1", y = "umap_2", title = "Pre-Filtering") ``` ```{r plot umap after} DimPlot(seurat_filtered, reduction = "umap", pt.size = 0.6) + - ggtitle("Post-QC") + ggtitle("Post-Filtering") ``` @@ -270,6 +250,8 @@ ImageDimPlot(seurat_filtered, axes = TRUE, crop = TRUE, combine = TRUE) ## Cell Type Markers {.tabset} ```{r markers of interest, results = 'asis'} + +## TODO replace with markers relevant to your project markers_of_interest <- c('CD4', 'CD8A', 'CD8B', 'CD63', 'CD69', 'HBB') for (marker in markers_of_interest) { @@ -279,43 +261,19 @@ for (marker in markers_of_interest) { reduction = "umap", pt.size = 0.6, order = T) %>% print() - if(marker != 'HBA1/2'){ - p <- ImageFeaturePlot(seurat_filtered, features = marker, max.cutoff = "q95", - size = 1, crop = TRUE, combine = FALSE) - print(p) - cat('\n') - - } - -} -``` - -## Apoptosis Markers {.tabset} - -```{r apoptosis markers, results = 'asis'} -apoptosis_markers <- c('BCL2', 'BCL2L1', 'CASP3', 'CASP8') - -for (marker in apoptosis_markers) { - cat("### ", marker, "\n") - - FeaturePlot(seurat_filtered, features = marker, max.cutoff = "q95", min.cutoff = "q05", - reduction = "umap", pt.size = 0.6, order = T) %>% - print() - p <- ImageFeaturePlot(seurat_filtered, features = marker, max.cutoff = "q95", size = 1, crop = TRUE, combine = FALSE) print(p) - cat('\n') } ``` - # Cell Type Identification ## Azimuth ```{r} +# perform Azimuth cell type identification (once). if already done previously, load from file azimuth_seurat_fn <- paste0(file_path_sans_ext(params$seurat_fn), '_azimuth.qs') if (!file.exists(azimuth_seurat_fn)) { seurat_filtered_ann_pbmc <- RunAzimuth(seurat_filtered, assay = "RNA", reference = "pbmcref") @@ -332,7 +290,8 @@ ImageDimPlot(seurat_filtered_ann_pbmc, axes = TRUE, crop = TRUE, combine = TRUE, ## Seurat and GPT4 ```{r} markers <- FindAllMarkers(seurat_filtered) -# + +# TODO: uncomment this chunk to get markers for pasting into GPT4 # markers %>% # dplyr::filter(avg_log2FC > 0) %>% # select(c("cluster", "gene")) %>% @@ -341,17 +300,13 @@ markers <- FindAllMarkers(seurat_filtered) # summarise(gene = paste(gene, collapse = ", ")) markers %>% sanitize_datatable() -if(grepl('LVL8', params$seurat_fn)){ - cluster_ids <- data.frame(cluster = as.factor(c(0:7)), - cell_type = as.factor(c('B-cells', 'Monocytes', 'Fibroblasts', - 'Cytotoxic T-cells', 'Endothelial Cells', - 'Keratinocytes', 'Plasma Cells', 'Mast Cells'))) -} else if (grepl('LVL7', params$seurat_fn)){ - cluster_ids <- data.frame(cluster = as.factor(c(0:6)), - cell_type = as.factor(c('B-cells', 'Fibroblasts', 'Monocytes', - 'Cytotoxic T-cells', 'Endothelial Cells', - 'T-cells', 'Keratinocytes'))) -} + +## TODO replace with cell types identified by GPT4 for your markers +cluster_ids <- data.frame(cluster = as.factor(c(0:6)), + cell_type = as.factor(c('B-cells', 'Fibroblasts', 'Monocytes', + 'Cytotoxic T-cells', 'Endothelial Cells', + 'T-cells', 'Keratinocytes'))) + seurat_filtered@meta.data <- left_join(seurat_filtered@meta.data, cluster_ids, by = c('seurat_clusters' = 'cluster')) rownames(seurat_filtered@meta.data) <- seurat_filtered@meta.data$cell_id diff --git a/inst/rmarkdown/templates/cosmx/skeleton/QC/run_markdown.R b/inst/rmarkdown/templates/cosmx/skeleton/QC/run_markdown.R index 6c518b6..d454e45 100644 --- a/inst/rmarkdown/templates/cosmx/skeleton/QC/run_markdown.R +++ b/inst/rmarkdown/templates/cosmx/skeleton/QC/run_markdown.R @@ -8,7 +8,6 @@ rmarkdown::render("reports/QC.Rmd", params = list( seurat_fn = '../data/CTCL_PAT8_DEV3_LVL7.RDS', project_file = './information.R', - umap_dim = 'approximateumap_8c6f278e.b9f4.4535.aeca.8955c1dff614_1', - image_fn = 'LVL7.png' + umap_dim = 'approximateumap_8c6f278e.b9f4.4535.aeca.8955c1dff614_1' ) ) \ No newline at end of file From 7a92b7fba0f3efd561aca93b93c4c4c027f24459 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 20 May 2024 13:47:20 -0400 Subject: [PATCH 24/55] minor details in QC RNA and TEAseq --- NEWS.md | 1 + .../rnaseq/skeleton/QC/QC_nf-core.Rmd | 160 ++++++++++++------ .../rnaseq/skeleton/QC/params_qc_nf-core.R | 5 + .../templates/rnaseq/skeleton/README.md | 2 +- .../teaseq/skeleton/QC/QC-01-load_data.R | 9 + .../teaseq/skeleton/QC/QC-02-run_analysis.R | 11 +- 6 files changed, 132 insertions(+), 56 deletions(-) diff --git a/NEWS.md b/NEWS.md index a70cefe..da7aea4 100644 --- a/NEWS.md +++ b/NEWS.md @@ -6,6 +6,7 @@ * Add by sample plots in QC * Add function to check nfcore samplesheet * Add PCA with variance analysis +* Minor details fixed in QC RNAseq report # bcbioR 0.1.1 diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index e89e074..3930e40 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -24,13 +24,29 @@ params: ```{r source_params, echo = F} +# 1. set up factor_of_interest parameter from parameter above or manualy +# this is used to color plots, it needs to be part of the metadata +factor_of_interest=params$factor_of_interest +# 2. Set input files in this file source(params$params_file) +# 3. If you set up this file, project information will be printed below and +#. it can be reused for other Rmd files. source(params$project_file) ``` +# Overview + +- Project: `r project` +- PI: `r PI` +- Analyst: `r analyst` +- Experiment: `r experiment` +- Aim: `r aim` + + ```{r load_libraries, cache = FALSE, message = FALSE, warning=FALSE} library(tidyverse) library(knitr) +library(rtracklayer) library(DESeq2) library(DEGreport) library(ggrepel) @@ -107,14 +123,6 @@ sanitize_datatable = function(df, ...) { } ``` -# Overview - -- Project: `r project` -- PI: `r PI` -- Analyst: `r analyst` -- Experiment: `r experiment` -- Aim: `r aim` - # Samples and metadata @@ -122,27 +130,43 @@ sanitize_datatable = function(df, ...) { ```{r load_metadata} meta_df=read_csv(metadata_fn) %>% - arrange(.data[[params$factor_of_interest]]) %>% distinct(sample, .keep_all = T) %>% + arrange(.data[[factor_of_interest]]) %>% distinct(sample, .keep_all = T) %>% select(!matches("fastq"), !matches("strandness")) meta_df$sample <- make.names(meta_df$sample) order <- meta_df$sample -ggplot(meta_df, aes(.data[[params$factor_of_interest]], fill = .data[[params$factor_of_interest]])) + +# remove some columns +meta_df <- data.frame(meta_df[,!(colnames(meta_df) %in% c("fastq_1", "fastq_2", "strandedness"))]) + + +ggplot(meta_df, aes(.data[[factor_of_interest]], + fill = .data[[factor_of_interest]])) + geom_bar() + ylab("") + xlab("") + scale_fill_cb_friendly() ``` -```{r prepare metrics} +```{r} +# read counts from SE object +se <- readRDS(se_object) +raw_counts <- assays(se)[["counts"]] %>% round() %>% + as.matrix() +raw_counts=raw_counts[rowSums(raw_counts)!=0,] +``` +```{r prepare metrics} +# Get metrics from nf-core into bcbio like table +# many metrics are already in the Genereal Table of MultiQC, this reads the file metrics <- read_tsv(file.path(multiqc_data_dir, 'multiqc_general_stats.txt')) -metrics_qualimap <- read_tsv(file.path(multiqc_data_dir, 'mqc_qualimap_genomic_origin_1.txt')) +# we get some more metrics from Qualimap and rename columns +metrics_qualimap <- read_tsv(file.path(multiqc_data_dir, 'mqc_qualimap_genomic_origin_1.txt')) metrics <- metrics %>% full_join(metrics_qualimap) metrics <- metrics %>% clean_names() %>% dplyr::rename_with(~gsub('.*mqc_generalstats_', '', .)) +# This uses the fastqc metrics to get total reads total_reads <- metrics %>% dplyr::filter(grepl('_', sample)) %>% remove_empty(which = 'cols') %>% @@ -151,6 +175,7 @@ total_reads <- metrics %>% group_by(sample) %>% summarize(total_reads = sum(fastqc_raw_total_sequences)) +# This renames to user-friendly names the metrics columns metrics <- metrics %>% dplyr::filter(!grepl('_', sample)) %>% remove_empty(which = 'cols') %>% @@ -161,19 +186,44 @@ metrics <- metrics %>% mutate(intergenic_rate = intergenic/(star_uniquely_mapped * 2)) %>% mutate(x5_3_bias = qualimap_5_3_bias) -if ("custom_content_biotype_counts_percent_r_rna" %in% colnames(metrics)){ - metrics <- mutate(metrics, r_rna_rate = custom_content_biotype_counts_percent_r_rna) +# Sometimes we don't have rRNA due to mismatch annotation, We skip this if is the case +gtf=rtracklayer::import(gtf_fn) + +one=grep("gene_type", colnames(as.data.frame(gtf)), value = TRUE) +another=grep("gene_biotype", colnames(as.data.frame(gtf)), value = TRUE) +biotype=NULL +if(length(one)==1){ + biotype=one +}else if(length(another)==1){ + biotype=another }else{ - metrics[["r_rna_rate"]] <- NA + warning("No gene biotype founded") } +if (!is.null(biotype)){ + annotation=as.data.frame(gtf) %>% .[,c("gene_id", biotype)] + rRNA=grepl("rRNA|tRNA",annotation[[biotype]]) + genes=intersect(annotation[rRNA,"gene_id"],row.names(raw_counts)) + ratio=data.frame(sample=colnames(raw_counts), + r_and_t_rna_rate=colSums(raw_counts[genes,])/colSums(raw_counts)) + metrics = left_join(metrics, ratio, by="sample") +}else{ + metrics[["r_and_t_rna_rate"]] <- NA +} +# if ("custom_content_biotype_counts_percent_r_rna" %in% colnames(metrics)){ +# metrics <- mutate(metrics, r_rna_rate = custom_content_biotype_counts_percent_r_rna) +# }else{ +# metrics[["r_rna_rate"]] <- NA +# } +metrics=metrics[,c("sample","mapped_reads","exonic_rate","intronic_rate", + "total_reads", + "x5_3_bias", "r_and_t_rna_rate","intergenic_rate")] metrics$sample <- make.names(metrics$sample) metrics <- metrics %>% - full_join(meta_df , by = c("sample" = "sample")) %>% + full_join(meta_df , by = c("sample" = "sample")) %>% select(where(~!all(is.na(.)))) - ``` ```{r show-metadata} @@ -195,7 +245,7 @@ Here, we want to see consistency and a minimum of 20 million reads. metrics %>% ggplot(aes(x = factor(sample, level = order), y = total_reads, - fill = .data[[params$factor_of_interest]])) + + fill = .data[[factor_of_interest]])) + geom_bar(stat = "identity") + coord_flip() + scale_y_continuous(name = "million reads") + @@ -203,9 +253,9 @@ metrics %>% ggtitle("Total reads") metrics %>% - ggplot(aes(x = .data[[params$factor_of_interest]], + ggplot(aes(x = .data[[factor_of_interest]], y = total_reads, - color = .data[[params$factor_of_interest]])) + + color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + coord_flip() + scale_y_continuous(name = "million reads") + @@ -228,13 +278,13 @@ metrics$mapped_reads_pct <- round(metrics$mapped_reads/metrics$total_reads*100,1 metrics %>% ggplot(aes(x = factor(sample, level = order), y = mapped_reads_pct, - color = .data[[params$factor_of_interest]])) + + color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + coord_flip() + scale_color_cb_friendly() + ylim(0, 100) + - ggtitle("Mapping rate") + - geom_hline(yintercept=70, color = cb_friendly_cols('blue')) + ggtitle("Mapping rate") + + geom_hline(yintercept=70, color = "grey", size=2) ``` @@ -243,7 +293,6 @@ metrics %>% The number of genes represented in every sample is expected to be consistent and over 20K (blue line). ```{r calc_genes_detected} -se <- readRDS(se_object) genes_detected <- colSums(assays(se)[["counts"]] > 0) %>% enframe() sample_names <- metrics[,c("sample"), drop=F] genes_detected <- left_join(genes_detected, sample_names, by = c("name" = "sample")) @@ -259,19 +308,19 @@ metrics <- metrics %>% ```{r plot_genes_detected} ggplot(metrics,aes(x = factor(sample, level = order), - y = n_genes, fill = .data[[params$factor_of_interest]])) + + y = n_genes, fill = .data[[factor_of_interest]])) + geom_bar(stat = "identity") + coord_flip() + scale_fill_cb_friendly() + ggtitle("Number of genes") + ylab("Number of genes") + xlab("") + - geom_hline(yintercept=20000, color = cb_friendly_cols('blue')) + geom_hline(yintercept=20000, color = "grey", size=2) metrics %>% - ggplot(aes(x = .data[[params$factor_of_interest]], + ggplot(aes(x = .data[[factor_of_interest]], y = n_genes, - color = .data[[params$factor_of_interest]])) + + color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + coord_flip() + scale_y_continuous(name = "million reads") + @@ -290,7 +339,7 @@ This plot shows how complex the samples are. We expect samples with more reads t metrics %>% ggplot(aes(x = total_reads, y = n_genes, - color = .data[[params$factor_of_interest]])) + + color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + scale_x_log10() + scale_color_cb_friendly() + @@ -306,7 +355,7 @@ Here we are looking for consistency, and exonic mapping rates around 70% or 75% metrics %>% ggplot(aes(x = factor(sample, level = order), y = exonic_rate * 100, - color = .data[[params$factor_of_interest]])) + + color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + ylab("Exonic rate %") + ggtitle("Exonic mapping rate") + @@ -314,8 +363,7 @@ metrics %>% coord_flip() + xlab("") + ylim(c(0,100)) + - geom_hline(yintercept=70, color = cb_friendly_cols('blue')) + - geom_hline(yintercept=75, color = cb_friendly_cols('brown')) + geom_hline(yintercept=70, color = "grey", size=2) ``` ## Intronic mapping rate @@ -326,7 +374,7 @@ Here, we expect a low intronic mapping rate (≤ 15% - 20%) metrics %>% ggplot(aes(x = factor(sample, level = order), y = intronic_rate * 100, - color = .data[[params$factor_of_interest]])) + + color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + ylab("Intronic rate %") + ggtitle("Intronic mapping rate") + @@ -334,8 +382,7 @@ metrics %>% coord_flip() + xlab("") + ylim(c(0,100)) + - geom_hline(yintercept=20, color = cb_friendly_cols('blue')) + - geom_hline(yintercept=15, color = cb_friendly_cols('brown')) + geom_hline(yintercept=20, color = "grey", size=2) ``` ## Intergenic mapping rate @@ -346,13 +393,14 @@ Here, we expect a low intergenic mapping rate, which is true for all samples. metrics %>% ggplot(aes(x = factor(sample, level = order), y = intergenic_rate * 100, - color = .data[[params$factor_of_interest]])) + + color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + ylab("Intergenic rate %") + ggtitle("Intergenic mapping rate") + coord_flip() + xlab("") + scale_color_cb_friendly() + - ylim(c(0, 100)) + ylim(c(0, 100)) + + geom_hline(yintercept=0.2, color = "grey", size=2) ``` ## rRNA mapping rate @@ -361,17 +409,18 @@ Samples should have a ribosomal RNA (rRNA) "contamination" rate below 10% ```{r plot_rrna_mapping_rate} -rrna_ylim <- max(round(metrics$r_rna_rate*100, 2)) + 10 +rrna_ylim <- max(round(metrics$r_and_t_rna_rate*100, 2)) + 10 metrics %>% ggplot(aes(x = factor(sample, level = order), - y = r_rna_rate * 100, - color = .data[[params$factor_of_interest]])) + + y = r_and_t_rna_rate * 100, + color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + ylab("rRNA rate, %")+ ylim(0, rrna_ylim) + ggtitle("rRNA mapping rate") + coord_flip() + - scale_color_cb_friendly() + scale_color_cb_friendly() + + geom_hline(yintercept=0.2, color = "grey", size=2) ``` ## 5'->3' bias @@ -382,13 +431,13 @@ There should be little bias, i.e. the values should be close to 1, or at least c metrics %>% ggplot(aes(x = factor(sample, level = order), y = x5_3_bias, - color = .data[[params$factor_of_interest]])) + + color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + ggtitle("5'-3' bias") + coord_flip() + ylim(c(0.5,1.5)) + xlab("") + ylab("5'-3' bias") + scale_color_cb_friendly()+ - geom_hline(yintercept=1, color = cb_friendly_cols('blue')) + geom_hline(yintercept=1, color = "grey", size=2) ``` ## Counts per gene - all genes @@ -396,7 +445,7 @@ metrics %>% We expect consistency in the box plots here between the samples, i.e. the distribution of counts across the genes is similar ```{r plot_counts_per_gene} -metrics_small <- metrics %>% dplyr::select(sample, .data[[params$factor_of_interest]]) +metrics_small <- metrics %>% dplyr::select(sample, .data[[factor_of_interest]]) metrics_small <- left_join(sample_names, metrics_small) counts <- @@ -409,7 +458,7 @@ counts <- left_join(counts, metrics_small, by = c("name" = "sample")) ggplot(counts, aes(factor(name, level = order), log2(counts+1), - fill = .data[[params$factor_of_interest]])) + + fill = .data[[factor_of_interest]])) + geom_boxplot() + scale_fill_cb_friendly() + coord_flip() + xlab("") + @@ -428,11 +477,6 @@ Principal Component Analysis (PCA) is a statistical technique used to simplify h ```{r PCA1:5 summary, all, unlabeled, fig.width= 7, fig.height = 5} -raw_counts <- assays(se)[["counts"]] %>% round() %>% - as_tibble() %>% - filter(rowSums(.)!=0) %>% - as.matrix() - vst <- vst(raw_counts) @@ -440,18 +484,21 @@ coldat_for_pca <- as.data.frame(metrics) rownames(coldat_for_pca) <- coldat_for_pca$sample coldat_for_pca <- coldat_for_pca[colnames(raw_counts),] pca1 <- degPCA(vst, coldat_for_pca, - condition = params$factor_of_interest, data = T)[["plot"]] + condition = factor_of_interest, data = T)[["plot"]] pca2 <- degPCA(vst, coldat_for_pca, - condition = params$factor_of_interest, data = T, pc1="PC3", pc2="PC4")[["plot"]] + condition = factor_of_interest, data = T, pc1="PC3", pc2="PC4")[["plot"]] pca1 + scale_color_cb_friendly() pca2 + scale_color_cb_friendly() ``` +# Covariates analysis + +When there are multiple factors that can influence the results of a given experiment, it is useful to assess which of them is responsible for the most variance as determined by PCA. This method adapts the method described by Daily et al. for which they integrated a method to correlate covariates with principal components values to determine the importance of each factor. ```{r covariate-plot,fig.height=12, fig.width=10} ## Remove non-useful columns output by nf-core -coldat_2 <- data.frame(coldat_for_pca[,!(colnames(coldat_for_pca) %in% c("fastq_1", "fastq_2", "salmon_library_types", "salmon_compatible_fragment_ratio", "samtools_reads_mapped_percent", "samtools_reads_properly_paired_percent", "samtools_mapped_passed_pct", "strandedness"))]) +coldat_2 <- data.frame(coldat_for_pca[,!(colnames(coldat_for_pca) %in% c("fastq_1", "fastq_2", "salmon_library_types", "salmon_compatible_fragment_ratio", "samtools_reads_mapped_percent", "samtools_reads_properly_paired_percent", "samtools_mapped_passed_pct", "strandedness", "qualimap_5_3_bias"))]) # Remove missing data coldat_2 <- na.omit(coldat_2) @@ -459,6 +506,11 @@ degCovariates(vst, metadata = coldat_2) ``` ## Hierarchical clustering + +Inter-correlation analysis (ICA) is another way to look at how well samples +cluster by plotting the correlation between the expression profiles of the +samples. + ```{r clustering fig, fig.width = 10, fig.asp = .62} vst_cor <- cor(vst) @@ -466,7 +518,7 @@ vst_cor <- cor(vst) colma=meta_df %>% as.data.frame() rownames(colma) <- colma$sample colma <- colma[rownames(vst_cor), ] -colma <- colma %>% dplyr::select(.data[[params$factor_of_interest]]) +colma <- colma %>% dplyr::select(.data[[factor_of_interest]]) anno_colors=lapply(colnames(colma), function(c){ l.col=cb_friendly_pal('grey')(length(unique(colma[[c]]))) names(l.col)=unique(colma[[c]]) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R index e9eb536..bbcc10b 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R @@ -1,5 +1,10 @@ # info params +# This is the file used to run nf-core or compatible to that metadata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' +# This file is inside star_salmon/ folder se_object=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.rds') +# This folder is in the output directory multiqc_data_dir='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/multiqc-report-data/' +# This file is inside the genome folder in the output directory +gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/genome.filtered.gtf.gz' diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/README.md b/inst/rmarkdown/templates/rnaseq/skeleton/README.md index bdf1941..50f8f16 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/README.md +++ b/inst/rmarkdown/templates/rnaseq/skeleton/README.md @@ -22,7 +22,7 @@ You can add more columns to this file with more metadata, and use this file as t - Upload file to our `Datasets` in Seqera using the name of the project but starting with `nfcore-rnaseq` - Go to `Launchpad`, select `nf-core_rnaseq` pipeline, and select the previous created `Datasets` in the `input` parameter after clicking in `Browser` - Select an output directory with the same name used for the `Dataset` inside the `results` folder in S3 -- When pipeline is down, data will be copied to our on-premise HPC in the scratch system under `bcbio` folder +- When pipeline is down, data will be copied to our on-premise HPC in the scratch system under `scratch/groups/hsph/hbc/bcbio/` folder ## Downstream analysis diff --git a/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-01-load_data.R b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-01-load_data.R index 2e79160..ba68ffb 100644 --- a/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-01-load_data.R +++ b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-01-load_data.R @@ -1,3 +1,12 @@ +library(Seurat) +library(tidyverse) +library(Matrix) +library(data.table) +library(magrittr) +library(Signac) +library(EnsDb.Hsapiens.v86) +library(qs) +library(bcbioR) # prepare seurat object with all 3 modes # read cellranger-arc outs diff --git a/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-02-run_analysis.R b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-02-run_analysis.R index 997d00d..6c2e493 100644 --- a/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-02-run_analysis.R +++ b/inst/rmarkdown/templates/teaseq/skeleton/QC/QC-02-run_analysis.R @@ -1,4 +1,13 @@ - +library(Seurat) +library(tidyverse) +library(Matrix) +library(data.table) +library(magrittr) +library(Signac) +library(EnsDb.Hsapiens.v86) +library(qs) +library(bcbioR) +# Run analysis on multi-omic sc Data seurat <- qread("data/tea_seurat.qs") # keep singlet only From 0e9f2ab3e0098ff4eadb4d15ee8bb17f75c2d02f Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 20 May 2024 14:08:47 -0400 Subject: [PATCH 25/55] fix sample names with underscore_number --- inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index 3930e40..6f32e81 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -168,7 +168,7 @@ metrics <- metrics %>% # This uses the fastqc metrics to get total reads total_reads <- metrics %>% - dplyr::filter(grepl('_', sample)) %>% + dplyr::filter(!is.na(fastqc_raw_total_sequences)) %>% remove_empty(which = 'cols') %>% dplyr::rename(single_sample = sample) %>% mutate(sample = gsub('_[12]+$', '', single_sample)) %>% @@ -177,7 +177,7 @@ total_reads <- metrics %>% # This renames to user-friendly names the metrics columns metrics <- metrics %>% - dplyr::filter(!grepl('_', sample)) %>% + dplyr::filter(is.na(fastqc_raw_total_sequences)) %>% remove_empty(which = 'cols') %>% full_join(total_reads) %>% mutate(mapped_reads = samtools_reads_mapped) %>% From d23a36bd14d8f6ffd2774c738b27e982324b828d Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 20 May 2024 14:11:34 -0400 Subject: [PATCH 26/55] fix rRNA title --- inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index 6f32e81..1758a3c 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -403,7 +403,7 @@ metrics %>% geom_hline(yintercept=0.2, color = "grey", size=2) ``` -## rRNA mapping rate +## tRNA/rRNA mapping rate Samples should have a ribosomal RNA (rRNA) "contamination" rate below 10% @@ -415,9 +415,9 @@ metrics %>% y = r_and_t_rna_rate * 100, color = .data[[factor_of_interest]])) + geom_point(alpha = 0.5, size=4) + - ylab("rRNA rate, %")+ + ylab("tRNA/rRNA rate, %")+ ylim(0, rrna_ylim) + - ggtitle("rRNA mapping rate") + + ggtitle("tRNA/rRNA mapping rate") + coord_flip() + scale_color_cb_friendly() + geom_hline(yintercept=0.2, color = "grey", size=2) From 4a5c1357c5ed3ef4338da76c2424872b162a664d Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 20 May 2024 14:42:56 -0400 Subject: [PATCH 27/55] fix DE load data for metrics and sample names --- NEWS.md | 1 + .../templates/rnaseq/skeleton/DE/DEG.Rmd | 19 ++-- .../templates/rnaseq/skeleton/DE/load_data.R | 87 ++++++++++++------- .../templates/rnaseq/skeleton/DE/params_de.R | 8 +- .../rnaseq/skeleton/QC/params_qc_nf-core.R | 3 +- 5 files changed, 76 insertions(+), 42 deletions(-) diff --git a/NEWS.md b/NEWS.md index da7aea4..dc80256 100644 --- a/NEWS.md +++ b/NEWS.md @@ -7,6 +7,7 @@ * Add function to check nfcore samplesheet * Add PCA with variance analysis * Minor details fixed in QC RNAseq report +* Correct metric for rRNA and tRNA # bcbioR 0.1.1 diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index 7e50c0d..5507a22 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -22,7 +22,7 @@ params: column: sample_type subset_column: null subset_value: null - ruv: true + ruv: false params_file: params_de.R project_file: ../information.R functions_file: load_data.R @@ -30,10 +30,13 @@ params: --- ```{r load_params, cache = FALSE, message = FALSE, warning=FALSE} +# 1. Set up input files in this R file (params_de.R) source(params$params_file) +# 2. Set up project file (already done from QC probably) source(params$project_file) +# 3. Load custom functions to load data from coldata/metrics/counts source(params$functions_file) -# set this values if you are not using the parameters at the top +# IMPORTANT set these values if you are not using the parameters at the top column=params$column numerator=params$numerator denominator=params$denominator @@ -82,18 +85,20 @@ set.seed(1234567890L) ```{r load_data, message=F, warning=F} -# this code will load from bcbio or nf-core folder +# This code will load from bcbio or nf-core folder # NOTE make sure to set numerator and denominator coldata <- load_coldata(coldata_fn, column, numerator, denominator, subset_column, subset_value) coldata$sample=row.names(coldata) -metrics <- load_metrics(se_object, multiqc_data_dir) %>% - left_join(coldata, by = c('sample' = 'description')) %>% + +counts <- load_counts(counts_fn) +counts <- counts[,colnames(counts) %in% coldata$sample] + +metrics <- load_metrics(se_object, multiqc_data_dir,gtf_fn,counts) %>% + left_join(coldata, by = c('sample')) %>% as.data.frame() rownames(metrics) <- metrics$sample -counts <- load_counts(counts_fn) -counts <- counts[,colnames(counts) %in% coldata$description] # if the names don't match in order or string check files names and coldata information counts = counts[rownames(coldata)] stopifnot(all(names(counts) == rownames(coldata))) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R index 457a6e1..8bc1ec2 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R @@ -1,63 +1,80 @@ library(tidyverse) library(SummarizedExperiment) library(janitor) -load_metrics <- function(se_object, multiqc_data_dir){ +load_metrics <- function(se_object, multiqc_data_dir, gtf_fn, counts){ # bcbio input if (!is.na(se_object)){ se <- readRDS(se_object) - metrics <- metadata(se)$metrics %>% mutate(sample = toupper(sample)) %>% as.data.frame() + metrics <- metadata(se)$metrics %>% as.data.frame() # left_join(coldata %>% rownames_to_column('sample')) %>% column_to_rownames('sample') - } else { #nf-core input - metrics <- read_tsv(paste0(multiqc_data_dir, 'multiqc_general_stats.txt')) - metrics_qualimap <- read_tsv(paste0(multiqc_data_dir, 'mqc_qualimap_genomic_origin_1.txt')) + # Get metrics from nf-core into bcbio like table + # many metrics are already in the Genereal Table of MultiQC, this reads the file + metrics <- read_tsv(file.path(multiqc_data_dir, 'multiqc_general_stats.txt')) + # we get some more metrics from Qualimap and rename columns + metrics_qualimap <- read_tsv(file.path(multiqc_data_dir, 'mqc_qualimap_genomic_origin_1.txt')) metrics <- metrics %>% full_join(metrics_qualimap) metrics <- metrics %>% clean_names() %>% dplyr::rename_with(~gsub('.*mqc_generalstats_', '', .)) + # This uses the fastqc metrics to get total reads total_reads <- metrics %>% - filter(grepl('_', sample)) %>% + dplyr::filter(!is.na(fastqc_raw_total_sequences)) %>% remove_empty(which = 'cols') %>% dplyr::rename(single_sample = sample) %>% mutate(sample = gsub('_[12]+$', '', single_sample)) %>% group_by(sample) %>% summarize(total_reads = sum(fastqc_raw_total_sequences)) - if (!("custom_content_biotype_counts_percent_r_rna" %in% colnames(metrics))){ - metrics[["custom_content_biotype_counts_percent_r_rna"]] <- rep(0,nrow(metrics)) - } - # browser() - + # This renames to user-friendly names the metrics columns metrics <- metrics %>% - filter(!grepl('_[12]$', sample)) %>% + dplyr::filter(is.na(fastqc_raw_total_sequences)) %>% remove_empty(which = 'cols') %>% full_join(total_reads) %>% - dplyr::rename(mapped_reads = samtools_reads_mapped) %>% + mutate(mapped_reads = samtools_reads_mapped) %>% mutate(exonic_rate = exonic/(star_uniquely_mapped * 2)) %>% mutate(intronic_rate = intronic/(star_uniquely_mapped * 2)) %>% mutate(intergenic_rate = intergenic/(star_uniquely_mapped * 2)) %>% - dplyr::rename(r_rna_rate = custom_content_biotype_counts_percent_r_rna) %>% - mutate(x5_3_bias = qualimap_5_3_bias) %>% - dplyr::select( - sample, - total_reads, - mapped_reads, - exonic_rate, - intronic_rate, - intergenic_rate, - r_rna_rate, - x5_3_bias - ) %>% - dplyr::select(where(~!all(is.na(.)))) %>% - as.data.frame() - - # metrics <- metrics %>% - # full_join(meta_df , by = c("sample" = "sample")) + mutate(x5_3_bias = qualimap_5_3_bias) + + # Sometimes we don't have rRNA due to mismatch annotation, We skip this if is the case + gtf=rtracklayer::import(gtf_fn) + + one=grep("gene_type", colnames(as.data.frame(gtf)), value = TRUE) + another=grep("gene_biotype", colnames(as.data.frame(gtf)), value = TRUE) + biotype=NULL + if(length(one)==1){ + biotype=one + }else if(length(another)==1){ + biotype=another + }else{ + warning("No gene biotype founded") + } + + if (!is.null(biotype)){ + annotation=as.data.frame(gtf) %>% .[,c("gene_id", biotype)] + rRNA=grepl("rRNA|tRNA",annotation[[biotype]]) + genes=intersect(annotation[rRNA,"gene_id"],row.names(counts)) + ratio=data.frame(sample=colnames(counts), + r_and_t_rna_rate=colSums(counts[genes,])/colSums(counts)) + metrics = left_join(metrics, ratio, by="sample") + }else{ + metrics[["r_and_t_rna_rate"]] <- NA + } + + # if ("custom_content_biotype_counts_percent_r_rna" %in% colnames(metrics)){ + # metrics <- mutate(metrics, r_rna_rate = custom_content_biotype_counts_percent_r_rna) + # }else{ + # metrics[["r_rna_rate"]] <- NA + # } + metrics=metrics[,c("sample","mapped_reads","exonic_rate","intronic_rate", + "total_reads", + "x5_3_bias", "r_and_t_rna_rate","intergenic_rate")] } metrics$sample <- make.names(metrics$sample) rownames(metrics) <- metrics$sample @@ -69,7 +86,7 @@ load_coldata <- function(coldata_fn, column, numerator, denominator, subset_colu dplyr::select(!matches("fastq") & !matches("strandness")) %>% distinct() if('description' %in% names(coldata)){ - coldata$sample <- coldata$description + coldata$sample <- tolower(coldata$description) } coldata <- coldata %>% distinct(sample, .keep_all = T) stopifnot(column %in% names(coldata)) @@ -93,10 +110,14 @@ load_counts <- function(counts_fn){ # bcbio input if(grepl('csv', counts_fn)){ - counts <- read_csv(counts_fn) %>% column_to_rownames('gene') + counts <- read_csv(counts_fn) %>% + mutate(gene = str_replace(gene, pattern = "\\.[0-9]+$", "")) %>% + column_to_rownames('gene') + colnames(counts) = tolower(colnames(counts)) + return(counts) } else { # nf-core input counts <- read_tsv(counts_fn) %>% dplyr::select(-gene_name) %>% - mutate(gene_id = str_replace(gene_id, pattern = "\\.[0-9]$", "")) %>% + mutate(gene_id = str_replace(gene_id, pattern = "\\.[0-9]+$", "")) %>% column_to_rownames('gene_id') %>% round return(counts) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R index 3a32cf9..64d1bdd 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R @@ -1,14 +1,20 @@ # project params date = "YYYYMMDD" -basedir <- './' +basedir <- './' # where to write down output files # params for bcbio # coldata_fn = "https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/coldata.csv" # counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/tximport-counts.csv' # se_object=url("https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/bcbio-se.rds") # + # params for nfcore +# This is the file used to run nf-core or compatible to that coldata_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' +# This file is inside star_salmon/ folder counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.tsv' +# This folder is in the output directory inside multiqc folder multiqc_data_dir = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/multiqc-report-data/' +# This file is inside the genome folder in the output directory +gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/genome.filtered.gtf.gz' se_object = NA diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R index bbcc10b..8151968 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R @@ -3,8 +3,9 @@ # This is the file used to run nf-core or compatible to that metadata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' # This file is inside star_salmon/ folder +# remove URL below to point to local files se_object=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.rds') -# This folder is in the output directory +# This folder is in the output directory inside multiqc folder multiqc_data_dir='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/multiqc-report-data/' # This file is inside the genome folder in the output directory gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/genome.filtered.gtf.gz' From cbaacfd932b8e33997b3f9fef421d74271ecf273 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Mon, 20 May 2024 15:48:51 -0400 Subject: [PATCH 28/55] fix sample order --- inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index 5507a22..2c1767f 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -100,8 +100,9 @@ metrics <- load_metrics(se_object, multiqc_data_dir,gtf_fn,counts) %>% as.data.frame() rownames(metrics) <- metrics$sample # if the names don't match in order or string check files names and coldata information -counts = counts[rownames(coldata)] -stopifnot(all(names(counts) == rownames(coldata))) +counts = counts[rownames(metrics)] +coldata = coldata[rownames(metrics),] +stopifnot(all(names(counts) == rownames(metrics))) ``` ```{r sanitize_datatable} @@ -187,6 +188,7 @@ dds_to_use <- DESeqDataSetFromMatrix(counts, coldata, design = formula) vsd_before <- vst(dds_to_use) norm_matrix = assay(vsd_before) +new_cdata <- coldata ``` ```{r, eval=run_ruv, results='asis', echo=FALSE} @@ -338,7 +340,8 @@ ggplot(top_n_exp, aes_string(x = column, y = 'log2_expression')) + geom_boxplot(outlier.shape = NA, linewidth=0.5, color="grey") + geom_point() + facet_wrap(~gene_name) + - ggtitle(str_interp('Expression of Top ${n} DEGs')) + ggtitle(str_interp('Expression of Top ${n} DEGs')) + + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) ``` From 120babc54ca4ca07887eb5f7aa3a304ebabe95c9 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Tue, 21 May 2024 10:51:54 -0400 Subject: [PATCH 29/55] add mm and hs rrna annotation --- inst/extdata/annotation/hg38.rna.gtf.gz | Bin 0 -> 25177 bytes inst/extdata/annotation/mm10.rna.gtf.gz | Bin 0 -> 23701 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 inst/extdata/annotation/hg38.rna.gtf.gz create mode 100644 inst/extdata/annotation/mm10.rna.gtf.gz diff --git a/inst/extdata/annotation/hg38.rna.gtf.gz b/inst/extdata/annotation/hg38.rna.gtf.gz new file mode 100644 index 0000000000000000000000000000000000000000..26bf2d9fa115850cb5b3d429a5e5128cce20dacf GIT binary patch literal 25177 zcmY(qby$_%6E!T2G)hQ!cS$#L=uT-Qln&{VlJ4&A2I(&8knZj{NFI^;?&I@**ZX__ 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zp&xZu5Qp()JjmyVb68MI|7F4>RE;hvFrQ~((35|Ab z>^p_LfZH(YsGsy=H~~K7Y3t*?sJ3k`o{du5@Q-`uDPwiw+60R*VgOyp@I(NqeMK2w;&6K zJzXgh5~5L;7**dAsWXuw?Kzd(*fCV4_u}gAnp8F{=L(crD~vg$xnpm|ETW0oDbrHG zC0YqTm9YY0sDn^np2lhyzkZSz(0qWRjY(}KM)vZwhvTz43Lc+CH5(|61`hTVof;HV zyiraqAGS!%F#!75p#yySOi1y@Xs29&%7i-b@jO>!3BT7aHPbq<9nOgZK|M}x+lJ|t zhY0kZ6Li@v-v%Oz&pL@Hx5EHLncDNdB6O6lD5pi*r+r^Cav_yAjJUEbprRD90w?H~ z7Q=<1dly-=8@h{bM8`XK^A(DM&sZbPn|2`V zUs9DHW)6Ua^*My67qfr|O%_Ex>CxJw3hFD99^`9+?#&b4ihbXpnSoFa9bm}1V43rv zNA&fv@9GgvW}2`{XKJ=y@oiqap=6XS$1kUk2%Txc2fs1vLL*O%ABb(K-WGl4$YTue zlb&Ye)*54EUKMhc8Q&F-TrA2&ps<3vzeDy>ZFCXdi4B48j=}l?byu@=smT5ZVJjI0 literal 0 HcmV?d00001 From 9024ef3296d3bb6cba3889100d7c33c7f2e7a7ac Mon Sep 17 00:00:00 2001 From: eberdan Date: Tue, 21 May 2024 11:30:25 -0400 Subject: [PATCH 30/55] Update params_qc_nf-core.R --- .../rnaseq/skeleton/QC/params_qc_nf-core.R | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R index 8151968..151a1e3 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R @@ -1,11 +1,12 @@ # info params # This is the file used to run nf-core or compatible to that -metadata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' +metadata_fn='/Path/to/metadata/meta.csv' # This file is inside star_salmon/ folder -# remove URL below to point to local files -se_object=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.rds') -# This folder is in the output directory inside multiqc folder -multiqc_data_dir='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/multiqc-report-data/' +se_object=('/path/to/nf-core/output/salmon/salmon.merged.gene_counts.rds') +# This folder called "multiqc_report_data" is inside the output directory star_salmon inside multiqc folder +multiqc_data_dir='/path/to/nf-core/output/star_salmon/multiqc_report_data' # This file is inside the genome folder in the output directory -gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/genome.filtered.gtf.gz' +gtf_fn='/path/to/nf-core/output/genome/hg38.filtered.gtf' +# Put hg38, mm10, mm39, or other +genome='hg38' From 488288ba76d3cfeefbc600fdb4d2dba4f91c833e Mon Sep 17 00:00:00 2001 From: eberdan Date: Tue, 21 May 2024 11:32:16 -0400 Subject: [PATCH 31/55] Update QC_nf-core.Rmd --- .../rnaseq/skeleton/QC/QC_nf-core.Rmd | 20 ++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index 1758a3c..6248c60 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -40,7 +40,6 @@ source(params$project_file) - PI: `r PI` - Analyst: `r analyst` - Experiment: `r experiment` -- Aim: `r aim` ```{r load_libraries, cache = FALSE, message = FALSE, warning=FALSE} @@ -187,7 +186,19 @@ metrics <- metrics %>% mutate(x5_3_bias = qualimap_5_3_bias) # Sometimes we don't have rRNA due to mismatch annotation, We skip this if is the case -gtf=rtracklayer::import(gtf_fn) + +if (params$genome == "hg38") { + gtf <- "../../data/hg38.rna.gtf" +} elseif (params$genome == "mm10") { + gtf <- "../../data/mm10.rna.gtf" +} elseif (params$genome == "mm39") { + gtf <- "../../data/mm39.rna.gtf" +} elseif (params$genome == "other") { + gtf <- gtf_fn +} else { + print("No genome provided! Please add it to params_qc_nf-core.R") +} + one=grep("gene_type", colnames(as.data.frame(gtf)), value = TRUE) another=grep("gene_biotype", colnames(as.data.frame(gtf)), value = TRUE) @@ -414,7 +425,7 @@ metrics %>% ggplot(aes(x = factor(sample, level = order), y = r_and_t_rna_rate * 100, color = .data[[factor_of_interest]])) + - geom_point(alpha = 0.5, size=4) + + geom_point(alpha = 0.5) + ylab("tRNA/rRNA rate, %")+ ylim(0, rrna_ylim) + ggtitle("tRNA/rRNA mapping rate") + @@ -488,8 +499,11 @@ pca1 <- degPCA(vst, coldat_for_pca, pca2 <- degPCA(vst, coldat_for_pca, condition = factor_of_interest, data = T, pc1="PC3", pc2="PC4")[["plot"]] + + pca1 + scale_color_cb_friendly() pca2 + scale_color_cb_friendly() + ``` # Covariates analysis From 84e84244e0e46e6631b6dcd891178af34603dc4b Mon Sep 17 00:00:00 2001 From: eberdan Date: Tue, 21 May 2024 11:37:16 -0400 Subject: [PATCH 32/55] Update README.md --- README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index fa1267a..f2822fb 100644 --- a/README.md +++ b/README.md @@ -20,6 +20,8 @@ devtools::install_github("bcbio/bcbioR",ref = "devel") ## Quick start +Use this code to generate a standard project name for all of your folders. **This code will not create any folders or files.** + ``` r library(bcbioR) ## basic example code @@ -29,6 +31,12 @@ bcbio_set_project() ### Set base project +use `setwd()` to set your current directory to the place where you want to work. The bcbioR functions will automatically write to whatever directory you have set. + +``` r +setwd("/path/to/analysis/folder") +``` + The following code will pop up a Rmd template and then clicking ‘save’ will populate that folder with HCBC data structure guidelines ``` r @@ -40,9 +48,10 @@ As well, You can get this by going to File -\> New File -\> R markdown: - then ` ### Set RNAseq report folder This code will populate the folder with HCBC data structure guidelines and Rmd code: +**You do not need to create a reports folder prior to running this code. This will create and populate the reports folder.** ``` r -bcbio_templates(type="rnaseq", outpath="test_folder/reports") +bcbio_templates(type="rnaseq", outpath="/path/to/analysis/folder/reports") ``` ### Discover more… From df1fe6800a02a06d8e3964249c6dade25900c76b Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Tue, 21 May 2024 16:40:44 -0400 Subject: [PATCH 33/55] merge fixes and update DE with precomputed r/tRNA --- .../templates/rnaseq/skeleton/DE/DEG.Rmd | 5 +- .../templates/rnaseq/skeleton/DE/load_data.R | 22 ++++++- .../templates/rnaseq/skeleton/DE/params_de.R | 18 ++++-- .../rnaseq/skeleton/QC/QC_nf-core.Rmd | 57 +++++++++++-------- .../rnaseq/skeleton/QC/params_qc_nf-core.R | 15 +++-- 5 files changed, 84 insertions(+), 33 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index 2c1767f..4bc4e3b 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -22,6 +22,8 @@ params: column: sample_type subset_column: null subset_value: null + # Put hg38, mm10, mm39, or other + genome: hg38 ruv: false params_file: params_de.R project_file: ../information.R @@ -37,6 +39,7 @@ source(params$project_file) # 3. Load custom functions to load data from coldata/metrics/counts source(params$functions_file) # IMPORTANT set these values if you are not using the parameters at the top +genome=params$genome column=params$column numerator=params$numerator denominator=params$denominator @@ -95,7 +98,7 @@ coldata$sample=row.names(coldata) counts <- load_counts(counts_fn) counts <- counts[,colnames(counts) %in% coldata$sample] -metrics <- load_metrics(se_object, multiqc_data_dir,gtf_fn,counts) %>% +metrics <- load_metrics(se_object, multiqc_data_dir, gtf_fn, counts) %>% left_join(coldata, by = c('sample')) %>% as.data.frame() rownames(metrics) <- metrics$sample diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R index 8bc1ec2..8a1d297 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/load_data.R @@ -43,7 +43,27 @@ load_metrics <- function(se_object, multiqc_data_dir, gtf_fn, counts){ mutate(x5_3_bias = qualimap_5_3_bias) # Sometimes we don't have rRNA due to mismatch annotation, We skip this if is the case - gtf=rtracklayer::import(gtf_fn) + gtf <- NULL + if (genome =="other"){ + gtf <- gtf_fn + }else{ + if (genome == "hg38") { + gtf <- "hg38.rna.gtf.gz" + } else if (genome == "mm10") { + gtf <- "mm10.rna.gtf.gz" + } else if (genome == "mm39") { + gtf <- "mm39.rna.gtf.gz" + } + gtf <- system.file("extdata", "annotation", + gtf, + package="bcbioR") + } + if (is.null(gtf)) { + print("No genome provided! Please add it at the top of this Rmd") + } + + gtf=rtracklayer::import(gtf) + one=grep("gene_type", colnames(as.data.frame(gtf)), value = TRUE) another=grep("gene_biotype", colnames(as.data.frame(gtf)), value = TRUE) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R index 64d1bdd..fce1561 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R @@ -9,12 +9,22 @@ basedir <- './' # where to write down output files # # params for nfcore +# Your data # This is the file used to run nf-core or compatible to that -coldata_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/coldata.csv' +coldata_fn='/Path/to/metadata/meta.csv' # This file is inside star_salmon/ folder -counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/salmon.merged.gene_counts.tsv' +counts_fn='/path/to/nf-core/output/star_salmon/salmon.merged.gene_counts.tsv' +# This folder called "multiqc_report_data" is inside the output directory star_salmon inside multiqc folder +multiqc_data_dir='/path/to/nf-core/output/star_salmon/multiqc_report_data' +# This file is inside the genome folder in the output directory, use this only non-model organism +# gtf_fn='/path/to/nf-core/output/genome/hg38.filtered.gtf' + + +# Example data: COMMENT THESE LINE IF YOU ARE USING YOUT DATA +coldata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/coldata.csv' +counts_fn=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/star_salmon/salmon.merged.gene_counts.tsv') # This folder is in the output directory inside multiqc folder -multiqc_data_dir = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/multiqc-report-data/' +multiqc_data_dir='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/multiqc/star_salmon/multiqc-report-data/' # This file is inside the genome folder in the output directory -gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/nf-core/genome.filtered.gtf.gz' +gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/devel/nf-core/genome/genome.filtered.gtf.gz' se_object = NA diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index 6248c60..c7a96f7 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -18,6 +18,8 @@ editor_options: chunk_output_type: console params: params_file: params_qc_nf-core.R + # Put hg38, mm10, mm39, or other + genome: hg38 project_file: ../information.R factor_of_interest: sample_type --- @@ -27,6 +29,7 @@ params: # 1. set up factor_of_interest parameter from parameter above or manualy # this is used to color plots, it needs to be part of the metadata factor_of_interest=params$factor_of_interest +genome=params$genome # 2. Set input files in this file source(params$params_file) # 3. If you set up this file, project information will be printed below and @@ -140,7 +143,7 @@ meta_df <- data.frame(meta_df[,!(colnames(meta_df) %in% c("fastq_1", "fastq_2", ggplot(meta_df, aes(.data[[factor_of_interest]], fill = .data[[factor_of_interest]])) + - geom_bar() + ylab("") + xlab("") + + geom_bar() + ylab("") + xlab("") + ylab("# of samples") + scale_fill_cb_friendly() ``` @@ -186,19 +189,26 @@ metrics <- metrics %>% mutate(x5_3_bias = qualimap_5_3_bias) # Sometimes we don't have rRNA due to mismatch annotation, We skip this if is the case - -if (params$genome == "hg38") { - gtf <- "../../data/hg38.rna.gtf" -} elseif (params$genome == "mm10") { - gtf <- "../../data/mm10.rna.gtf" -} elseif (params$genome == "mm39") { - gtf <- "../../data/mm39.rna.gtf" -} elseif (params$genome == "other") { - gtf <- gtf_fn -} else { - print("No genome provided! Please add it to params_qc_nf-core.R") +gtf <- NULL +if (genome =="other"){ + gtf <- gtf_fn +}else{ + if (genome == "hg38") { + gtf <- "hg38.rna.gtf.gz" + } else if (genome == "mm10") { + gtf <- "mm10.rna.gtf.gz" + } else if (genome == "mm39") { + gtf <- "mm39.rna.gtf.gz" + } + gtf <- system.file("extdata", "annotation", + gtf, + package="bcbioR") +} +if (is.null(gtf)) { + print("No genome provided! Please add it at the top of this Rmd") } +gtf=rtracklayer::import(gtf) one=grep("gene_type", colnames(as.data.frame(gtf)), value = TRUE) another=grep("gene_biotype", colnames(as.data.frame(gtf)), value = TRUE) @@ -411,7 +421,7 @@ metrics %>% coord_flip() + xlab("") + scale_color_cb_friendly() + ylim(c(0, 100)) + - geom_hline(yintercept=0.2, color = "grey", size=2) + geom_hline(yintercept=20, color = "grey", size=2) ``` ## tRNA/rRNA mapping rate @@ -422,16 +432,17 @@ Samples should have a ribosomal RNA (rRNA) "contamination" rate below 10% rrna_ylim <- max(round(metrics$r_and_t_rna_rate*100, 2)) + 10 metrics %>% - ggplot(aes(x = factor(sample, level = order), - y = r_and_t_rna_rate * 100, - color = .data[[factor_of_interest]])) + - geom_point(alpha = 0.5) + - ylab("tRNA/rRNA rate, %")+ - ylim(0, rrna_ylim) + - ggtitle("tRNA/rRNA mapping rate") + - coord_flip() + - scale_color_cb_friendly() + - geom_hline(yintercept=0.2, color = "grey", size=2) + ggplot(aes(x = factor(sample, level = order), + y = r_and_t_rna_rate * 100, + color = .data[[factor_of_interest]])) + + geom_point(alpha = 0.5) + + ylab("tRNA/rRNA rate, %")+ + ylim(0, rrna_ylim) + + ggtitle("tRNA/rRNA mapping rate") + + coord_flip() + + scale_color_cb_friendly() + + ylim(c(0, 100)) + + geom_hline(yintercept=20, color = "grey", size=2) ``` ## 5'->3' bias diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R index 151a1e3..4bd19b8 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R @@ -1,12 +1,19 @@ # info params +# Your data # This is the file used to run nf-core or compatible to that metadata_fn='/Path/to/metadata/meta.csv' # This file is inside star_salmon/ folder -se_object=('/path/to/nf-core/output/salmon/salmon.merged.gene_counts.rds') +se_object='/path/to/nf-core/output/star_salmon/salmon.merged.gene_counts.rds' # This folder called "multiqc_report_data" is inside the output directory star_salmon inside multiqc folder multiqc_data_dir='/path/to/nf-core/output/star_salmon/multiqc_report_data' +# This file is inside the genome folder in the output directory, use this only non-model organism +# gtf_fn='/path/to/nf-core/output/genome/hg38.filtered.gtf' + +# Example data: COMMENT THESE LINE IF YOU ARE USING YOUT DATA +metadata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/coldata.csv' +se_object=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/star_salmon/salmon.merged.gene_counts.rds') +# This folder is in the output directory inside multiqc folder +multiqc_data_dir='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/multiqc/star_salmon/multiqc-report-data/' # This file is inside the genome folder in the output directory -gtf_fn='/path/to/nf-core/output/genome/hg38.filtered.gtf' -# Put hg38, mm10, mm39, or other -genome='hg38' +gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/devel/nf-core/genome/genome.filtered.gtf.gz' From 322896096c033d74f3df04eb493a409642c0b95b Mon Sep 17 00:00:00 2001 From: Alex Bartlett Date: Wed, 22 May 2024 14:53:13 -0400 Subject: [PATCH 34/55] add gitignore, tweaks for lack of subset_value, typos --- inst/rmarkdown/templates/common/skeleton/.gitignore | 9 +++++++++ inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd | 6 +++--- ...plicative_DE_docs.md.md => Multiplicative_DE_docs.md} | 0 inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R | 2 +- .../templates/rnaseq/skeleton/DE/run_markdown.R | 2 +- .../templates/rnaseq/skeleton/QC/params_qc_nf-core.R | 2 +- 6 files changed, 15 insertions(+), 6 deletions(-) create mode 100644 inst/rmarkdown/templates/common/skeleton/.gitignore rename inst/rmarkdown/templates/rnaseq/skeleton/DE/{Multiplicative_DE_docs.md.md => Multiplicative_DE_docs.md} (100%) diff --git a/inst/rmarkdown/templates/common/skeleton/.gitignore b/inst/rmarkdown/templates/common/skeleton/.gitignore new file mode 100644 index 0000000..4cdfa50 --- /dev/null +++ b/inst/rmarkdown/templates/common/skeleton/.gitignore @@ -0,0 +1,9 @@ +*.Rproj* +.Rhistory +.Rproj.user +.Rhistory +.DS* +*._* +*placeholder* +data/* +final/* \ No newline at end of file diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index 4bc4e3b..5c0e78a 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -123,11 +123,11 @@ sanitize_datatable = function(df, ...) { - Analyst: `r analyst` - Experiment: `r experiment` - Aim: `r aim` -- Comparison: `r paste0(subset_value, ': ', numerator, ' vs. ', denominator)` +- Comparison: `r ifelse(is.na(subset_value) | is.null(subset_value), paste0(numerator, ' vs. ', denominator), paste0(subset_value, ': ', numerator, ' vs. ', denominator))` ```{r create_filenames} -if (!is.null(subset_value)){ +if (!is.null(subset_value) & !is.na(subset_value)){ filenames = str_interp("${subset_value}_${numerator}_vs_${denominator}") } else { filenames = str_interp("${numerator}_vs_${denominator}") @@ -415,7 +415,7 @@ res_for_writing <- res %>% pathways_for_writing <- pathways_long %>% mutate(comparison = str_interp("${numerator}_vs_${denominator}")) -if (!is.null(subset_value)){ +if (!is.null(subset_value) & !is.na(subset_value)){ counts_norm <- counts_norm %>% mutate(subset = subset_value) res_for_writing <- res_for_writing %>% diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md.md b/inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md similarity index 100% rename from inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md.md rename to inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R index fce1561..9469b00 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R @@ -20,7 +20,7 @@ multiqc_data_dir='/path/to/nf-core/output/star_salmon/multiqc_report_data' # gtf_fn='/path/to/nf-core/output/genome/hg38.filtered.gtf' -# Example data: COMMENT THESE LINE IF YOU ARE USING YOUT DATA +# Example data: COMMENT THESE LINE IF YOU ARE USING YOUR DATA coldata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/coldata.csv' counts_fn=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/star_salmon/salmon.merged.gene_counts.tsv') # This folder is in the output directory inside multiqc folder diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R index e209d6b..0d5acf1 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R @@ -6,7 +6,7 @@ render_de <- function(column,numerator, denominator, subset_value = NA, rmarkdown::render(input = "DEG.Rmd", output_dir = ".", output_format = "html_document", - output_file = ifelse(!is.null(subset_value), + output_file = ifelse(!is.na(subset_value), paste0('DE_', subset_value, '_', numerator, '_vs_', denominator, '.html'), paste0('DE_', numerator, '_vs_', denominator, '.html') ), diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R index 4bd19b8..55eb411 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R @@ -10,7 +10,7 @@ multiqc_data_dir='/path/to/nf-core/output/star_salmon/multiqc_report_data' # This file is inside the genome folder in the output directory, use this only non-model organism # gtf_fn='/path/to/nf-core/output/genome/hg38.filtered.gtf' -# Example data: COMMENT THESE LINE IF YOU ARE USING YOUT DATA +# Example data: COMMENT THESE LINE IF YOU ARE USING YOUR DATA metadata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/coldata.csv' se_object=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/star_salmon/salmon.merged.gene_counts.rds') # This folder is in the output directory inside multiqc folder From 2a651b1ec16482b15e274fee93d534a561f37840 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 22 May 2024 15:05:16 -0400 Subject: [PATCH 35/55] making example data more explicit --- R/hello.R | 1 + .../rnaseq/skeleton/DE/params_de-testdata.R | 18 ++++++++++++++++++ .../templates/rnaseq/skeleton/DE/params_de.R | 9 --------- .../rnaseq/skeleton/DE/run_markdown.R | 12 ++++++++---- .../skeleton/QC/params_qc_nf-core-testdata.R | 9 +++++++++ .../rnaseq/skeleton/QC/params_qc_nf-core.R | 10 +--------- .../rnaseq/skeleton/QC/run_markdown.R | 5 ++++- 7 files changed, 41 insertions(+), 23 deletions(-) create mode 100644 inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de-testdata.R create mode 100644 inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core-testdata.R diff --git a/R/hello.R b/R/hello.R index 47e4f18..7f3cde0 100644 --- a/R/hello.R +++ b/R/hello.R @@ -1,5 +1,6 @@ .fix <- function(x){ x <- tolower(x) %>% str_replace_all(., "[[:punct:]]", "_") + x <- str_replace_all(x, " ", "_") return(x) } diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de-testdata.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de-testdata.R new file mode 100644 index 0000000..cc75ad2 --- /dev/null +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de-testdata.R @@ -0,0 +1,18 @@ +# project params +date = "YYYYMMDD" +basedir <- './' # where to write down output files + +# params for bcbio +# coldata_fn = "https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/coldata.csv" +# counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/tximport-counts.csv' +# se_object=url("https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/bcbio-se.rds") +# + +# Example data +coldata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/coldata.csv' +counts_fn=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/star_salmon/salmon.merged.gene_counts.tsv') +# This folder is in the output directory inside multiqc folder +multiqc_data_dir='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/multiqc/star_salmon/multiqc-report-data/' +# This file is inside the genome folder in the output directory +gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/devel/nf-core/genome/genome.filtered.gtf.gz' +se_object = NA diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R index 9469b00..e2221c5 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R @@ -19,12 +19,3 @@ multiqc_data_dir='/path/to/nf-core/output/star_salmon/multiqc_report_data' # This file is inside the genome folder in the output directory, use this only non-model organism # gtf_fn='/path/to/nf-core/output/genome/hg38.filtered.gtf' - -# Example data: COMMENT THESE LINE IF YOU ARE USING YOUR DATA -coldata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/coldata.csv' -counts_fn=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/star_salmon/salmon.merged.gene_counts.tsv') -# This folder is in the output directory inside multiqc folder -multiqc_data_dir='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/multiqc/star_salmon/multiqc-report-data/' -# This file is inside the genome folder in the output directory -gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/devel/nf-core/genome/genome.filtered.gtf.gz' -se_object = NA diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R index 0d5acf1..e2acf07 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R @@ -1,7 +1,11 @@ library(rmarkdown) # set working directory to this file before using the function -render_de <- function(column,numerator, denominator, subset_value = NA, - params_file = 'params_de.R'){ + +# set directory to this file folder +setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) +# example running with test data +render_de <- function(column, numerator, denominator, subset_value = NA, + params_file = 'params_de-testdata.R'){ rmarkdown::render(input = "DEG.Rmd", output_dir = ".", @@ -19,9 +23,9 @@ render_de <- function(column,numerator, denominator, subset_value = NA, denominator = denominator, params_file = params_file, project_file = '../information.R', - functions_file = '../load_data.R' + functions_file = 'load_data.R' ) ) } - +#Example data render_de("sample_type","tumor", "normal") diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core-testdata.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core-testdata.R new file mode 100644 index 0000000..dae62ce --- /dev/null +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core-testdata.R @@ -0,0 +1,9 @@ +# info params + +# Example data: COMMENT THESE LINE IF YOU ARE USING YOUR DATA +metadata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/coldata.csv' +se_object=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/star_salmon/salmon.merged.gene_counts.rds') +# This folder is in the output directory inside multiqc folder +multiqc_data_dir='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/multiqc/star_salmon/multiqc-report-data/' +# This file is inside the genome folder in the output directory +gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/devel/nf-core/genome/genome.filtered.gtf.gz' diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R index 55eb411..fd75d18 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core.R @@ -7,13 +7,5 @@ metadata_fn='/Path/to/metadata/meta.csv' se_object='/path/to/nf-core/output/star_salmon/salmon.merged.gene_counts.rds' # This folder called "multiqc_report_data" is inside the output directory star_salmon inside multiqc folder multiqc_data_dir='/path/to/nf-core/output/star_salmon/multiqc_report_data' -# This file is inside the genome folder in the output directory, use this only non-model organism +# This file is inside the genome folder in the output directory, use this only for non-model organism # gtf_fn='/path/to/nf-core/output/genome/hg38.filtered.gtf' - -# Example data: COMMENT THESE LINE IF YOU ARE USING YOUR DATA -metadata_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/coldata.csv' -se_object=url('https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/star_salmon/salmon.merged.gene_counts.rds') -# This folder is in the output directory inside multiqc folder -multiqc_data_dir='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/devel/rnaseq/nf-core/multiqc/star_salmon/multiqc-report-data/' -# This file is inside the genome folder in the output directory -gtf_fn='https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/devel/nf-core/genome/genome.filtered.gtf.gz' diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/run_markdown.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/run_markdown.R index bb07111..51acbef 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/run_markdown.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/run_markdown.R @@ -1,10 +1,13 @@ library(rmarkdown) +# set directory to this file folder +setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) +# example running with test data rmarkdown::render("QC_nf-core.Rmd", output_dir = ".", clean = TRUE, output_format = "html_document", params = list( - params_file = 'params_qc_nf-core.R', + params_file = 'params_qc_nf-core-testdata.R', project_file = '../information.R') ) From 452c73b6e83d91c5c1ce0a471502114f8a036bc2 Mon Sep 17 00:00:00 2001 From: Alex Bartlett Date: Wed, 22 May 2024 15:10:06 -0400 Subject: [PATCH 36/55] more tweaks for subset value --- inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd | 4 ++-- inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index 5c0e78a..0710bd7 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -123,7 +123,7 @@ sanitize_datatable = function(df, ...) { - Analyst: `r analyst` - Experiment: `r experiment` - Aim: `r aim` -- Comparison: `r ifelse(is.na(subset_value) | is.null(subset_value), paste0(numerator, ' vs. ', denominator), paste0(subset_value, ': ', numerator, ' vs. ', denominator))` +- Comparison: `r ifelse(is.null(subset_value), paste0(numerator, ' vs. ', denominator), paste0(subset_value, ': ', numerator, ' vs. ', denominator))` ```{r create_filenames} @@ -415,7 +415,7 @@ res_for_writing <- res %>% pathways_for_writing <- pathways_long %>% mutate(comparison = str_interp("${numerator}_vs_${denominator}")) -if (!is.null(subset_value) & !is.na(subset_value)){ +if (!is.null(subset_value)){ counts_norm <- counts_norm %>% mutate(subset = subset_value) res_for_writing <- res_for_writing %>% diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R index 0d5acf1..551d1db 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/run_markdown.R @@ -1,12 +1,12 @@ library(rmarkdown) # set working directory to this file before using the function -render_de <- function(column,numerator, denominator, subset_value = NA, +render_de <- function(column,numerator, denominator, subset_value = NULL, params_file = 'params_de.R'){ rmarkdown::render(input = "DEG.Rmd", output_dir = ".", output_format = "html_document", - output_file = ifelse(!is.na(subset_value), + output_file = ifelse(!is.null(subset_value), paste0('DE_', subset_value, '_', numerator, '_vs_', denominator, '.html'), paste0('DE_', numerator, '_vs_', denominator, '.html') ), From 3030f902b2eeb76ed83c2f949ccfa045d9a19761 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 22 May 2024 15:18:05 -0400 Subject: [PATCH 37/55] fix dependencies --- DESCRIPTION | 1 + R/bcbioR-package.R | 1 + 2 files changed, 2 insertions(+) diff --git a/DESCRIPTION b/DESCRIPTION index 9eaabc3..66b3f42 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,6 +11,7 @@ Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.1 Imports: + DESeq2, stringr, ggplot2, magrittr, diff --git a/R/bcbioR-package.R b/R/bcbioR-package.R index 122b9ef..64459df 100644 --- a/R/bcbioR-package.R +++ b/R/bcbioR-package.R @@ -7,6 +7,7 @@ #' @importFrom readr read_csv #' @importFrom stringr str_replace_all ## usethis namespace: end +#' @import DESeq2 #' @import ggplot2 #' @import hues #' @import ggprism From 291c3c055bdcb10509cba7aa6c820a19ba287af0 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 22 May 2024 15:26:31 -0400 Subject: [PATCH 38/55] fix na vs null check --- inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index 0710bd7..be18e1f 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -127,7 +127,7 @@ sanitize_datatable = function(df, ...) { ```{r create_filenames} -if (!is.null(subset_value) & !is.na(subset_value)){ +if (!is.null(subset_value) & !is.null(subset_value)){ filenames = str_interp("${subset_value}_${numerator}_vs_${denominator}") } else { filenames = str_interp("${numerator}_vs_${denominator}") From 9b3354cf5a224df44e5885012ddc7af7acbe1112 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 22 May 2024 15:27:04 -0400 Subject: [PATCH 39/55] update NAMESPACE --- NAMESPACE | 1 + 1 file changed, 1 insertion(+) diff --git a/NAMESPACE b/NAMESPACE index 9fce5a5..3cc700a 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -8,6 +8,7 @@ export(cb_friendly_pal) export(list_cb_friendly_cols) export(scale_color_cb_friendly) export(scale_fill_cb_friendly) +import(DESeq2) import(R.utils) import(ggplot2) import(ggprism) From 879e7340cb7ca33e32ab7ac46ecffc7b700a3e71 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 22 May 2024 15:32:56 -0400 Subject: [PATCH 40/55] remove duplicate library --- .../rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index c7a96f7..447be28 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -52,7 +52,6 @@ library(rtracklayer) library(DESeq2) library(DEGreport) library(ggrepel) -library(pheatmap) # library(RColorBrewer) library(DT) library(pheatmap) @@ -132,8 +131,9 @@ sanitize_datatable = function(df, ...) { ```{r load_metadata} meta_df=read_csv(metadata_fn) %>% - arrange(.data[[factor_of_interest]]) %>% distinct(sample, .keep_all = T) %>% - select(!matches("fastq"), !matches("strandness")) + arrange(.data[[factor_of_interest]]) %>% + distinct(sample, .keep_all = T) %>% + dplyr::select(!matches("fastq"), !matches("strandness")) meta_df$sample <- make.names(meta_df$sample) order <- meta_df$sample @@ -243,7 +243,7 @@ metrics=metrics[,c("sample","mapped_reads","exonic_rate","intronic_rate", metrics$sample <- make.names(metrics$sample) metrics <- metrics %>% full_join(meta_df , by = c("sample" = "sample")) %>% - select(where(~!all(is.na(.)))) + dplyr::select(where(~!all(is.na(.)))) ``` From e9e2f556a2b2e3774a4369a4d07b50f04280f9db Mon Sep 17 00:00:00 2001 From: eberdan Date: Thu, 23 May 2024 12:20:41 -0400 Subject: [PATCH 41/55] Update QC_nf-core.Rmd fixed some text errors and axis labels --- .../rnaseq/skeleton/QC/QC_nf-core.Rmd | 25 ++++++++++--------- 1 file changed, 13 insertions(+), 12 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index 447be28..b69265c 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -260,7 +260,7 @@ meta_sm %>% sanitize_datatable() ## Total reads -Here, we want to see consistency and a minimum of 20 million reads. +Here, we want to see consistency and a minimum of 20 million reads (the grey line). ```{r plot_total_reads} metrics %>% @@ -271,7 +271,8 @@ metrics %>% coord_flip() + scale_y_continuous(name = "million reads") + scale_fill_cb_friendly() + xlab("") + - ggtitle("Total reads") + ggtitle("Total reads") + + geom_hline(yintercept=20000000, color = "grey", size=2) metrics %>% ggplot(aes(x = .data[[factor_of_interest]], @@ -292,7 +293,7 @@ max_pct_mapped <- round(max(metrics$mapped_reads/metrics$total_reads)*100,1) ## Mapping rate -The genomic mapping rate represents the percentage of reads mapping to the reference genome. We want to see consistent mapping rates between samples and over 70% mapping. These samples have mapping rates (`r min_pct_mapped` - `r max_pct_mapped`%). +The genomic mapping rate represents the percentage of reads mapping to the reference genome. We want to see consistent mapping rates between samples and over 70% mapping (the grey line). These samples have mapping rates: `r min_pct_mapped` - `r max_pct_mapped`%. ```{r plot_mapping_rate} metrics$mapped_reads_pct <- round(metrics$mapped_reads/metrics$total_reads*100,1) @@ -304,14 +305,14 @@ metrics %>% coord_flip() + scale_color_cb_friendly() + ylim(0, 100) + - ggtitle("Mapping rate") + + ggtitle("Mapping rate") + xlab("") + geom_hline(yintercept=70, color = "grey", size=2) ``` ## Number of genes detected -The number of genes represented in every sample is expected to be consistent and over 20K (blue line). +The number of genes represented in every sample is expected to be consistent and over 20K (grey line). ```{r calc_genes_detected} genes_detected <- colSums(assays(se)[["counts"]] > 0) %>% enframe() @@ -370,7 +371,7 @@ metrics %>% ## Exonic mapping rate -Here we are looking for consistency, and exonic mapping rates around 70% or 75% (blue and red lines, respectively). +Here we are looking for consistency, and exonic mapping rates around or above 70% (grey line). ```{r plot_exonic_mapping_rate} metrics %>% @@ -389,7 +390,7 @@ metrics %>% ## Intronic mapping rate -Here, we expect a low intronic mapping rate (≤ 15% - 20%) +Here, we expect a low intronic mapping rate (≤ 15% - 20%). The grey line indicates 20%. ```{r plot_intronic_mapping_rate} metrics %>% @@ -408,7 +409,7 @@ metrics %>% ## Intergenic mapping rate -Here, we expect a low intergenic mapping rate, which is true for all samples. +Here, we expect a low intergenic mapping rate, which is true for all samples. The grey line indicates 15% ```{r plot_intergenic_mapping_rate} metrics %>% @@ -421,12 +422,12 @@ metrics %>% coord_flip() + xlab("") + scale_color_cb_friendly() + ylim(c(0, 100)) + - geom_hline(yintercept=20, color = "grey", size=2) + geom_hline(yintercept=15, color = "grey", size=2) ``` ## tRNA/rRNA mapping rate -Samples should have a ribosomal RNA (rRNA) "contamination" rate below 10% +Samples should have a ribosomal RNA (rRNA) "contamination" rate below 10% (the grey line). ```{r plot_rrna_mapping_rate} @@ -441,8 +442,8 @@ metrics %>% ggtitle("tRNA/rRNA mapping rate") + coord_flip() + scale_color_cb_friendly() + - ylim(c(0, 100)) + - geom_hline(yintercept=20, color = "grey", size=2) + ylim(c(0, 100)) + xlab("") + + geom_hline(yintercept=10, color = "grey", size=2) ``` ## 5'->3' bias From 72313e6ad511f07e601feeb2270ad783d4131d10 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Thu, 23 May 2024 23:12:00 -0400 Subject: [PATCH 42/55] fix params_de missing variable --- inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R | 1 + 1 file changed, 1 insertion(+) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R index e2221c5..8426428 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de.R @@ -18,4 +18,5 @@ counts_fn='/path/to/nf-core/output/star_salmon/salmon.merged.gene_counts.tsv' multiqc_data_dir='/path/to/nf-core/output/star_salmon/multiqc_report_data' # This file is inside the genome folder in the output directory, use this only non-model organism # gtf_fn='/path/to/nf-core/output/genome/hg38.filtered.gtf' +se_object = NA From c6eb85d2500f7646918d0ef5bb1d0cdf759ead81 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Thu, 23 May 2024 23:14:38 -0400 Subject: [PATCH 43/55] rename example data --- .../skeleton/DE/{params_de-testdata.R => params_de-example.R} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename inst/rmarkdown/templates/rnaseq/skeleton/DE/{params_de-testdata.R => params_de-example.R} (100%) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de-testdata.R b/inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de-example.R similarity index 100% rename from inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de-testdata.R rename to inst/rmarkdown/templates/rnaseq/skeleton/DE/params_de-example.R From 8d132a96fcb9984f519da9b66f961b005dfa5eeb Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Thu, 23 May 2024 23:16:06 -0400 Subject: [PATCH 44/55] rename example data --- .../{params_qc_nf-core-testdata.R => params_qc_nf-core-example.R} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename inst/rmarkdown/templates/rnaseq/skeleton/QC/{params_qc_nf-core-testdata.R => params_qc_nf-core-example.R} (100%) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core-testdata.R b/inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core-example.R similarity index 100% rename from inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core-testdata.R rename to inst/rmarkdown/templates/rnaseq/skeleton/QC/params_qc_nf-core-example.R From 2dbda683ee9db2c435056964a1d12c23978c29bd Mon Sep 17 00:00:00 2001 From: Alex Bartlett <74612800+abartlett004@users.noreply.github.com> Date: Wed, 29 May 2024 13:17:50 -0400 Subject: [PATCH 45/55] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f2822fb..65ad35d 100644 --- a/README.md +++ b/README.md @@ -89,3 +89,4 @@ Some best practices when developing: - Emma Berdan - Heather Wick - James Billingsley +- Zhu Zhuo From bd7f3eacf55ce74384917ab37b8bd5ab736f5309 Mon Sep 17 00:00:00 2001 From: eberdan Date: Wed, 29 May 2024 14:41:54 -0400 Subject: [PATCH 46/55] Update PCA_variance_analysis.Rmd save mid-way through updating --- .../skeleton/DE/PCA_variance_analysis.Rmd | 24 +++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd index 101c6b8..3be4ea1 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd @@ -3,6 +3,20 @@ title: "PCA with variance analysis" author: "Harvard Chan Bioinformatics Core" --- +Dispersion estimates are a key part of the DESEQ2 analysis. DESEQ2 uses data from all samples and all genes to generate a relationship between level expression and variance and then shrinks per gene dispersions to match this distribution. If one group has higher variance than all others this will affect the dispersion estimates. Here we visually check that the variance per group is similar using a PCA. The ellipses are minimal volume enclosing ellipses using the Khachiyan algorithm. + + +**Manually add in your covariate of interest to define the groups. One can be created by combining multiple metadata columns using the paste0 function.** + +```{r } +## Example of creating a group covariate + +# meta$group <- paste0(meta$sex,"_", meta$age,"_",meta$treatment) + +factor_of_interest <- "insert column name for covariate of interest" +``` + + ```{r } library(DEGreport) library(ggplot2) @@ -14,14 +28,20 @@ colors=cb_friendly_cols(1:15) ggplot2::theme_set(theme_prism(base_size = 14)) pca <- degPCA(assay(bcbio_vsd_data), colData(bcbio_vsd_data), - condition = "sample_type", name = "sample", data = T) + condition = factor_of_interest, name = "sample", data = T) pca$plot + ggtitle(paste0("All samples", "\nPCA using ", nrow(vst), " genes")) + theme(plot.title=element_text(hjust=0.5)) + geom_mark_ellipse(aes(color = sample_type)) ``` + +## PERMDISP + +Groups in a univariate analysis can also differ with regard to their mean values, variation around those means, or both. In univariate analyses, dispersion can be examined using Levene’s test. PERMDISP is a multivariate extension of Levene’s test to examine whether groups differ in variability. In essence, PERMDISP involves calculating the distance from each data point to its group centroid and then testing whether those distances differ among the groups. [Source](https://uw.pressbooks.pub/appliedmultivariatestatistics/chapter/permdisp/) + +Here we apply this test to our variance stabilized data. We calculate distances betwen samples and then use the betadisper() function from the popular vegan package. T + ```{r} -# NOTE:This is not confirmed to be a valid test but it could help to understand the data library(vegan) vare.disa <- vegdist(t(assay(bcbio_vsd_data))) From 47243778a126a392585199f00c599d7851dd4316 Mon Sep 17 00:00:00 2001 From: eberdan Date: Wed, 29 May 2024 14:49:15 -0400 Subject: [PATCH 47/55] Update PCA_variance_analysis.Rmd --- .../templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd index 3be4ea1..4074693 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/PCA_variance_analysis.Rmd @@ -39,7 +39,7 @@ pca$plot + ggtitle(paste0("All samples", "\nPCA using ", nrow(vst), " genes")) + Groups in a univariate analysis can also differ with regard to their mean values, variation around those means, or both. In univariate analyses, dispersion can be examined using Levene’s test. PERMDISP is a multivariate extension of Levene’s test to examine whether groups differ in variability. In essence, PERMDISP involves calculating the distance from each data point to its group centroid and then testing whether those distances differ among the groups. [Source](https://uw.pressbooks.pub/appliedmultivariatestatistics/chapter/permdisp/) -Here we apply this test to our variance stabilized data. We calculate distances betwen samples and then use the betadisper() function from the popular vegan package. T +Here we apply this test to our variance stabilized data. We calculate distances betwen samples and then use the betadisper() function from the popular vegan package. We get two overall p-values where significant means that the dispersions are different between groups. The first p-value comes from the anova() function and the second from the permutest() function. We also get pairwise p-values for every group-group comparison. ```{r} library(vegan) @@ -47,6 +47,8 @@ vare.disa <- vegdist(t(assay(bcbio_vsd_data))) mod = betadisper(vare.disa, colData(bcbio_vsd_data)[['sample_type']]) anova(mod) +permutest(mod, pairwise = TRUE) + ``` From 6d1e977041ea6bf48255f834d010968fdbf05868 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Fri, 31 May 2024 09:11:47 -0400 Subject: [PATCH 48/55] add all templates to bcbio_templates --- R/hello.R | 26 ++++++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/R/hello.R b/R/hello.R index 7f3cde0..aee6389 100644 --- a/R/hello.R +++ b/R/hello.R @@ -39,7 +39,11 @@ bcbio_nfcore_check <- function(file){ #' Normally these helper files are inside a report folder inside a #' project folder. #' -#' @param type string indicating the type of analysis, supported: rnaseq. +#' @param type string indicating the type of analysis, supported: +#' - base +#' - rnaseq, scrnaseq, +#' - teaseq +#' - cosmx #' #' @param outpath string path indicating where to copy all the files to #' @examples @@ -49,20 +53,30 @@ bcbio_nfcore_check <- function(file){ #' @export bcbio_templates <- function(type="rnaseq", outpath){ switch(type, + base={ + fpath <- system.file("rmarkdown/templates/common", "skeleton", package="bcbioR") + copyDirectory(fpath, outpath) + }, rnaseq={ - fpath <- system.file("rmarkdown/templates/rnaseq", "skeleton", package="bcbioR") - #file.copy(fpath, outpath, recursive = T) copyDirectory(fpath, outpath) }, scrnaseq={ - fpath <- system.file("rmarkdown/templates/singlecell", "skeleton", package="bcbioR") - #file.copy(fpath, outpath, recursive = T) + copyDirectory(fpath, outpath) + }, + teaseq={ + fpath <- system.file("rmarkdown/templates/teaseq", "skeleton", package="bcbioR") + copyDirectory(fpath, outpath) + }, + cosmx={ + fpath <- system.file("rmarkdown/templates/cosmx", "skeleton", package="bcbioR") copyDirectory(fpath, outpath) }, { - stop('project type not recognize, please choose: ', 'rnaseq', 'scrnaseq') + stop('project type not recognize, please choose: ', 'base', + 'rnaseq', 'scrnaseq', + 'teaseq', 'cosmx') } ) } From 7883bb6d495d1e74c42dfe1dbffb1ec7afd7a4d8 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Fri, 31 May 2024 17:03:58 -0400 Subject: [PATCH 49/55] fix missing coma --- inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd index be18e1f..8139da5 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/DE/DEG.Rmd @@ -103,7 +103,7 @@ metrics <- load_metrics(se_object, multiqc_data_dir, gtf_fn, counts) %>% as.data.frame() rownames(metrics) <- metrics$sample # if the names don't match in order or string check files names and coldata information -counts = counts[rownames(metrics)] +counts = counts[,rownames(metrics)] coldata = coldata[rownames(metrics),] stopifnot(all(names(counts) == rownames(metrics))) ``` From 8b05353f84d4416ddb2ee928b0bffa688532afd3 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Fri, 31 May 2024 17:25:09 -0400 Subject: [PATCH 50/55] update readme --- README.Rmd | 84 ------------------------------------------------------ README.md | 18 +++++------- 2 files changed, 7 insertions(+), 95 deletions(-) delete mode 100644 README.Rmd diff --git a/README.Rmd b/README.Rmd deleted file mode 100644 index c81fba9..0000000 --- a/README.Rmd +++ /dev/null @@ -1,84 +0,0 @@ ---- -output: github_document ---- - -# bcbioR - - - -[![R-CMD-check](https://github.com/bcbio/bcbioR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/bcbio/bcbioR/actions/workflows/R-CMD-check.yaml) - -The goal of `bcbioR` is to create guidelines for NGS data interpretation based on the experience of the Harvard Chan Bioinformatics Core and everybody who contributes to this package. - -## Installation - -You can install the development version of bcbioR from [GitHub](https://github.com/) with: - -``` -# install.packages("devtools") -devtools::install_github("bcbio/bcbioR") -devtools::install_github("bcbio/bcbioR",ref = "devel") -``` - -## Quick start - -``` r -library(bcbioR) -## basic example code -# will help you to build a folder name following HCBC naming rules -bcbio_set_project() -``` - -### Set base project - -The following code will pop up a Rmd template and then clicking 'save' will populate that folder with HCBC data structure guidelines - -``` r -rmarkdown::draft("project_folder",template="common",package="bcbioR") -``` - -As well, You can get this by going to File -\> New File -\> R markdown: - then `From Template`, and choose `bcbio base` - choose the folder to deploy files, it could be a new folder or an existing one - -### Set RNAseq report folder - -This code will populate the folder with HCBC data structure guidelines and Rmd code: - -``` r -bcbio_templates(type="rnaseq", outpath="test_folder/reports") -``` - -### Discover more... - -Go to the vignette to know more `vignette("bcbioR_quick_start",package="bcbioR")` - -## How to Contribute - -### Open an issue - -* If you find a bug -* If you want a new feature -* If you want to add code to the templates - -### Modify the code - -* Clone the repository -* Make sure you are in the `devel` branch -* Create a new branch `git checkout -b feature1` -* Modify you code, add and commit -* Push to GitHub the new branch -* Create a PR from your branch to `devel` -* Assignt the PR to me or Alex - -Some best practices when developing: - -* install `devtools` -* render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this. -* Use `usethis::use_import_from("stringr","str_replace_all")` to add a new function you are using in the code. - -### Contributors - -- Lorena Pantano -- Alex Bartlett -- Emma Berdan -- Heather Wick -- James Billingsley diff --git a/README.md b/README.md index 65ad35d..86661fa 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ devtools::install_github("bcbio/bcbioR",ref = "devel") Use this code to generate a standard project name for all of your folders. **This code will not create any folders or files.** -``` r +``` library(bcbioR) ## basic example code # will help you to build a folder name following HCBC naming rules @@ -33,24 +33,21 @@ bcbio_set_project() use `setwd()` to set your current directory to the place where you want to work. The bcbioR functions will automatically write to whatever directory you have set. -``` r +``` setwd("/path/to/analysis/folder") ``` -The following code will pop up a Rmd template and then clicking ‘save’ will populate that folder with HCBC data structure guidelines +The following code will pop up a Rmd template will populate that folder with HCBC data structure guidelines -``` r -rmarkdown::draft("project_folder",template="common",package="bcbioR") ``` - -As well, You can get this by going to File -\> New File -\> R markdown: - then `From Template`, and choose `bcbio base` - choose the folder to deploy files, it could be a new folder or an existing one +bcbio_templates(type="base", outpath="/path/to/analysis/folder") +``` ### Set RNAseq report folder -This code will populate the folder with HCBC data structure guidelines and Rmd code: -**You do not need to create a reports folder prior to running this code. This will create and populate the reports folder.** +This code will populate the folder with HCBC data structure guidelines and Rmd code: **You do not need to create a reports folder prior to running this code. This will create and populate the reports folder.** -``` r +``` bcbio_templates(type="rnaseq", outpath="/path/to/analysis/folder/reports") ``` @@ -79,7 +76,6 @@ Go to the vignette to know more `vignette("bcbioR_quick_start",package="bcbioR") Some best practices when developing: - install `devtools` -- render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this. - Use `usethis::use_import_from("stringr","str_replace_all")` to add a new function you are using in the code. ### Contributors From ef6c6d7e8a9f51c82158c5dc80e077716152790d Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Fri, 31 May 2024 17:29:28 -0400 Subject: [PATCH 51/55] update NEWS --- NEWS.md | 1 + man/bcbio_templates.Rd | 8 +++++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/NEWS.md b/NEWS.md index dc80256..c49ffc1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,6 @@ # bcbioR 0.1.2 +* Add draft templates for TEAseq, COSMX * Adapt templates to nf-core rnaseq * Fix when sample start by number * Fix when rRNA rate is missing diff --git a/man/bcbio_templates.Rd b/man/bcbio_templates.Rd index bc2e50f..45c9ad9 100644 --- a/man/bcbio_templates.Rd +++ b/man/bcbio_templates.Rd @@ -7,7 +7,13 @@ bcbio_templates(type = "rnaseq", outpath) } \arguments{ -\item{type}{string indicating the type of analysis, supported: rnaseq.} +\item{type}{string indicating the type of analysis, supported: +\itemize{ +\item base +\item rnaseq, scrnaseq, +\item teaseq +\item cosmx +}} \item{outpath}{string path indicating where to copy all the files to} } From b25e7e943fe2946d52e73b712293eabbe7bb9d93 Mon Sep 17 00:00:00 2001 From: eberdan Date: Mon, 3 Jun 2024 15:17:29 -0400 Subject: [PATCH 52/55] Update QC_nf-core.Rmd made summary graph x-axis labels vertical and removed legend. --- inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index b69265c..a2c7bdc 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -144,7 +144,7 @@ meta_df <- data.frame(meta_df[,!(colnames(meta_df) %in% c("fastq_1", "fastq_2", ggplot(meta_df, aes(.data[[factor_of_interest]], fill = .data[[factor_of_interest]])) + geom_bar() + ylab("") + xlab("") + ylab("# of samples") + - scale_fill_cb_friendly() + scale_fill_cb_friendly() + theme(axis.text.x=element_text(angle = 90, vjust = 0.5), legend.position = "none") ``` From 8211c2b8932e786dc0212ed6f9f4e16845535981 Mon Sep 17 00:00:00 2001 From: Alex Bartlett Date: Wed, 5 Jun 2024 14:19:25 -0400 Subject: [PATCH 53/55] elizabeth suggestions --- .../rnaseq/skeleton/QC/QC_nf-core.Rmd | 20 ++++++++----------- 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd index a2c7bdc..f1b035e 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd +++ b/inst/rmarkdown/templates/rnaseq/skeleton/QC/QC_nf-core.Rmd @@ -26,7 +26,7 @@ params: ```{r source_params, echo = F} -# 1. set up factor_of_interest parameter from parameter above or manualy +# 1. set up factor_of_interest parameter from parameter above or manually # this is used to color plots, it needs to be part of the metadata factor_of_interest=params$factor_of_interest genome=params$genome @@ -137,10 +137,6 @@ meta_df=read_csv(metadata_fn) %>% meta_df$sample <- make.names(meta_df$sample) order <- meta_df$sample -# remove some columns -meta_df <- data.frame(meta_df[,!(colnames(meta_df) %in% c("fastq_1", "fastq_2", "strandedness"))]) - - ggplot(meta_df, aes(.data[[factor_of_interest]], fill = .data[[factor_of_interest]])) + geom_bar() + ylab("") + xlab("") + ylab("# of samples") + @@ -148,7 +144,7 @@ ggplot(meta_df, aes(.data[[factor_of_interest]], ``` -```{r} +```{r load_data} # read counts from SE object se <- readRDS(se_object) raw_counts <- assays(se)[["counts"]] %>% round() %>% @@ -156,9 +152,9 @@ raw_counts <- assays(se)[["counts"]] %>% round() %>% raw_counts=raw_counts[rowSums(raw_counts)!=0,] ``` -```{r prepare metrics} +```{r prepare_metrics} # Get metrics from nf-core into bcbio like table -# many metrics are already in the Genereal Table of MultiQC, this reads the file +# many metrics are already in the General Table of MultiQC, this reads the file metrics <- read_tsv(file.path(multiqc_data_dir, 'multiqc_general_stats.txt')) # we get some more metrics from Qualimap and rename columns @@ -247,7 +243,7 @@ metrics <- metrics %>% ``` -```{r show-metadata} +```{r show_metadata} meta_sm <- meta_df %>% as.data.frame() %>% column_to_rownames("sample") @@ -347,7 +343,7 @@ metrics %>% coord_flip() + scale_y_continuous(name = "million reads") + scale_color_cb_friendly() + xlab("") + - ggtitle("Total reads") + ggtitle("Number of Genes") ``` @@ -436,7 +432,7 @@ metrics %>% ggplot(aes(x = factor(sample, level = order), y = r_and_t_rna_rate * 100, color = .data[[factor_of_interest]])) + - geom_point(alpha = 0.5) + + geom_point(alpha = 0.5, size=4) + ylab("tRNA/rRNA rate, %")+ ylim(0, rrna_ylim) + ggtitle("tRNA/rRNA mapping rate") + @@ -498,7 +494,7 @@ In this section, we look at how well the different groups in the dataset cluster Principal Component Analysis (PCA) is a statistical technique used to simplify high-dimensional data by identifying patterns and reducing the number of variables. In the context of gene expression, PCA helps analyze large datasets containing information about the expression levels of thousands of genes across different samples (e.g., tissues, cells). - + ```{r PCA1:5 summary, all, unlabeled, fig.width= 7, fig.height = 5} vst <- vst(raw_counts) From bc5e1baf9bfff68d3f6743299490155c91798793 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 5 Jun 2024 16:46:06 -0400 Subject: [PATCH 54/55] clean rnaseq rmd --- DESCRIPTION | 4 +- .../skeleton/DE/Multiplicative_DE_docs.md | 129 ------------------ .../templates/rnaseq/skeleton/README.md | 8 +- vignettes/bcbioR_quick_start.Rmd | 31 ++++- 4 files changed, 35 insertions(+), 137 deletions(-) delete mode 100644 inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md diff --git a/DESCRIPTION b/DESCRIPTION index 66b3f42..cbc3225 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -19,7 +19,9 @@ Imports: ggprism, grDevices, R.utils, - readr + readr, + fs, + withr Suggests: knitr, rmarkdown, diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md b/inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md deleted file mode 100644 index aa35021..0000000 --- a/inst/rmarkdown/templates/rnaseq/skeleton/DE/Multiplicative_DE_docs.md +++ /dev/null @@ -1,129 +0,0 @@ -# Overview - -This is an example of complex DE analysis with multiple covariates with multiple levels. - -We have the SEX variable (2 levels) and the GENOTYPE VARIABLE (4 levels) - -# Intercept Analysis - -``` -# Model design and creating dds object from the dataset -design = ~sex + genotype + sex:genotype -dds <- DESeqDataSet(se_Striatum, design) -``` - -## Filtering lowly expressed genes -We are filtering out genes with fewer than 10 raw counts in total and are present in fewer than 3 samples. - -``` -keep <- rowSums(counts(dds)>=10) >=4 -dds <- dds[keep, ] -#dds # comment out this line to print the dds object and compare the dimension of the dataset before and after filtering is applied. -``` - -setting up WT as reference genotype and Male and reference sex. Otherwise DESeq2 will use the conditions in their alphabetical order. - -``` -dds$genotype <- relevel(dds$genotype, ref = "WT") -dds$sex <- relevel(dds$sex, ref = "Male") - -#Checking model design and reference condition comment out the three lines below to print the design and order of genotype and sex -design(dds) -levels(dds$genotype) -levels(dds$sex) - -#estimating size factors for normalization and fitting our model with DESeq model -dds <- estimateSizeFactors(dds) -dds <- DESeq(dds) -resultsNames(dds) #This will print out the name of coefficients being compared, comment it out to view - -# get coefficient matrix -mod_mat <- model.matrix(design(dds), data = colData(dds)) -mod_mat - -(Intercept) sexFemale genotypeCR3KO genotypeQ175 genotypeQ175_CR3KO sexFemale:genotypeCR3KO sexFemale:genotypeQ175 sexFemale:genotypeQ175_CR3KO -a10_st_q175_m_r1 1 0 0 1 0 0 0 0 -a12_st_q175_f_r1 1 1 0 1 0 0 1 0 -a14_st_wt_m_r1 1 0 0 0 0 0 0 0 -a16_st_wt_f_r1 1 1 0 0 0 0 0 0 -``` - - -coefficient weights extracted from the mod_mat above - -``` -WT_M <- c(1, 0, 0, 0, 0, 0, 0, 0) -WT_F <- c(1, 1, 0, 0, 0, 0, 0, 0) -WTCR3ko_M <- c(1, 0, 1, 0, 0, 0, 0, 0) -WTCR3ko_F <- c( 1, 1, 1, 0, 0, 1, 0, 0) -Q175_M <- c(1, 0, 0, 1, 0, 0, 0, 0) -Q175_F <- c(1, 1, 0, 1, 0, 0, 1, 0) -Q175CR3ko_M <- c(1, 0, 0, 0, 1, 0, 0, 0) -Q175CR3ko_F <- c(1, 1, 0, 0, 1, 0, 0, 1) -``` - -# Differential gene expression analysis - -## Comp_2: Female vs Male : WTCR3ko -``` -comp2_F.v.M_WTCR3ko <- results(dds, contrast = c(WTCR3ko_F - WTCR3ko_M)) -comp2_F.v.M_WTCR3ko_shrink <- lfcShrink(dds, contrast = c(WTCR3ko_F - WTCR3ko_M), type = "ashr") -summary(comp2_F.v.M_WTCR3ko) -``` - -## Comp_3: Female vs Male : Q175 -``` -comp3_F.v.M_Q175 <- results(dds, contrast = c(Q175_F - Q175_M)) -comp3_F.v.M_Q175_shrink <- lfcShrink(dds, contrast = c(Q175_F - Q175_M), type = "ashr") -summary(comp3_F.v.M_Q175) -``` - -## Comp_5: WTCR3ko vs WT : Male -```{r} -comp5_WTCR3ko.v.WT_Male <- results(dds, contrast = c(WTCR3ko_M - WT_M)) -comp5_WTCR3ko.v.WT_Male_shrink <- lfcShrink(dds, contrast = c(WTCR3ko_M - WT_M), type = "ashr") -summary(comp5_WTCR3ko.v.WT_Male) -``` - -## Comp_6: WTCR3ko vs WT : Female -```{r} -comp6_WTCR3ko.v.WT_Female <- results(dds, contrast = c(WTCR3ko_F - WT_F)) -comp6_WTCR3ko.v.WT_Female_shrink <- lfcShrink(dds, contrast = c(WTCR3ko_F - WT_F), type = "ashr") -summary(comp6_WTCR3ko.v.WT_Female) -``` - -## Comp_11: Q175CR3ko vs Q175 : Male -``` -comp11_Q175CR3ko.v.Q175_Male <- results(dds, contrast = c(Q175CR3ko_M - Q175_M)) -comp11_Q175CR3ko.v.Q175_Male_shrink <- lfcShrink(dds, contrast = c(Q175CR3ko_M - Q175_M), type = "ashr") -summary(comp11_Q175CR3ko.v.Q175_Male) -``` - -## Comp_12: Q175CR3ko vs Q175 : Female -``` -comp12_Q175CR3ko.v.Q175_Female <- results(dds, contrast = c(Q175CR3ko_F - Q175_F)) -comp12_Q175CR3ko.v.Q175_Female_shrink <- lfcShrink(dds, contrast = c(Q175CR3ko_F - Q175_F), type = "ashr") -summary(comp12_Q175CR3ko.v.Q175_Female) -``` - -## Comp_15: (Q175CR3ko-Q176) - (WTCR3ko - WT) : Male - -Does the CR3 knockout in Q175 differ from CR3 knockout in WT for Males? - -``` -comp15_CR3koinQ175.v.CR3koinWT_Male <- results(dds, - contrast = c(Q175CR3ko_M - Q175_M) - (WTCR3ko_M - WT_M)) -comp15_CR3koinQ175.v.CR3koinWT_Male_shrink <- lfcShrink(dds, - contrast = c(Q175CR3ko_M - Q175_M) - (WTCR3ko_M - WT_M), type = "ashr") -summary(comp15_CR3koinQ175.v.CR3koinWT_Male) -``` - -## Comp_17: (WTCR3koall) - (WTall) - -Does the average of the samples in WTCR3KO differ from average of the samples in WT - -``` -comp17_WTCR3all.v.WTall <- results(dds, contrast = c(WTCR3ko_M + WTCR3ko_F)/2 - (WT_M + WT_F)/2) -comp17_WTCR3all.v.WTall_shrink <- lfcShrink(dds, contrast = c((WTCR3ko_M + WTCR3ko_F)/2 - (WT_M + WT_F)/2), type = "ashr") -summary(comp17_WTCR3all.v.WTall) -``` diff --git a/inst/rmarkdown/templates/rnaseq/skeleton/README.md b/inst/rmarkdown/templates/rnaseq/skeleton/README.md index 50f8f16..b04a5d5 100644 --- a/inst/rmarkdown/templates/rnaseq/skeleton/README.md +++ b/inst/rmarkdown/templates/rnaseq/skeleton/README.md @@ -17,9 +17,9 @@ CONTROL_REP1,s3path/AEG588A1_S1_L004_R1_001.fastq.gz,s3path/AEG588A1_S1_L004_R2_ Use `bcbio_nfcore_check(csv_file)` to check the file is correct. -You can add more columns to this file with more metadata, and use this file as the `coldata` file the templates. +You can add more columns to this file with more metadata, and use this file as the `coldata` file in the templates. -- Upload file to our `Datasets` in Seqera using the name of the project but starting with `nfcore-rnaseq` +- Upload file to our `Datasets` in Seqera using the name of the project but starting with `rnaseq-pi_lastname-hbc_code` - Go to `Launchpad`, select `nf-core_rnaseq` pipeline, and select the previous created `Datasets` in the `input` parameter after clicking in `Browser` - Select an output directory with the same name used for the `Dataset` inside the `results` folder in S3 - When pipeline is down, data will be copied to our on-premise HPC in the scratch system under `scratch/groups/hsph/hbc/bcbio/` folder @@ -48,10 +48,6 @@ On the `YAML` header file of the `Rmd` you can specify some parameters or just s - Pathway analysis - Tables -There are some code related to alternative analysis: - -- `DE/Multiplicative_DE_docs.md` that shows some cases when there is multiple variables in the model with multiple levels: sex (2 levels) and genotype (4 levels) - ## DropBox - In `reports/QC` diff --git a/vignettes/bcbioR_quick_start.Rmd b/vignettes/bcbioR_quick_start.Rmd index 7282482..a05670a 100644 --- a/vignettes/bcbioR_quick_start.Rmd +++ b/vignettes/bcbioR_quick_start.Rmd @@ -42,8 +42,37 @@ And get the colors directly: ```{r} cb_friendly_cols(1:16) ``` -```{r} +This is the full palette: + +```{r fig.width=9, fig.height=6} library(hues) swatch(cb_friendly_cols(1:16)) ``` +# Set projects + +HCBC uses a structured based directory to organize projects. You can set up this by using: + +```{r} +tmp_dir=withr::local_tempdir() +bcbio_templates(type="base", outpath=tmp_dir) +fs::dir_ls(tmp_dir, recurse=TRUE) +``` + + +We support multiple analyses type: + +- RNAseq +- scRNAseq +- TEAseq +- COSMX + +To get the example code for any of them you can use a similar command: + +```{r} +analysis_tmp=fs::path_join(c(tmp_dir, "reports")) +bcbio_templates(type="rnaseq", outpath=analysis_tmp) +fs::dir_ls(analysis_tmp, recurse=TRUE) +``` + +Use `scrnaseq`, `teaseq` or `cosmx` to get those other templates. From 85e4a21d152bee1dd108ae603623c5bf7a56a4c2 Mon Sep 17 00:00:00 2001 From: Lorena Pantano Date: Wed, 5 Jun 2024 18:29:16 -0400 Subject: [PATCH 55/55] update readme --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 86661fa..35161bb 100644 --- a/README.md +++ b/README.md @@ -86,3 +86,4 @@ Some best practices when developing: - Heather Wick - James Billingsley - Zhu Zhuo +- Elizabeth Partan