From 8115c3b16d10c2d23f3b4daa6e6a06a3b1929933 Mon Sep 17 00:00:00 2001 From: Paul Jonas Jost <70631928+PaulJonasJost@users.noreply.github.com> Date: Tue, 27 Aug 2024 09:03:51 +0200 Subject: [PATCH] Documentation example data (#335) * First try slideshow * Code changing example * R Code structure * Fixed bug in the links * Code structure * Remove data * Adjusted Slideshow * Apply suggestions from code review Co-authored-by: Lea Seep <74967328+LeaSeep@users.noreply.github.com> --------- Co-authored-by: Lea Seep <74967328+LeaSeep@users.noreply.github.com> --- docs/assets/images/Slideshow1.png | Bin 0 -> 285080 bytes docs/assets/images/Slideshow2.png | Bin 0 -> 464301 bytes docs/assets/images/Slideshow3.png | Bin 0 -> 382105 bytes docs/assets/images/Slideshow4.png | Bin 0 -> 494305 bytes .../assets/images/volcano_plot_comparison.pdf | Bin 0 -> 1018553 bytes docs/assets/images/volcano_plot_original.pdf | Bin 0 -> 1022437 bytes docs/assets/images/volcano_plot_theme.pdf | Bin 0 -> 1020888 bytes .../assets/images/volcano_plot_thresholds.pdf | Bin 0 -> 1020783 bytes docs/code-and-data.md | 13 +- docs/code-and-data/data.md | 6 - docs/code-and-data/examples.md | 526 +++++++++++++++++- docs/code-and-data/r-code.md | 288 +++++++++- docs/interface-details.md | 4 +- 13 files changed, 826 insertions(+), 11 deletions(-) create mode 100644 docs/assets/images/Slideshow1.png create mode 100644 docs/assets/images/Slideshow2.png create mode 100644 docs/assets/images/Slideshow3.png create mode 100644 docs/assets/images/Slideshow4.png create mode 100644 docs/assets/images/volcano_plot_comparison.pdf create mode 100644 docs/assets/images/volcano_plot_original.pdf create mode 100644 docs/assets/images/volcano_plot_theme.pdf create mode 100644 docs/assets/images/volcano_plot_thresholds.pdf delete mode 100644 docs/code-and-data/data.md diff --git a/docs/assets/images/Slideshow1.png b/docs/assets/images/Slideshow1.png new file mode 100644 index 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+ + +

Test 1

+ + + +### Downloaded R Code + +If successful, you should have downloaded a folder with the files `Data.RDS`, `utils. +R` and `Code.R` (you need to unzipp the downloaded folder). The `Data.RDS` file contains the data used in the analysis, a more +detailed description of the data can be found in the [Data object](data.md). The +`utils.R` file contains the custom functions used in the analysis, while the `Code.R` file +contains the code to reproduce the volcano plot along with the data processing steps. + +The R code is shown below: + +
+ +```r +# ShinyOmics R Code Download +# Load necassary packages ---- +# Note that you do not need to install packages everytime you run the script +# The following will check whether the package is installed and if not, installs it +# We provide the version and repo of the package that was used in the project +# in case the you run into problems try to install the specifc version + +# This command is requried only once per R installation. (uncomment if needed) +# install.packages("BiocManager", repos = "https://cloud.r-project.org") +# BiocManager::install(version = "3.16") +check_and_install_package <- function(package_name) { + for(package in package_name){ + # Check if the package is installed + if (!requireNamespace(package, quietly = TRUE)) { + # If not installed, install the package + BiocManager::install(package) + } + } +} +check_and_install_package("SummarizedExperiment") +library("SummarizedExperiment") #tested with: source Bioconductor, v.1.28.0 +check_and_install_package("rstudioapi") +library("rstudioapi") #tested with: source CRAN, v.0.16.0 +check_and_install_package("ggplot2") +library("ggplot2") #tested with: source CRAN, v.3.5.1 +check_and_install_package("DESeq2") +library("DESeq2") #tested with: source Bioconductor, v.1.38.3 + + +# Load the data ---- +# The following will try to detect the directory of the file and load the data +# this is succesfull if +# - you have just unzipped the folder and did not move the files separately to different locations +# - you kept the original filenames +# - you work in RStudio + +# If the requisites are not met you will have to adjust the path to the data file +# and your utils.R file (if present) manually + +MANUALLY <- FALSE # change to TRUE, if you want to set the paths manually + +if(MANUALLY){ + # Adjust the path to the data file + envList <- readRDS('path/to/Data.rds') + # Adjust the path to the utils.R file + source('path/to/utils.R') + print('Path manually set') +}else{ + # if you get an error try to set paths manually + # remember to set MANUALLY <- TRUE + direcoty_of_files <- dirname(rstudioapi::getSourceEditorContext()$path) + envList <- readRDS(paste0(direcoty_of_files,'/','Data.rds')) + if('utils.R' %in% list.files(direcoty_of_files)){ + source(file.path(direcoty_of_files,'utils.R')) + } + print('Path automatically set') +} + +# Set Environment ---- +list2env(envList,envir = globalenv()) +# loads the varaibles directly into global env +# if loadedversion present, make it global +if(exists('loadedVersion')){ + assign('loadedVersion',loadedVersion,envir = globalenv()) +} + +# if you want to combine multiple plots use the `with` notation instead e.g. +# plot <- with(envList, {ggplot(..)+geom_point()}) + +# Setting default options +CUSTOM_THEME <- theme_bw(base_size = 15) + + theme( + axis.title = element_text(size = 15), # Axis labels + axis.text = element_text(size = 15), # Axis tick labels + legend.text = element_text(size = 15), # Legend text + legend.title = element_text(size = 15), # Legend title + plot.title = element_text(size = 17, face = 'bold') # Plot title + ) + +# Happy Adjusting! :) +# Data Selection ---- +selected <- rownames(rowData(res_tmp$data_original)) + +samples_selected <- colnames(assay(res_tmp$data_original)) +tmp_data_selected <- res_tmp$data_original[selected,samples_selected] +# Data Preprocessing ---- +res_tmp$data <- tmp_data_selected[which(rowSums(assay(tmp_data_selected)) > 10),] +dds <- DESeq2::DESeqDataSetFromMatrix( + countData = assay(res_tmp$data), + colData = colData(res_tmp$data), + design = as.formula(par_tmp$DESeq_formula) + ) + de_seq_result <- DESeq2::DESeq(dds) + res_tmp$DESeq_obj <- de_seq_result + dds_vst <- vst( + object = de_seq_result, + blind = TRUE + ) + assay(res_tmp$data) <- as.data.frame(assay(dds_vst)) + + + + # Test correction list +PADJUST_METHOD <- list( + "None" = "none", + "Bonferroni" = "bonferroni", + "Benjamini-Hochberg" = "BH", + "Benjamini Yekutieli" = "BY", + "Holm" = "holm", + "Hommel" = "hommel", + "Hochberg" = "hochberg", + "FDR" = "BH" +) +# get the results +res2plot <- list() + +if(par_tmp$PreProcessing_Procedure == "vst_DESeq"){ + dds <- res_tmp$DESeq_obj + + # rewind the comparisons again + newList <- par_tmp$SigAna$comparisons + contrasts <- vector("list", length(par_tmp$SigAna$comparisons)) + for (i in 1:length(newList)) { + contrasts[[i]] <- unlist(strsplit(x = par_tmp$SigAna$comparisons[i],split = ":")) + } + + # get the results for each contrast and put it all in a big results object + sig_results <- list() + for (i in 1:length(contrasts)) { + if(identical( + list(test_method = "Wald", test_correction = PADJUST_METHOD[[par_tmp$SigAna$test_correction]]), + par_tmp$SigAna[[par_tmp$SigAna$sample_annotation_types_cmp]][[par_tmp$SigAna$comparisons[i]]] + )){ + print("Results exists, skipping calculations.") + sig_results[[par_tmp$SigAna$comparisons[i]]] <- res_tmp$SigAna[[par_tmp$SigAna$sample_annotation_types_cmp]][[par_tmp$SigAna$comparisons[i]]] + next + } + sig_results[[par_tmp$SigAna$comparisons[i]]] <- DESeq2::results( + dds, + contrast = c( + par_tmp$SigAna$sample_annotation_types_cmp, + contrasts[[i]][1], + contrasts[[i]][2] + ), + pAdjustMethod = PADJUST_METHOD[[par_tmp$SigAna$test_correction]] + ) + # fill in res_tmp, par_tmp + res_tmp$SigAna[[par_tmp$SigAna$sample_annotation_types_cmp]][[par_tmp$SigAna$comparisons[i]]] <- sig_results[[par_tmp$SigAna$comparisons[i]]] + par_tmp$SigAna[[par_tmp$SigAna$sample_annotation_types_cmp]][[par_tmp$SigAna$comparisons[i]]] <- list( + test_method = "Wald", + test_correction = PADJUST_METHOD[[par_tmp$SigAna$test_correction]] + ) + } + }else{ + # all other methods require manual testing + # rewind the comparisons again + newList <- par_tmp$SigAna$comparisons + contrasts <- vector("list", length(par_tmp$SigAna$comparisons)) + contrasts_all <- list() + for (i in 1:length(newList)) { + contrasts[[i]] <- unlist(strsplit(x = par_tmp$SigAna$comparisons[i],split = ":")) + contrasts_all <- append(contrasts_all, contrasts[[i]]) + } + # make all contrasts unique + contrasts_all <- unique(unlist(contrasts_all)) + # name the contrasts with the comparison names + names(contrasts) <- par_tmp$SigAna$comparisons + # get names of columns we want to choose: + index_comparisons <- which( + colData(res_tmp$data)[,par_tmp$SigAna$sample_annotation_types_cmp] %in% contrasts_all + ) + samples_selected <- colData(res_tmp$data)[index_comparisons,] + # get the data + data_selected <- as.matrix(assay(res_tmp$data))[,index_comparisons] + sig_results <- significance_analysis( + df = as.data.frame(data_selected), + samples = as.data.frame(samples_selected), + contrasts = contrasts, + method = par_tmp$SigAna$test_method, + correction = PADJUST_METHOD[[par_tmp$SigAna$test_correction]], + contrast_level = par_tmp$SigAna$sample_annotation_types_cmp + ) + } + + + + + +# plot volcano plot +contrast <- paste0(par_tmp$SigAna$contrast[[1]], ":", par_tmp$SigAna$contrast[[2]]) +data4Volcano <- sig_results[[contrast]] +par_name <- gsub(":","_",contrast) +significance_threshold <- par_tmp$SigAna[paste0("SignificanceAnalysis-",par_name,"_psig_th")] +lfc_threshold <- par_tmp$SigAna[paste0("SignificanceAnalysis-",par_name,"_lfc_th")] +data4Volcano$probename <- rownames(data4Volcano) +data4Volcano$threshold <- ifelse(data4Volcano$padj>significance_threshold,"non-significant","significant") +data4Volcano$threshold_raw <- ifelse(data4Volcano$pvalue>significance_threshold,"non-significant","significant") +data4Volcano$threshold_fc <- ifelse( + data4Volcano$log2FoldChange>lfc_threshold, + "up-regulated", + ifelse( + data4Volcano$log2FoldChange<(-1*as.numeric(lfc_threshold)), + "down-regulated", " " + ) +) +data4Volcano$combined <- paste0(data4Volcano$threshold," + ",data4Volcano$threshold_fc) +data4Volcano$combined_raw <- paste0(data4Volcano$threshold_raw," + ",data4Volcano$threshold_fc) +colorScheme2 <- c("#cf0e5bCD", "#0e5bcfCD", "#939596CD","#cf0e5b1A", "#0e5bcf1A", "#9395961A") +names(colorScheme2) <- c( + "significant + up-regulated", "significant + down-regulated", "significant + ", + "non-significant + up-regulated", "non-significant + down-regulated", "non-significant + " +) + +# remove NA values +data4Volcano <- data4Volcano[complete.cases(data4Volcano),] + + +Volcano_plot <- ggplot( + data4Volcano, + aes(label=probename) +) + + geom_point(aes( + x = log2FoldChange, + y = -log10(padj), + colour = combined + )) + + geom_hline( + yintercept = -1*(log10(as.numeric(significance_threshold))), + color="lightgrey" + ) + + geom_vline( + xintercept = c((-1*as.numeric(lfc_threshold)),as.numeric(lfc_threshold)), + color="lightgrey" + ) + + scale_color_manual(values=colorScheme2, name="") + + xlab("Log FoldChange") + + ylab("-log10(p_adj-value)") + + theme(legend.position = "none") + + CUSTOM_THEME + + ggtitle(label="Corrected p-Values") +lapply(ls(pattern='plot'), get) +``` +
+ +A detailed explanation of the code structure can be found [here](r-code.md). We will +concentrate on specific parts of the code to understand the workflow and alter it in +different ways. + +### Running the Original Code + +Running the unaltered code will produce the following plot: + +![The original plot reproduced from the shiny application](/cOmicsArt/assets/images/volcano_plot_original.pdf) + +### Altering the Theme + +As a first step, we can change the overall theme of the volcano plot. As the volcano +plot is created using `ggplot2`, we can use the `theme` function to alter the +appearance by just replacing the original `CUSTOM_THEME` object with the altered one. +In the code `CUTSOM_THEME` can be found in lines 71-79. Below you can see the +original and altered theme side by side: + +
+ + +
+

Original Theme

+
+

+# Setting default options
+CUSTOM_THEME <- theme_bw(base_size = 15) + 
+  theme(
+    # Axis labels
+    axis.title = element_text(size = 15),
+    # Axis tick labels
+    axis.text = element_text(size = 15),
+    # Legend text
+    legend.text = element_text(size = 15),
+    # Legend title
+    legend.title = element_text(size = 15),
+    # Plot title
+    plot.title = element_text(size = 17, face = 'bold')
+  )
+      
+
+
+ + +
+ → +
+ + +
+

Altered Theme

+
+

+# Setting altered theme options
+CUSTOM_THEME <- theme_dark(base_size = 18) + 
+  theme(
+    # Axis labels with italic font
+    axis.title = element_text(size = 18, face = 'italic'),
+    # Axis tick labels
+    axis.text = element_text(size = 15),
+    # Legend text
+    legend.text = element_text(size = 15),
+    # Legend title
+    legend.title = element_text(size = 15),
+    # Plot title with red color
+    plot.title = element_text(size = 17, face = 'bold', color = 'red'),
+    # Darker panel background
+    panel.background = element_rect(fill = 'gray20')
+  )
+      
+
+
+ +
+ +We can save the plot easily as pdf, png or svg using: + +```r +ggsave( + filename = paste0("Volcano_plot",".pdf"), + plot = Volcano_plot, + width = 10, + height = 5 +) +``` + +The altered volcano plot will look like this: + +![Changed Theme with a dark background and red title.](/cOmicsArt/assets/images/volcano_plot_theme.pdf) + +A more extensive list of possible manipulations with ggplot2 can we found in [their +website](https://ggplot2.tidyverse.org). + +### Altering Thresholds + +In many cases, we have to adjust the pvalue threshold or the log2 fold change +threshold, determining the significance of the data points. In the code, the thresholds +are defined in lines 194 + 195. They originally use the vales stored from the +application, but we can change to new values, without the need to go back to cOmicsArt. + +
+ + +
+

Original Thresholds

+
+

+significance_threshold <- par_tmp$SigAna[paste0("SignificanceAnalysis-", par_name, "_psig_th")]
+lfc_threshold <- par_tmp$SigAna[paste0("SignificanceAnalysis-", par_name, "_lfc_th")]
+      
+
+
+ + +
+ → +
+ + +
+

Altered Thresholds

+
+

+significance_threshold <- 0.01
+lfc_threshold <- 2.5
+      
+
+
+ +
+ +After changing the thresholds, the plot will look like this: + +![Changed thresholds to 0.01 and 2.5 for pvalue and log2 fold change, respectively.](/cOmicsArt/assets/images/volcano_plot_thresholds.pdf) + +### Altering the Comparison + +In a troughout analysis, we never only want to compare a single contrast. We can +adjust the code to change the contrast we compare. In the most straightforward case, +we will flip the treatment and control groups. We will keep the adjusted thresholds +and thus only have to take care of the `par_tmp$SigAna$comparisons` and +`par_tmp$SigAna$contrast`. We overwrite both after loading the RDS file, holding the data and results obtained from cOmicsArt. + +
+ + +
+

Original Thresholds

+
+

+list2env(envList,envir = globalenv()) 
+# loads the variables directly into global env
+# if loadedversion present, make it global
+if(exists('loadedVersion')){
+  assign('loadedVersion',loadedVersion,envir = globalenv())
+}
+      
+
+
+ + +
+ → +
+ + +
+

Altered Thresholds

+
+

+# Set Environment ----
+list2env(envList,envir = globalenv()) 
+# loads the variables directly into global env
+# if loadedversion present, make it global
+if(exists('loadedVersion')){
+  assign('loadedVersion',loadedVersion,envir = globalenv())
+}
+par_tmp$SigAna$comparisons <- "untrt:trt"
+par_tmp$SigAna$contrast <- c("untrt", "trt")
+      
+
+
+ +
+ +The altered plot will look like a mirrored version of the threshold adjusted plot: + +![Changed comparison to untrt:trt.](/cOmicsArt/assets/images/volcano_plot_comparison.pdf) + +### Conclusions + +There are of course many more things one can adjust in the plot theme, the threshholds +and or the comparison. The code is structured in a way that makes it easy to adjust +the code and rerun the analysis. With this example, we showed how to adjust the +code to our needs and individualize the workflow. \ No newline at end of file diff --git a/docs/code-and-data/r-code.md b/docs/code-and-data/r-code.md index 90410133..f55c4f01 100644 --- a/docs/code-and-data/r-code.md +++ b/docs/code-and-data/r-code.md @@ -3,4 +3,290 @@ title: R code layout: default parent: Code and Data download nav_order: 2 ---- \ No newline at end of file +--- + +# R code + +This page contains an explanation of the R code structure when downloading R code and +data to reproduce/alter the analysis. We will use the original code of [the volcano +plot example](examples.md). + +## 1. Package loading and installation + +As a first step the code loads the necessary packages for the analysis. If the +packages is not installed, the code will install it. The code will look like this: + +```r +check_and_install_package <- function(package_name) { + for(package in package_name){ + # Check if the package is installed + if (!requireNamespace(package, quietly = TRUE)) { + # If not installed, install the package + BiocManager::install(package) + } + } +} + +check_and_install_package("SummarizedExperiment") +library("SummarizedExperiment") #tested with: source Bioconductor, v.1.28.0 +check_and_install_package("rstudioapi") +library("rstudioapi") #tested with: source CRAN, v.0.16.0 +check_and_install_package("ggplot2") +library("ggplot2") #tested with: source CRAN, v.3.5.1 +check_and_install_package("DESeq2") +library("DESeq2") #tested with: source Bioconductor, v.1.38.3 +``` + +With this we ensure that the packages are installed and loaded and the code will run as intended. + +## 2. Data loading + +The code will then try to detect the directoy of the file and based on that load the +data. Notice that this works if: +1. You have just unzipped the folder and **did not** move the files to another directory. +2. You kept the **original file names**. +3. You work in **RStudio**. + +Adjustments to these might render the code unable to load the data. In this case you +can manually insert the file paths of the rds object and the utility file. The code +will look like this: + +```r +MANUALLY <- FALSE # change to TRUE, if you want to set the paths manually + +if(MANUALLY){ + # Adjust the path to the data file + envList <- readRDS('path/to/Data.rds') + # Adjust the path to the utils.R file + source('path/to/utils.R') + print('Path manually set') +}else{ + # if you get an error try to set paths manually + # remember to set MANUALLY <- TRUE + direcoty_of_files <- dirname(rstudioapi::getSourceEditorContext()$path) + envList <- readRDS(paste0(direcoty_of_files,'/','Data.rds')) + if('utils.R' %in% list.files(direcoty_of_files)){ + source(file.path(direcoty_of_files,'utils.R')) + } + print('Path automatically set') +} + +# Set Environment ---- +list2env(envList,envir = globalenv()) +# loads the varaibles directly into global env +# if loadedversion present, make it global +if(exists('loadedVersion')){ + assign('loadedVersion',loadedVersion,envir = globalenv()) +} +``` + +## 3. Data Selection + +This one is rather straightforward. To adjust it you can just adjst `selected` and +`samples_selected` to your needs. The code will look like this: + +```r +# Data Selection ---- +selected <- rownames(rowData(res_tmp$data_original)) +samples_selected <- colnames(assay(res_tmp$data_original)) + +tmp_data_selected <- res_tmp$data_original[selected,samples_selected] +``` + +## 4. Preprocessing + +The preprocessing is set up as used in the application. Adjustments to this part might +be more intricate, e.g. adjusting the statistical model in DESeq2. The code will look +like this (here depending on the preprocessing method): + +```r +res_tmp$data <- tmp_data_selected[which(rowSums(assay(tmp_data_selected)) > 10),] +dds <- DESeq2::DESeqDataSetFromMatrix( + countData = assay(res_tmp$data), + colData = colData(res_tmp$data), + design = as.formula(par_tmp$DESeq_formula) +) +de_seq_result <- DESeq2::DESeq(dds) +res_tmp$DESeq_obj <- de_seq_result +dds_vst <- vst( + object = de_seq_result, + blind = TRUE +) +assay(res_tmp$data) <- as.data.frame(assay(dds_vst)) +``` + +Up until this point the code will look the same for all different kinds of analysis +you can download the code for. The following steps will be highly specific to the +analysis for which the code was downloaded. If you want to combine multiple analysis +steps you may copy and paste the these last parts of multiple analyses below the first 4 +steps altogether. + +## 5. Analysis + +This is the core of the downloaded code. Here the analysis is performed. Adjusting +these parts is possible with some knowledge of R and the specific analysis. The code +for e.g. a statistical analysis will look like this: + +```r +# get the results +res2plot <- list() + +if(par_tmp$PreProcessing_Procedure == "vst_DESeq"){ + dds <- res_tmp$DESeq_obj + + # rewind the comparisons again + newList <- par_tmp$SigAna$comparisons + contrasts <- vector("list", length(par_tmp$SigAna$comparisons)) + for (i in 1:length(newList)) { + contrasts[[i]] <- unlist(strsplit(x = par_tmp$SigAna$comparisons[i],split = ":")) + } + + # get the results for each contrast and put it all in a big results object + sig_results <- list() + for (i in 1:length(contrasts)) { + if(identical( + list(test_method = "Wald", test_correction = PADJUST_METHOD[[par_tmp$SigAna$test_correction]]), + par_tmp$SigAna[[par_tmp$SigAna$sample_annotation_types_cmp]][[par_tmp$SigAna$comparisons[i]]] + )){ + print("Results exists, skipping calculations.") + sig_results[[par_tmp$SigAna$comparisons[i]]] <- res_tmp$SigAna[[par_tmp$SigAna$sample_annotation_types_cmp]][[par_tmp$SigAna$comparisons[i]]] + next + } + sig_results[[par_tmp$SigAna$comparisons[i]]] <- DESeq2::results( + dds, + contrast = c( + par_tmp$SigAna$sample_annotation_types_cmp, + contrasts[[i]][1], + contrasts[[i]][2] + ), + pAdjustMethod = PADJUST_METHOD[[par_tmp$SigAna$test_correction]] + ) + # fill in res_tmp, par_tmp + res_tmp$SigAna[[par_tmp$SigAna$sample_annotation_types_cmp]][[par_tmp$SigAna$comparisons[i]]] <- sig_results[[par_tmp$SigAna$comparisons[i]]] + par_tmp$SigAna[[par_tmp$SigAna$sample_annotation_types_cmp]][[par_tmp$SigAna$comparisons[i]]] <- list( + test_method = "Wald", + test_correction = PADJUST_METHOD[[par_tmp$SigAna$test_correction]] + ) + } + }else{ + # all other methods require manual testing + # rewind the comparisons again + newList <- par_tmp$SigAna$comparisons + contrasts <- vector("list", length(par_tmp$SigAna$comparisons)) + contrasts_all <- list() + for (i in 1:length(newList)) { + contrasts[[i]] <- unlist(strsplit(x = par_tmp$SigAna$comparisons[i],split = ":")) + contrasts_all <- append(contrasts_all, contrasts[[i]]) + } + # make all contrasts unique + contrasts_all <- unique(unlist(contrasts_all)) + # name the contrasts with the comparison names + names(contrasts) <- par_tmp$SigAna$comparisons + # get names of columns we want to choose: + index_comparisons <- which( + colData(res_tmp$data)[,par_tmp$SigAna$sample_annotation_types_cmp] %in% contrasts_all + ) + samples_selected <- colData(res_tmp$data)[index_comparisons,] + # get the data + data_selected <- as.matrix(assay(res_tmp$data))[,index_comparisons] + sig_results <- significance_analysis( + df = as.data.frame(data_selected), + samples = as.data.frame(samples_selected), + contrasts = contrasts, + method = par_tmp$SigAna$test_method, + correction = PADJUST_METHOD[[par_tmp$SigAna$test_correction]], + contrast_level = par_tmp$SigAna$sample_annotation_types_cmp + ) + } +``` + +An example how to adjust this, for example changing the comparison method is shown in +[the volcano plot example](examples.md) in the last step. + +## 6. Visualization + +After the analysis is done, the results are visualized. There are two visualization +types in cOmicsArt, using ggplot2 and using pheatmap. These parts can be adjusted +following their respective documentation. Also, there exists gallerys and other useful resources, where one can screen for what they search for visually and get the particular code snippets: +- [R Gallery,](ggplot2 https://r-graph-gallery.com/ggplot2-package.html) +- [Top 50 ggplot visualisaitions](https://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html?utm_content=cmp-true) +- [ggplot extensions](https://exts.ggplot2.tidyverse.org/gallery/) +- [Pheatmp function](https://r-charts.com/correlation/pheatmap/?utm_content=cmp-true) +From own experience, we can also recommend LLMs such as ChatGPT to help to adjust the plotting commands. The code for a typical ggplot2 visualization +will look like this: + +```r +# plot volcano plot +contrast <- paste0(par_tmp$SigAna$contrast[[1]], ":", par_tmp$SigAna$contrast[[2]]) +data4Volcano <- sig_results[[contrast]] +par_name <- gsub(":","_",contrast) +significance_threshold <- par_tmp$SigAna[paste0("SignificanceAnalysis-",par_name,"_psig_th")] +lfc_threshold <- par_tmp$SigAna[paste0("SignificanceAnalysis-",par_name,"_lfc_th")] +data4Volcano$probename <- rownames(data4Volcano) +data4Volcano$threshold <- ifelse(data4Volcano$padj>significance_threshold,"non-significant","significant") +data4Volcano$threshold_raw <- ifelse(data4Volcano$pvalue>significance_threshold,"non-significant","significant") +data4Volcano$threshold_fc <- ifelse( + data4Volcano$log2FoldChange>lfc_threshold, + "up-regulated", + ifelse( + data4Volcano$log2FoldChange<(-1*as.numeric(lfc_threshold)), + "down-regulated", " " + ) +) +data4Volcano$combined <- paste0(data4Volcano$threshold," + ",data4Volcano$threshold_fc) +data4Volcano$combined_raw <- paste0(data4Volcano$threshold_raw," + ",data4Volcano$threshold_fc) +colorScheme2 <- c("#cf0e5bCD", "#0e5bcfCD", "#939596CD","#cf0e5b1A", "#0e5bcf1A", "#9395961A") +names(colorScheme2) <- c( + "significant + up-regulated", "significant + down-regulated", "significant + ", + "non-significant + up-regulated", "non-significant + down-regulated", "non-significant + " +) + +# remove NA values +data4Volcano <- data4Volcano[complete.cases(data4Volcano),] + + +Volcano_plot <- ggplot( + data4Volcano, + aes(label=probename) +) + + geom_point(aes( + x = log2FoldChange, + y = -log10(padj), + colour = combined + )) + + geom_hline( + yintercept = -1*(log10(as.numeric(significance_threshold))), + color="lightgrey" + ) + + geom_vline( + xintercept = c((-1*as.numeric(lfc_threshold)),as.numeric(lfc_threshold)), + color="lightgrey" + ) + + scale_color_manual(values=colorScheme2, name="") + + xlab("Log FoldChange") + + ylab("-log10(p_adj-value)") + + theme(legend.position = "none") + + CUSTOM_THEME + + ggtitle(label="Corrected p-Values") +lapply(ls(pattern='plot'), get) +``` + +## Additional Information + +The code also contains a lot of comments intended to help you understand the code. +These comments are marked with `#`. Additionally, some upper case variables are used, +these are constants that are used in subsequent parts of the code. They help in making +code more readable and easier to adjust. A good example is the `CUSTOM_THEME` variable +that is used in the ggplot2 visualization. This variable is defined as: + +```r +# Setting default options +CUSTOM_THEME <- theme_bw(base_size = 15) + + theme( + axis.title = element_text(size = 15), # Axis labels + axis.text = element_text(size = 15), # Axis tick labels + legend.text = element_text(size = 15), # Legend text + legend.title = element_text(size = 15), # Legend title + plot.title = element_text(size = 17, face = 'bold') # Plot title + ) +``` \ No newline at end of file diff --git a/docs/interface-details.md b/docs/interface-details.md index 34bac085..681d03be 100644 --- a/docs/interface-details.md +++ b/docs/interface-details.md @@ -47,8 +47,8 @@ To get more information on the tabs, navigate to the respective documentation us
  • Main Panel Structure: Each main panel contains the visualization of the analysis results. Some panels are further subdivided to show multiple results, for example, the Significance Analysis tab.
  • Picture Download Options: Users can download visualizations and results in common formats (e.g., PNG, TIFF, PDF). There are respective buttons to select the file format. Upon 'Save plot' the file is downloaded to the local machine.
  • Sending Visualizations to Report: Upon button click on 'Send only to report' the current shown visualization and associated parameters are saved to the Report. Hence, you can play around with parameters and can save the one of interest to you without cluttering the report with all tried options.
  • -
  • Get Underlying R Code and Data: Upon button click, the R script and respective data to generate shown plot will be presented for download. The script includes the Data selection, pre-processing as well as the respective analysis. For more details make sure to check out [Code and Data](code-and-data.md).
  • -
  • Notes: At the bottom of each tab you can find the Notes field - here you can enter text which will be saved within the report. You can use [markdown syntax](https://www.markdownguide.org/cheat-sheet/) here.
  • +
  • Get Underlying R Code and Data: Upon button click, the R script and respective data to generate shown plot will be presented for download. The script includes the Data selection, pre-processing as well as the respective analysis. For more details make sure to check out Code and Data.
  • +
  • Notes: At the bottom of each tab you can find the Notes field - here you can enter text which will be saved within the report. You can use markdown syntax here.
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