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XDec_total_code_vpetrosyan.Rmd
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---
title: "XDec_BRCA_deconvolution"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load libraries}
#Note - loading ggtern will disable features in ggplots 2 - load later
library(ggalluvial)
library(gplots)
library(EDec)
library(clue)
library(quadprog)
library(reshape)
library(reshape2)
library(ggfortify)
library(dplyr)
library(tidyr)
library(survival)
library(survminer)
library(VennDiagram)
library(corrplot)
library(RColorBrewer)
library(pvclust)
library(alluvial)
library(ggplot2)
library("recount")
library(caret)
library(plotROC)
library(pROC)
library(progeny)
lmp <- function (modelobject) {
if (class(modelobject) != "lm") stop("Not an object of class 'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)
}
quantile_normalisation = function(df){
df_rank <- apply(df,2,rank,ties.method="min")
df_sorted <- data.frame(apply(df, 2, sort))
df_mean <- apply(df_sorted, 1, mean)
index_to_mean <- function(my_index, my_mean){
return(my_mean[my_index])
}
df_final <- apply(df_rank, 2, index_to_mean, my_mean=df_mean)
rownames(df_final) <- rownames(df)
return(df_final)
}
logistic.a100 = function(x){
a = 1/quantile(x, 1.00)
1 - exp(1)^(-a*x)
}
multiplot = function(..., plotlist = NULL, file, cols = 1, layout = NULL) {
require(grid)
plots <- c(list(...), plotlist)
numPlots = length(plots)
if (is.null(layout)) {
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots == 1) {
print(plots[[1]])
} else {
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
for (i in 1:numPlots) {
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
removeEMPTYstrings = function(x) {
newVectorWOstrings <- x[x != ""]
return(newVectorWOstrings)
}
source("~/Documents/XDec/sources/createSimulatedMixtures.R")
source("~/Documents/XDec/sources/PAM50ExerciseFunctions.R")
source("~/Documents/XDec/sources/diffExpEDec.R")
my_palette_1 = colorRampPalette(c("white", "lightgreen", "darkgreen"))(n = 59)
col_breaks_1 = c(seq(0.00, 0.33999, length = 20), # white
seq(0.34, 0.66999, length = 20), # lightgreen
seq(0.67, 1.00000, length = 20)) # darkgreen
my_palette_2 = colorRampPalette(c("white", "lightgreen", "darkgreen"))(n = 59)
col_breaks_2 = c(seq(0.50, 0.6699, length = 20), # white
seq(0.67, 0.8399, length = 20), # lightgreen
seq(0.84, 1.0000, length = 20)) # darkgreen
my_palette_3 = colorRampPalette(c("yellow","orange","red"))(n = 59)
col_breaks_3 = c(seq(0.0000,1.9999, length = 20), # for yellow
seq(2.0000,3.9999, length = 20), # for orange
seq(4.0000,6.0000, length = 20)) # for red
```
```{r GEO cell line refs}
GEO.Beta = read.table("GEO_Refs_BetaValues.txt", header = TRUE, check.names = FALSE, sep = "\t", row.names = 1)
GEO.Beta = GEO.Beta[rowSums(GEO.Beta) > 0,]
GEO.Metadata = read.table("GEO_Refs_Metadata.txt", header = TRUE, check.names = FALSE, sep = "\t", row.names = NULL)
GEO.Variance.Cancer = read.table("GEO_Refs_Variance_Cancer.txt", header = TRUE, check.names = FALSE, sep = "\t", row.names = 1)
CancerEpithelial.Profiles = as.vector(GEO.Metadata[GEO.Metadata$Cell_Type == "cancer_epithelial",]$Sample_ID)
NormalEpithelial.Profiles = as.vector(GEO.Metadata[GEO.Metadata$Cell_Type == "normal_epithelial",]$Sample_ID)
Stromal.Profiles = as.vector(GEO.Metadata[GEO.Metadata$Cell_Type == "stromal",]$Sample_ID)
Immune.Profiles = as.vector(GEO.Metadata[GEO.Metadata$Cell_Type == "immune",]$Sample_ID)
B.Cell.Profiles = as.vector(GEO.Metadata[GEO.Metadata$Cell_Subtype == "B_cells",]$Sample_ID)
DC.Profiles = as.vector(GEO.Metadata[GEO.Metadata$Cell_Subtype == "DC",]$Sample_ID)
Monocytes.Profiles = as.vector(GEO.Metadata[GEO.Metadata$Cell_Subtype == "monocytes",]$Sample_ID)
NK.Profiles = as.vector(GEO.Metadata[GEO.Metadata$Cell_Subtype == "CD56+_NK",]$Sample_ID)
T.Cells.Profiles = as.vector(GEO.Metadata[GEO.Metadata$Cell_Subtype == "T_cells",]$Sample_ID)
```
```{r read in the TCGA data and EDec proportions}
TCGA.Beta = read.table("HiSeqV2_TCGA.txt", header = TRUE, check.names = FALSE, sep = "\t", row.names = 1)
TCGA.Beta = (2^TCGA.Beta)-1
TCGA.Beta = TCGA.Beta[rowSums(TCGA.Beta) > 0,]
TCGA.Metadata = read.table("BRCA_clinicalMatrix_New.txt", header = TRUE, check.names = FALSE, sep = "\t", row.names = NULL)
TCGA.Metadata = TCGA.Metadata[TCGA.Metadata$sampleID %in% colnames(TCGA.Beta),]
TCGA.Metadata = TCGA.Metadata[match(colnames(TCGA.Beta), TCGA.Metadata$sampleID),]
EDec.Proportions = read.table("TCGA_VitorProportions.txt", header = TRUE, check.names = FALSE, sep = "\t", row.names = 1)
interTCGA.samples = intersect(rownames(EDec.Proportions), colnames(TCGA.Beta))
EDec.Proportions = EDec.Proportions[interTCGA.samples,]
EDec.Proportions$Cancer = EDec.Proportions$`Cancerous Epithelial 1` + EDec.Proportions$`Cancerous Epithelial 2` + EDec.Proportions$`Cancerous Epithelial 3` + EDec.Proportions$`Cancerous Epithelial 4` + EDec.Proportions$`Cancerous Epithelial 5`
EDec.Proportions$`Cancerous Epithelial 1` = NULL
EDec.Proportions$`Cancerous Epithelial 2` = NULL
EDec.Proportions$`Cancerous Epithelial 3` = NULL
EDec.Proportions$`Cancerous Epithelial 4` = NULL
EDec.Proportions$`Cancerous Epithelial 5` = NULL
EDec.Proportions$Samples = rownames(EDec.Proportions)
colnames(EDec.Proportions) = c("Epithelial","Stromal","Immune","Cancer","Samples")
```
```{r load TCGA metadata}
TCGA.Metadata.subtypes = as.character(TCGA.Metadata$PAM50Call_New)
TCGA.table.colors = rep("white", length(TCGA.Metadata.subtypes))
TCGA.table.colors[TCGA.Metadata.subtypes=="Control"] = "pink"
TCGA.table.colors[TCGA.Metadata.subtypes=="Normal"] = "purple"
TCGA.table.colors[TCGA.Metadata.subtypes=="Basal"] = "red"
TCGA.table.colors[TCGA.Metadata.subtypes=="LumA"] = "green"
TCGA.table.colors[TCGA.Metadata.subtypes=="LumB"] = "yellow"
TCGA.table.colors[TCGA.Metadata.subtypes=="HER2"] = "blue"
TCGA.Control.samples = as.character(TCGA.Metadata[TCGA.Metadata$PAM50Call_New == "Control",]$sampleID)
TCGA.Normal.samples = as.character(TCGA.Metadata[TCGA.Metadata$PAM50Call_New == "Normal",]$sampleID)
TCGA.Basal.samples = as.character(TCGA.Metadata[TCGA.Metadata$PAM50Call_New == "Basal",]$sampleID)
TCGA.LumA.samples = as.character(TCGA.Metadata[TCGA.Metadata$PAM50Call_New == "LumA",]$sampleID)
TCGA.LumB.samples = as.character(TCGA.Metadata[TCGA.Metadata$PAM50Call_New == "LumB",]$sampleID)
TCGA.Her2.samples = as.character(TCGA.Metadata[TCGA.Metadata$PAM50Call_New == "Her2",]$sampleID)
```
```{r GEO refs normalization with TCGA}
GEO.TCGA.genes = intersect(rownames(GEO.Beta), rownames(TCGA.Beta))
TCGA.Beta = TCGA.Beta[GEO.TCGA.genes,]
TCGA.Beta = as.data.frame(TCGA.Beta)
TCGA.Beta$Genes = rownames(TCGA.Beta)
GEO.Beta = GEO.Beta[GEO.TCGA.genes,]
GEO.Beta = as.data.frame(GEO.Beta)
GEO.Beta$Genes = rownames(GEO.Beta)
input.allrnaseq = merge(GEO.Beta, TCGA.Beta, by = "Genes")
rownames(input.allrnaseq) = input.allrnaseq$Genes
input.allrnaseq$Genes = NULL
input.allrnaseq.trans = t(input.allrnaseq)
input.allrnaseq.trans.QN = as.data.frame(t(quantile_normalisation(input.allrnaseq.trans)))
input.allrnaseq.trans.QN.logistic = t(apply(t(input.allrnaseq.trans.QN), 2, logistic.a100))
input.allrnaseq.trans.QN.logistic.nonZero = input.allrnaseq.trans.QN.logistic[rowSums(input.allrnaseq.trans.QN.logistic[,]) > 0, ]
input.allrnaseq.trans.QN.logistic.nonZero.max = (1/max(input.allrnaseq.trans.QN.logistic.nonZero)) * input.allrnaseq.trans.QN.logistic.nonZero
TCGA.Beta$Genes = NULL
GEO.Beta$Genes = NULL
GEO.Beta.Trans = input.allrnaseq.trans.QN.logistic.nonZero.max[,colnames(GEO.Beta)]
TCGA.Beta.Trans = input.allrnaseq.trans.QN.logistic.nonZero.max[,colnames(TCGA.Beta)]
TCGA.Beta.Trans.DF = as.data.frame(TCGA.Beta.Trans)
TCGA.Beta.Trans.DF$average = rowMeans(TCGA.Beta.Trans.DF)
TCGA.Expressed = TCGA.Beta.Trans.DF[,"average", drop = FALSE]
TCGA.Expressed = TCGA.Expressed[TCGA.Expressed$average >= 0.01,,drop = FALSE]
TCGA.Expressed = rownames(TCGA.Expressed)
GEO.Beta.Trans = GEO.Beta.Trans[TCGA.Expressed,]
TCGA.Beta.Trans = TCGA.Beta.Trans[TCGA.Expressed,]
TCGA.Beta.GEO.Refs = cbind(TCGA.Beta.Trans, GEO.Beta.Trans[,c(NormalEpithelial.Profiles[1:4], Immune.Profiles[1:5], Stromal.Profiles)])
```
####Cell line deconvolution---
```{r Stage 0 - Cell lines}
GEO.Subtypes.Model = c("epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","B_cells","DC","monocytes","CD56+_NK","T_cells","B_cells","DC","monocytes","CD56+_NK","T_cells","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","epithelial","CAF","CAF","CAF","CAF")
GEO.Colors.Model = rep("white", length(GEO.Subtypes.Model))
GEO.Colors.Model[GEO.Subtypes.Model=="epithelial"] = "black"
GEO.Colors.Model[GEO.Subtypes.Model=="CAF"] = "green"
GEO.Colors.Model[GEO.Subtypes.Model=="B_cells"] = "blue"
GEO.Colors.Model[GEO.Subtypes.Model=="DC"] = "aquamarine"
GEO.Colors.Model[GEO.Subtypes.Model=="monocytes"] = "navy"
GEO.Colors.Model[GEO.Subtypes.Model=="CD56+_NK"] = "darkslategray2"
GEO.Colors.Model[GEO.Subtypes.Model=="T_cells"] = "royalblue3"
tTestOneVsRest.GEO.7 = perform_t_tests_all_classes_one_vs_rest(dataMatrix = GEO.Beta.Trans, classVector = GEO.Subtypes.Model7)
tTestEachPair.GEO.7 = perform_t_tests_all_classes_each_pair(dataMatrix = GEO.Beta.Trans, classVector = GEO.Subtypes.Model7)
chosenProbes.one.GEO.7 = c()
for(i in 1:7){
sigProbes.GEO.7 = row.names(tTestOneVsRest.GEO.7$P.Values[tTestOneVsRest.GEO.7$P.Values[,i] < 1e-4,])
sigDiff.GEO.7 = tTestOneVsRest.GEO.7$Difference.Between.Means[which(row.names(tTestOneVsRest.GEO.7$P.Values) %in% sigProbes.GEO.7), i]
highDiffProbes.GEO.7 = names(tail(sort(sigDiff.GEO.7), 25))
lowDiffProbes.GEO.7 = names(head(sort(sigDiff.GEO.7), 25))
chosenProbes.one.GEO.7 = c(chosenProbes.one.GEO.7, highDiffProbes.GEO.7, lowDiffProbes.GEO.7)
print(c(highDiffProbes.GEO.7, lowDiffProbes.GEO.7))
}
id.one.GEO.7 = duplicated(chosenProbes.one.GEO.7)
chosenProbes.one.dup.GEO.7 = chosenProbes.one.GEO.7[!id.one.GEO.7]
# Epithelial vs CAF
chosenProbes.pair.GEO.7 = c()
for(j in c(6)){
sigProbes.pair.GEO.7 = row.names(tTestEachPair.GEO.7$P.Values[tTestEachPair.GEO.7$P.Values[,j] < 1e-5,])
sigDiff.pair.GEO.7 = tTestEachPair.GEO.7$Difference.Between.Means[which(row.names(tTestEachPair.GEO.7$P.Values) %in% sigProbes.pair.GEO.7), j]
highDiffProbes.pair.GEO.7 = names(tail(sort(sigDiff.pair.GEO.7), 75))
lowDiffProbes.pair.GEO.7 = names(head(sort(sigDiff.pair.GEO.7), 75))
chosenProbes.pair.GEO.7 = c(chosenProbes.pair.GEO.7 , highDiffProbes.pair.GEO.7 , lowDiffProbes.pair.GEO.7 )
}
id.pair.GEO.7 = duplicated(chosenProbes.pair.GEO.7)
chosenProbes.pair.dup.GEO.7 = chosenProbes.pair.GEO.7[!id.pair.GEO.7]
chosenProbes.GEO.7 = union(chosenProbes.one.dup.GEO.7, chosenProbes.pair.dup.GEO.7)
GEO.7.Beta.Trans.chosenProbes = GEO.Beta.Trans[chosenProbes.GEO.7,]
```
```{r plot Stage 0 - Cell lines}
heatmap.2(as.matrix(GEO.7.Beta.Trans.chosenProbes),
trace = "none", col = my_palette_1, breaks = col_breaks_1, key = FALSE, labRow = FALSE,
ColSideColors = GEO.Colors.Model, labCol = colnames(GEO.7.Beta.Trans.chosenProbes), margins = c(10,10))
```
```{r Stage 1 - Cell lines}
Pam50.Genes = c("UBE2T","BIRC5","NUF2","CDC6","CCNB1","TYMS","MYBL2","CEP55","MELK","NDC80","RRM2","UBE2C","CENPF","PTTG1","EXO1","ANLN","CCNE1","CDC20","MKI67","KIF2C","ACTR3B","MYC","EGFR","KRT5","PHGDH","CDH3","MIA","KRT17","FOXC1","SFRP1","KRT14","ESR1","SLC39A6","BAG1","MAPT","PGR","CXXC5","MLPH","BCL2","MDM2","NAT1","FOXA1","BLVRA","MMP11","GPR160","FGFR4","GRB7","TMEM45B","ERBB2")
chosenProbes.GEO.7.PAM50 = c(chosenProbes.GEO.7, Pam50.Genes)
set.seed(12345)
stability.TCGA.SC.Model = estimate_stability(TCGA.Beta.GEO.Refs, chosenProbes.GEO.7.PAM50, k.cells, subset_prop = 0.8, num_subsets = 3, reps_per_subset = 3, max_its = 2000, rss_diff_stop = 1e-10)
sc.num = 9
TCGA.BRCA.k.cell.lines = 6
XDec.TCGA.cell.lines = run_edec_stage_1(meth_bulk_samples = TCGA.Beta.GEO.Refs, informative_loci = chosenProbes.GEO.7.PAM50, num_cell_types = TCGA.BRCA.k.cell.lines, max_its = 2000, rss_diff_stop = 1e-10)
XDec.TCGA.cell.lines.norm.gene = XDec.TCGA.cell.lines$methylation
XDec.TCGA.cell.lines.norm.gene.probes = XDec.TCGA.cell.lines.norm.gene[chosenProbes.GEO.7,]
XDec.TCGA.cell.lines.norm.gene.names = NULL
for(i in 1:TCGA.BRCA.k){XDec.TCGA.cell.lines.norm.gene.names = c(XDec.TCGA.cell.lines.norm.gene.names, paste("Profile_",i,sep = ""))}
colnames(XDec.TCGA.cell.lines.norm.gene.probes) = XDec.TCGA.cell.lines.norm.gene.names
Corr.TCGA.probes.cell.lines = cor(GEO.7.Beta.Trans.chosenProbes, XDec.TCGA.cell.lines.norm.gene.probes, use = "complete", method = "spearman")
Prop.TCGA.cell.lines = as.data.frame(XDec.TCGA.cell.lines$proportions)[colnames(TCGA.Beta),]
colnames(Prop.TCGA.cell.lines) = XDec.TCGA.cell.lines.norm.gene.names
```
```{r plot Stage 1 - Cell lines}
heatmap.2(as.matrix(Corr.TCGA.probes.cell.lines),
trace = "none", col = my_palette_1, breaks = col_breaks_1, RowSideColors = GEO.Colors.Model, key = FALSE,labCol = colnames(XDec.TCGA.cell.lines.norm.gene.probes), cexCol = 2, Colv = TRUE, margins = c(10,10))
#This plot allows us to assign cell types to profiles
```
```{r assign cell types - Cell lines}
colnames(Corr.TCGA.probes.cell.lines) = c("epithelial_1","myeliod","stromal","epithelial_2","lymphoid","epithelial_3")
colnames(XDec.TCGA.cell.lines.norm.gene.probes) = c("epithelial_1","myeliod","stromal","epithelial_2","lymphoid","epithelial_3")
colnames(Prop.TCGA.cell.lines.subset) = c("epithelial_1","myeliod","stromal","epithelial_2","lymphoid","epithelial_3")
```
```{r TCGA Metadata}
TCGA.Metadata.Subset = TCGA.Metadata
TCGA.Metadata.Subset = TCGA.Metadata.Subset[!TCGA.Metadata.Subset$PAM50Call_New == '',]
TCGA.Metadata.Subset.Samples = as.character(TCGA.Metadata.Subset$sampleID)
Prop.TCGA.cell.lines.subset = Prop.TCGA.cell.lines[TCGA.Metadata.Subset.Samples,]
TCGA.Metadata.subtypes.subset = as.character(TCGA.Metadata.Subset$PAM50Call_New)
TCGA.table.colors = rep("white", length(TCGA.Metadata.subtypes.subset))
TCGA.table.colors[TCGA.Metadata.subtypes.subset=="Control"] = "pink"
TCGA.table.colors[TCGA.Metadata.subtypes.subset=="Normal"] = "purple"
TCGA.table.colors[TCGA.Metadata.subtypes.subset=="Basal"] = "red"
TCGA.table.colors[TCGA.Metadata.subtypes.subset=="LumA"] = "green"
TCGA.table.colors[TCGA.Metadata.subtypes.subset=="LumB"] = "yellow"
TCGA.table.colors[TCGA.Metadata.subtypes.subset=="Her2"] = "blue"
TCGA.Metadata.Her2.subset = as.character(TCGA.Metadata.Subset$HER2_Final_Status_nature2012)
TCGA.table.colors.2 = rep("white", length(TCGA.Metadata.Her2.subset))
TCGA.table.colors.2[TCGA.Metadata.Her2.subset=="Negative"] = "black"
TCGA.table.colors.2[TCGA.Metadata.Her2.subset=="Positive"] = "red"
```
```{r plot proportions - Cell lines}
heatmap.2(as.matrix(t(Prop.TCGA.cell.lines.subset)),
trace = "none", col = my_palette_1, breaks = col_breaks_1, ColSideColors = TCGA.table.colors, key = FALSE,labCol = FALSE, cexRow = 2, Colv = TRUE, margins = c(10,10))
```
```{r compare XDec cell lines and EDec}
Prop.TCGA.cell.lines.subset.EDec = Prop.TCGA.cell.lines.subset[interTCGA.samples,]
Prop.TCGA.cell.lines.subset.EDec$Samples = rownames(Prop.TCGA.cell.lines.subset.EDec)
colnames(EDec.Proportions)[1:4] = paste("EDec",colnames(EDec.Proportions)[1:4],sep = "_")
Prop.TCGA.cell.lines.subset.EDec.All = merge(Prop.TCGA.cell.lines.subset.EDec, EDec.Proportions, by = "Samples")
XDec.Stroma = Prop.TCGA.cell.lines.subset.EDec.All$stromal
EDec.Stroma = Prop.TCGA.cell.lines.subset.EDec.All$EDec_Stromal
XDec.Immune = Prop.TCGA.cell.lines.subset.EDec.All$myeliod + Prop.TCGA.cell.lines.subset.EDec.All$lymphoid
EDec.Immune = Prop.TCGA.cell.lines.subset.EDec.All$EDec_Immune
XDec.Epi = Prop.TCGA.cell.lines.subset.EDec.All$epithelial_1 + Prop.TCGA.cell.lines.subset.EDec.All$epithelial_2 + Prop.TCGA.cell.lines.subset.EDec.All$epithelial_3
EDec.Epi = Prop.TCGA.cell.lines.subset.EDec.All$EDec_Cancer + Prop.TCGA.cell.lines.subset.EDec.All$EDec_Epithelial
XDec.EDec.Stroma.lm = lm(XDec.Stroma ~ -1 + EDec.Stroma)
XDec.EDec.Immune.lm = lm(XDec.Immune ~ -1 + EDec.Immune)
XDec.EDec.Epi.lm = lm(XDec.Epi ~ -1 + EDec.Epi)
```
```{r plot correlation EDec/XDec cell lines}
plot(Prop.TCGA.cell.lines.subset.EDec.All$EDec_Stromal, Prop.TCGA.cell.lines.subset.EDec.All$stromal, xlim = c(0,1), ylim = c(0,1), xlab = "EDec proportions (%)", ylab = "XDec proportions (%)", col = "green", pch = 20)
abline(XDec.EDec.Stroma.lm, col = "green")
points(Prop.TCGA.cell.lines.subset.EDec.All$EDec_Immune, Prop.TCGA.cell.lines.subset.EDec.All$myeliod + Prop.TCGA.cell.lines.subset.EDec.All$lymphoid, xlim = c(0,1), ylim = c(0,1), main = "Immune", xlab = "EDec proportions (%)", ylab = "XDec proportions (%)", col = "blue", pch = 20)
abline(XDec.EDec.Immune.lm, col = "blue")
points(Prop.TCGA.cell.lines.subset.EDec.All$EDec_Cancer + Prop.TCGA.cell.lines.subset.EDec.All$EDec_Epithelial, Prop.TCGA.cell.lines.subset.EDec.All$epithelial_1 + Prop.TCGA.cell.lines.subset.EDec.All$epithelial_2 + Prop.TCGA.cell.lines.subset.EDec.All$epithelial_3, xlim = c(0,1), ylim = c(0,1), main = "Epithelial", xlab = "EDec proportions (%)", ylab = "XDec proportions (%)", col = "black", pch = 20)
abline(XDec.EDec.Epi.lm, col = "black")
```
###Single Cell Deconvolution Stage 0 and 1 -----
```{r preprocess scRNA references}
GSE118389.RSEM = read.table(file = "GSE118389_counts_rsem.txt", quote = "\"", sep = "\t", check.names = FALSE, comment.char = "!", header = TRUE, row.names = 1)
GSE118389.CellType.Meta = read.table(file = "SingleCell/GSE118389_Metadata.txt", quote = "\"", sep = "\t", check.names = FALSE, comment.char = "!", header = TRUE, row.names = 1)
GSE118389.CellType.Epithelial = as.vector(GSE118389.CellType.Meta[GSE118389.CellType.Meta$Cell_Type == "Epithelial",]$Sample)
GSE118389.CellType.Stroma = as.vector(GSE118389.CellType.Meta[GSE118389.CellType.Meta$Cell_Type == "Stroma",]$Sample)
GSE118389.CellType.Endothelial = as.vector(GSE118389.CellType.Meta[GSE118389.CellType.Meta$Cell_Type == "Endothelial",]$Sample)
GSE118389.CellType.Immune = as.vector(GSE118389.CellType.Meta[GSE118389.CellType.Meta$Cell_Type == "Immune",]$Sample)
GSE118389.CellType.Tcell = as.vector(GSE118389.CellType.Meta[GSE118389.CellType.Meta$Cell_Subtype == "Tcell",]$Sample)
GSE118389.CellType.Bcell = as.vector(GSE118389.CellType.Meta[GSE118389.CellType.Meta$Cell_Subtype == "Bcell",]$Sample)
GSE118389.CellType.Macrophage = as.vector(GSE118389.CellType.Meta[GSE118389.CellType.Meta$Cell_Subtype == "macrophage",]$Sample)
GSE118389.Markers = c("EPCAM","EGFR","CDH1","KRT14","ITGA6","KRT5","TP63","KRT17","MME","KRT8","KRT18","KRT19","FOXA1","GATA3","MUC1","CD24","KIT","GABRP","FAP","COL1A1","COL3A1","COL5A1","ACTA2","TAGLN","LUM","FBLN1","COL6A3","COL1A2","COL6A1","COL6A2","PECAM1","VWF","CDH5","SELE","PTPRC","CD2","CD3D","CD3E","CD3G","CD8A","CD8B","MS4A1","CD79A","CD79B","BLNK","CD14","CD68","CD163","CSF1R")
## Reorder Epithelial based on library size and sum every 10 samples
GSE118389.RSEM.Epithelial = GSE118389.RSEM[,GSE118389.CellType.Epithelial]
Less100K.Epithelial = colSums(GSE118389.RSEM.Epithelial) > 100000
GSE118389.RSEM.Epithelial = GSE118389.RSEM.Epithelial[,Less100K.Epithelial]
#Remove with low coverage
GSE118389.RSEM.Epithelial = GSE118389.RSEM.Epithelial[,names(sort(colSums(GSE118389.RSEM.Epithelial), decreasing = TRUE))]
GSE118389.RSEM.Epithelial = GSE118389.RSEM.Epithelial[,1:640]
GSE118389.RSEM.Epithelial.Sum = t(sapply(seq(1,ncol(GSE118389.RSEM.Epithelial), by = 5), function(i) {
indx <- i:(i+4)
rowSums(GSE118389.RSEM.Epithelial[indx[indx <= ncol(GSE118389.RSEM.Epithelial)]])}))
GSE118389.RSEM.Epithelial.Sum = as.data.frame(t(GSE118389.RSEM.Epithelial.Sum))
names.Epithelial = NULL
for(i in 1:length(colnames(GSE118389.RSEM.Epithelial.Sum))){
names.Epithelial.temp = paste("Epithelial.P", i, sep = "")
names.Epithelial = c(names.Epithelial, names.Epithelial.temp)
}
colnames(GSE118389.RSEM.Epithelial.Sum) = names.Epithelial
## Reorder Stroma based on library size and sum every 5 samples
GSE118389.RSEM.Stroma = GSE118389.RSEM[,GSE118389.CellType.Stroma]
Less100K.Stroma = colSums(GSE118389.RSEM.Stroma) > 100000
GSE118389.RSEM.Stroma = GSE118389.RSEM.Stroma[,Less100K.Stroma]
GSE118389.RSEM.Stroma = GSE118389.RSEM.Stroma[,names(sort(colSums(GSE118389.RSEM.Stroma), decreasing = TRUE))]
GSE118389.RSEM.Stroma = GSE118389.RSEM.Stroma[,1:90]
GSE118389.RSEM.Stroma.Sum = t(sapply(seq(1,ncol(GSE118389.RSEM.Stroma), by = 5), function(i) {
indx <- i:(i+4)
rowSums(GSE118389.RSEM.Stroma[indx[indx <= ncol(GSE118389.RSEM.Stroma)]])}))
GSE118389.RSEM.Stroma.Sum = as.data.frame(t(GSE118389.RSEM.Stroma.Sum))
names.Stroma = NULL
for(i in 1:length(colnames(GSE118389.RSEM.Stroma.Sum))){
names.Stroma.temp = paste("Stroma.P", i, sep = "")
names.Stroma = c(names.Stroma, names.Stroma.temp)
}
colnames(GSE118389.RSEM.Stroma.Sum) = names.Stroma
## Reorder Endothelial based on library size and sum every 10 samples
GSE118389.RSEM.Endothelial = GSE118389.RSEM[,GSE118389.CellType.Endothelial]
Less100K.Endothelial = colSums(GSE118389.RSEM.Endothelial) > 100000
GSE118389.RSEM.Endothelial = GSE118389.RSEM.Endothelial[,Less100K.Endothelial]
GSE118389.RSEM.Endothelial = GSE118389.RSEM.Endothelial[,names(sort(colSums(GSE118389.RSEM.Endothelial), decreasing = TRUE))]
GSE118389.RSEM.Endothelial = GSE118389.RSEM.Endothelial[,1:10]
GSE118389.RSEM.Endothelial.Sum = t(sapply(seq(1,ncol(GSE118389.RSEM.Endothelial), by = 5), function(i) {
indx <- i:(i+4)
rowSums(GSE118389.RSEM.Endothelial[indx[indx <= ncol(GSE118389.RSEM.Endothelial)]])}))
GSE118389.RSEM.Endothelial.Sum = as.data.frame(t(GSE118389.RSEM.Endothelial.Sum))
names.Endothelial = NULL
for(i in 1:length(colnames(GSE118389.RSEM.Endothelial.Sum))){
names.Endothelial.temp = paste("Endothelial.P", i, sep = "")
names.Endothelial = c(names.Endothelial, names.Endothelial.temp)
}
colnames(GSE118389.RSEM.Endothelial.Sum) = names.Endothelial
## Reorder Tcell based on library size and sum every 10 samples
GSE118389.RSEM.Tcell = GSE118389.RSEM[,GSE118389.CellType.Tcell]
Less100K.Tcell = colSums(GSE118389.RSEM.Tcell) > 100000
GSE118389.RSEM.Tcell = GSE118389.RSEM.Tcell[,Less100K.Tcell]
GSE118389.RSEM.Tcell = GSE118389.RSEM.Tcell[,names(sort(colSums(GSE118389.RSEM.Tcell), decreasing = TRUE))]
GSE118389.RSEM.Tcell = GSE118389.RSEM.Tcell[,1:50]
GSE118389.RSEM.Tcell.Sum = t(sapply(seq(1,ncol(GSE118389.RSEM.Tcell), by = 5), function(i) {
indx <- i:(i+4)
rowSums(GSE118389.RSEM.Tcell[indx[indx <= ncol(GSE118389.RSEM.Tcell)]])}))
GSE118389.RSEM.Tcell.Sum = as.data.frame(t(GSE118389.RSEM.Tcell.Sum))
names.Tcell = NULL
for(i in 1:length(colnames(GSE118389.RSEM.Tcell.Sum))){
names.Tcell.temp = paste("Tcell.P", i, sep = "")
names.Tcell = c(names.Tcell, names.Tcell.temp)
}
colnames(GSE118389.RSEM.Tcell.Sum) = names.Tcell
## Reorder Bcell based on library size and sum every 10 samples
GSE118389.RSEM.Bcell = GSE118389.RSEM[,GSE118389.CellType.Bcell]
Less100K.Bcell = colSums(GSE118389.RSEM.Bcell) > 100000
GSE118389.RSEM.Bcell = GSE118389.RSEM.Bcell[,Less100K.Bcell]
GSE118389.RSEM.Bcell = GSE118389.RSEM.Bcell[,names(sort(colSums(GSE118389.RSEM.Bcell), decreasing = TRUE))]
GSE118389.RSEM.Bcell = GSE118389.RSEM.Bcell[,1:15]
GSE118389.RSEM.Bcell.Sum = t(sapply(seq(1,ncol(GSE118389.RSEM.Bcell), by = 5), function(i) {
indx <- i:(i+4)
rowSums(GSE118389.RSEM.Bcell[indx[indx <= ncol(GSE118389.RSEM.Bcell)]])}))
GSE118389.RSEM.Bcell.Sum = as.data.frame(t(GSE118389.RSEM.Bcell.Sum))
names.Bcell = NULL
for(i in 1:length(colnames(GSE118389.RSEM.Bcell.Sum))){
names.Bcell.temp = paste("Bcell.P", i, sep = "")
names.Bcell = c(names.Bcell, names.Bcell.temp)
}
colnames(GSE118389.RSEM.Bcell.Sum) = names.Bcell
## Reorder Macrophage based on library size and sum every 10 samples
GSE118389.RSEM.Macrophage = GSE118389.RSEM[,GSE118389.CellType.Macrophage]
Less100K.Macrophage = colSums(GSE118389.RSEM.Macrophage) > 100000
GSE118389.RSEM.Macrophage = GSE118389.RSEM.Macrophage[,Less100K.Macrophage]
GSE118389.RSEM.Macrophage = GSE118389.RSEM.Macrophage[,names(sort(colSums(GSE118389.RSEM.Macrophage), decreasing = TRUE))]
GSE118389.RSEM.Macrophage = GSE118389.RSEM.Macrophage[,1:60]
GSE118389.RSEM.Macrophage.Sum = t(sapply(seq(1,ncol(GSE118389.RSEM.Macrophage), by = 5), function(i) {
indx <- i:(i+4)
rowSums(GSE118389.RSEM.Macrophage[indx[indx <= ncol(GSE118389.RSEM.Macrophage)]])}))
GSE118389.RSEM.Macrophage.Sum = as.data.frame(t(GSE118389.RSEM.Macrophage.Sum))
names.Macrophage = NULL
for(i in 1:length(colnames(GSE118389.RSEM.Macrophage.Sum))){
names.Macrophage.temp = paste("Macrophage.P", i, sep = "")
names.Macrophage = c(names.Macrophage, names.Macrophage.temp)
}
colnames(GSE118389.RSEM.Macrophage.Sum) = names.Macrophage
## Combine Sums
GSE118389.Combined = cbind(GSE118389.RSEM.Epithelial, GSE118389.RSEM.Stroma, GSE118389.RSEM.Tcell, GSE118389.RSEM.Macrophage)
GSE118389.CombinedSums = cbind(GSE118389.RSEM.Epithelial.Sum, GSE118389.RSEM.Stroma.Sum, GSE118389.RSEM.Tcell.Sum, GSE118389.RSEM.Macrophage.Sum)
GSE118389.CombinedSums = GSE118389.CombinedSums[rowSums(GSE118389.CombinedSums) > 0,]
plot(colSums(GSE118389.Combined))
## Normalized Counts
GSE118389.RSEM.Epithelial.Sum.Norm = GSE118389.RSEM.Epithelial.Sum
for(i in 1:length(colnames(GSE118389.RSEM.Epithelial.Sum.Norm))){
GSE118389.RSEM.Epithelial.Sum.Norm[i] = GSE118389.RSEM.Epithelial.Sum.Norm[i] * (colSums(GSE118389.RSEM.Epithelial.Sum.Norm)[1]/colSums(GSE118389.RSEM.Epithelial.Sum.Norm[i]))
}
GSE118389.RSEM.Stroma.Sum.Norm = GSE118389.RSEM.Stroma.Sum
for(i in 1:length(colnames(GSE118389.RSEM.Stroma.Sum.Norm))){
GSE118389.RSEM.Stroma.Sum.Norm[i] = GSE118389.RSEM.Stroma.Sum.Norm[i] * (colSums(GSE118389.RSEM.Epithelial.Sum.Norm)[1]/colSums(GSE118389.RSEM.Stroma.Sum.Norm[i]))
}
GSE118389.RSEM.Endothelial.Sum.Norm = GSE118389.RSEM.Endothelial.Sum
for(i in 1:length(colnames(GSE118389.RSEM.Endothelial.Sum.Norm))){
GSE118389.RSEM.Endothelial.Sum.Norm[i] = GSE118389.RSEM.Endothelial.Sum.Norm[i] * (colSums(GSE118389.RSEM.Epithelial.Sum.Norm)[1]/colSums(GSE118389.RSEM.Endothelial.Sum.Norm[i]))
}
GSE118389.RSEM.Tcell.Sum.Norm = GSE118389.RSEM.Tcell.Sum
for(i in 1:length(colnames(GSE118389.RSEM.Tcell.Sum.Norm))){
GSE118389.RSEM.Tcell.Sum.Norm[i] = GSE118389.RSEM.Tcell.Sum.Norm[i] * (colSums(GSE118389.RSEM.Epithelial.Sum.Norm)[1]/colSums(GSE118389.RSEM.Tcell.Sum.Norm[i]))
}
GSE118389.RSEM.Bcell.Sum.Norm = GSE118389.RSEM.Bcell.Sum
for(i in 1:length(colnames(GSE118389.RSEM.Bcell.Sum.Norm))){
GSE118389.RSEM.Bcell.Sum.Norm[i] = GSE118389.RSEM.Bcell.Sum.Norm[i] * (colSums(GSE118389.RSEM.Epithelial.Sum.Norm)[1]/colSums(GSE118389.RSEM.Bcell.Sum.Norm[i]))
}
GSE118389.RSEM.Macrophage.Sum.Norm = GSE118389.RSEM.Macrophage.Sum
for(i in 1:length(colnames(GSE118389.RSEM.Macrophage.Sum.Norm))){
GSE118389.RSEM.Macrophage.Sum.Norm[i] = GSE118389.RSEM.Macrophage.Sum.Norm[i] * (colSums(GSE118389.RSEM.Epithelial.Sum.Norm)[1]/colSums(GSE118389.RSEM.Macrophage.Sum.Norm[i]))
}
GSE118389.CombinedSums.Norm = cbind(GSE118389.RSEM.Epithelial.Sum.Norm, GSE118389.RSEM.Stroma.Sum.Norm, GSE118389.RSEM.Tcell.Sum.Norm, GSE118389.RSEM.Macrophage.Sum.Norm)
GSE118389.CombinedSums.Norm = GSE118389.CombinedSums.Norm[rowSums(GSE118389.CombinedSums.Norm) > 0,]
```
```{r normalize scRNA references}
GSE118389.Subtypes = gsub(pattern = "\\.P\\d+", replacement = "", colnames(GSE118389.CombinedSums.Norm))
GSE118389.Colors = rep("white", length(GSE118389.Subtypes))
GSE118389.Colors[GSE118389.Subtypes=="Epithelial"] = "black"
GSE118389.Colors[GSE118389.Subtypes=="Stroma"] = "green"
GSE118389.Colors[GSE118389.Subtypes=="Endothelial"] = "lightgreen"
GSE118389.Colors[GSE118389.Subtypes=="Tcell"] = "blue"
GSE118389.Colors[GSE118389.Subtypes=="Bcell"] = "lightblue"
GSE118389.Colors[GSE118389.Subtypes=="Macrophage"] = "navy"
input.SC.trans.QN.logistic = t(apply(t(GSE118389.CombinedSums.Norm), 2, logistic.a100))
input.SC.trans.QN.logistic.nonZero = input.SC.trans.QN.logistic[rowSums(input.SC.trans.QN.logistic[,]) > 0, ]
input.SC.trans.QN.logistic.nonZero.max = (1/max(input.SC.trans.QN.logistic.nonZero)) * input.SC.trans.QN.logistic.nonZero
GSE118389.CombinedSums.Norm.Trans = input.SC.trans.QN.logistic.nonZero.max
GSE118389.Markers.Int = intersect(GSE118389.Markers, rownames(GSE118389.CombinedSums.Norm.Trans))
GSE118389.CombinedSums.Norm.Trans.Markers = GSE118389.CombinedSums.Norm.Trans[GSE118389.Markers.Int,]
heatmap.2(as.matrix(GSE118389.CombinedSums.Norm.Trans.Markers),
trace = "none", col = my_palette_1, breaks = col_breaks_1, key = FALSE, Rowv = FALSE, Colv = FALSE,
labRow = rownames(GSE118389.CombinedSums.Norm.Trans.Markers), labCol = FALSE, margins = c(10,10))
#TSNE visualization of different cell types
norm_ref = GSE118389.CombinedSums.Norm.Trans[GSE118389.Markers.Int,]
norm_ref_t_ct = as.data.frame(t(norm_ref))
norm_ref_t_ct$cell_type = GSE118389.Subtypes
autoplot(prcomp(t(norm_ref)),
data = norm_ref_t_ct,
colour = 'cell_type',
label = FALSE,
main = "ReferencesSC")
```
```{r Stage 0 - Single Cell}
tTestOneVsRest.SC = perform_t_tests_all_classes_one_vs_rest(dataMatrix = GSE118389.CombinedSums.Norm.Trans, classVector = GSE118389.Subtypes)
tTestEachPair.SC = perform_t_tests_all_classes_each_pair(dataMatrix = GSE118389.CombinedSums.Norm.Trans, classVector = GSE118389.Subtypes)
chosenProbes.one.SC = c()
for(i in 1:4){
sigProbes.SC = row.names(tTestOneVsRest.SC$P.Values[tTestOneVsRest.SC$P.Values[,i] < 0.05,])
sigDiff.SC = tTestOneVsRest.SC$Difference.Between.Means[which(row.names(tTestOneVsRest.SC$P.Values) %in% sigProbes.SC), i]
highDiffProbes.SC = names(tail(sort(sigDiff.SC), 50))
lowDiffProbes.SC = names(head(sort(sigDiff.SC), 50))
chosenProbes.one.SC = c(chosenProbes.one.SC, highDiffProbes.SC, lowDiffProbes.SC)
}
id.one.SC = duplicated(chosenProbes.one.SC)
chosenProbes.one.dup.SC = chosenProbes.one.SC[!id.one.SC]
chosenProbes.pair.SC = c()
for(j in c(6)){
sigProbes.pair.SC = row.names(tTestEachPair.SC$P.Values[tTestEachPair.SC$P.Values[,j] < 0.05,])
sigDiff.pair.SC = tTestEachPair.SC$Difference.Between.Means[which(row.names(tTestEachPair.SC$P.Values) %in% sigProbes.pair.SC), j]
highDiffProbes.pair.SC = names(tail(sort(sigDiff.pair.SC), 25))
lowDiffProbes.pair.SC = names(head(sort(sigDiff.pair.SC), 25))
chosenProbes.pair.SC = c(chosenProbes.pair.SC , highDiffProbes.pair.SC , lowDiffProbes.pair.SC )
}
id.pair.SC <- duplicated(chosenProbes.pair.SC) | duplicated(chosenProbes.pair.SC, fromLast = TRUE)
chosenProbes.pair.dup.SC = chosenProbes.pair.SC[!id.pair.SC]
chosenProbes.pair.SC = c()
for(j in c(6)){
sigProbes.pair.SC = row.names(tTestEachPair.SC$P.Values[tTestEachPair.SC$P.Values[,j] < 0.05,])
sigDiff.pair.SC = tTestEachPair.SC$Difference.Between.Means[which(row.names(tTestEachPair.SC$P.Values) %in% sigProbes.pair.SC), j]
highDiffProbes.pair.SC = names(tail(sort(sigDiff.pair.SC), 25))
lowDiffProbes.pair.SC = names(head(sort(sigDiff.pair.SC), 25))
chosenProbes.pair.SC = c(chosenProbes.pair.SC , highDiffProbes.pair.SC , lowDiffProbes.pair.SC )
}
id.pair.SC <- duplicated(chosenProbes.pair.SC) | duplicated(chosenProbes.pair.SC, fromLast = TRUE)
chosenProbes.pair.dup.SC = chosenProbes.pair.SC[!id.pair.SC]
chosenProbes.SC = union(chosenProbes.one.dup.SC, chosenProbes.pair.dup.SC)
chosenProbes.SC = intersect(rownames(TCGA.Beta.Trans), chosenProbes.SC)
```
```{r plot Stage 0 - Single Cell}
Pam50.Genes = c("UBE2T","BIRC5","NUF2","CDC6","CCNB1","TYMS","MYBL2","CEP55","MELK","NDC80","RRM2","UBE2C","CENPF","PTTG1","EXO1","ANLN","CCNE1","CDC20","MKI67","KIF2C","ACTR3B","MYC","EGFR","KRT5","PHGDH","CDH3","MIA","KRT17","FOXC1","SFRP1","KRT14","ESR1","SLC39A6","BAG1","MAPT","PGR","CXXC5","MLPH","BCL2","MDM2","NAT1","FOXA1","BLVRA","MMP11","GPR160","FGFR4","GRB7","TMEM45B","ERBB2")
chosenProbes.SC.PAM50 = union(chosenProbes.SC, Pam50.Genes)
SC.Beta.Trans.chosenProbes = GSE118389.CombinedSums.Norm.Trans[chosenProbes.SC,]
heatmap.2(as.matrix(SC.Beta.Trans.chosenProbes),
trace = "none", col = my_palette_1, breaks = col_breaks_1, key = FALSE, labRow = FALSE,
ColSideColors = GSE118389.Colors, labCol = colnames(SC.Beta.Trans.chosenProbes), margins = c(10,10))
#write.table(chosenProbes.SC.PAM50,"single_cell_probes.txt")
#write.table(GSE118389.CombinedSums.Norm.Trans[chosenProbes.SC,],"single_cell_probes_exp.txt")
```
```{r Stage 1 - Single Cell}
k.cells = c(3:12)
set.seed(12345)
stability.TCGA.SC.Model = estimate_stability(TCGA.Beta.Trans, chosenProbes.SC.PAM50, k.cells, subset_prop = 0.8, num_subsets = 3, reps_per_subset = 3, max_its = 2000, rss_diff_stop = 1e-10)
sc.num = 9
XDec.TCGA.SC.Model = run_edec_stage_1(meth_bulk_samples = TCGA.Beta.Trans, informative_loci = chosenProbes.SC.PAM50, num_cell_types = sc.num, max_its = 2000, rss_diff_stop = 1e-10)
XDec.TCGA.SC.Model.norm.gene = XDec.TCGA.SC.Model$methylation
XDec.TCGA.SC.Model.norm.gene.chosenProbes = XDec.TCGA.SC.Model.norm.gene[chosenProbes.SC,]
XDec.TCGA.SC.Model.Names = NULL
for(i in 1:sc.num){XDec.TCGA.SC.Model.Names = c(XDec.TCGA.SC.Model.Names, paste("Profile_",i,sep = ""))}
colnames(XDec.TCGA.SC.Model.norm.gene.chosenProbes) = XDec.TCGA.SC.Model.Names
Corr.TCGA.SC.Model = cor(SC.Beta.Trans.chosenProbes, XDec.TCGA.SC.Model.norm.gene.chosenProbes, use = "complete", method = "spearman")
Prop.TCGA.SC.Model = as.data.frame(XDec.TCGA.SC.Model$proportions)[colnames(TCGA.Beta.Trans),]
colnames(Prop.TCGA.SC.Model) = XDec.TCGA.SC.Model.Names
Prop.TCGA.SC.Model.Subset = Prop.TCGA.SC.Model[TCGA.Metadata.Subset.Samples,]
```
```{r plot Stage 1 - Single cell }
heatmap.2(as.matrix(Corr.TCGA.SC.Model),
trace = "none", col = my_palette_1, breaks = col_breaks_1, RowSideColors = GSE118389.Colors, key = FALSE,
labCol = colnames(XDec.TCGA.SC.Model.norm.gene.chosenProbes), cexCol = 2, Colv = TRUE, margins = c(10,10))
heatmap.2(as.matrix(t(Prop.TCGA.SC.Model.Subset)),
trace = "none", col = my_palette_1, breaks = col_breaks_1, key = FALSE,
ColSideColors = TCGA.table.colors, labCol = FALSE, cexRow = 2, Colv = TRUE, margins = c(10,10))
#Boxplot to determine enrichment
Prop.TCGA.SC.Model.Subset.Box = Prop.TCGA.SC.Model.Subset
Prop.TCGA.SC.Model.Subset.Box$Type = TCGA.Metadata.subtypes.subset
Prop.TCGA.SC.Model.Subset.Box$Type = gsub(pattern = "Control", replacement = "1_Control", x = Prop.TCGA.SC.Model.Subset.Box$Type)
Prop.TCGA.SC.Model.Subset.Box$Type = gsub(pattern = "Normal", replacement = "2_Normal-like", x = Prop.TCGA.SC.Model.Subset.Box$Type)
Prop.TCGA.SC.Model.Subset.Box$Type = gsub(pattern = "Basal", replacement = "3_Basal", x = Prop.TCGA.SC.Model.Subset.Box$Type)
Prop.TCGA.SC.Model.Subset.Box$Type = gsub(pattern = "LumA", replacement = "4_LumA", x = Prop.TCGA.SC.Model.Subset.Box$Type)
Prop.TCGA.SC.Model.Subset.Box$Type = gsub(pattern = "LumB", replacement = "5_LumB", x = Prop.TCGA.SC.Model.Subset.Box$Type)
Prop.TCGA.SC.Model.Subset.Box$Type = gsub(pattern = "Her2", replacement = "6_Her2", x = Prop.TCGA.SC.Model.Subset.Box$Type)
Prop.TCGA.SC.Model.Subset.Box.Melt = melt(Prop.TCGA.SC.Model.Subset.Box)
ggplot(data = Prop.TCGA.SC.Model.Subset.Box.Melt, aes(x = Type, y = value)) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) +
geom_boxplot(aes(fill = variable), outlier.shape = NA) + scale_colour_manual(values = "black")
```
```{r assign cell types - Cell lines}
#profile 1 is epithelial basal
#profile 2 is T-cell
#profile 3 is adipoctye
#profile 4 is epithelial other
#profile 5 is macrophage
#profile 6 is CAF
#profile 7 is epithelial HER2
#profile 8 is epithelial control
#profile 9 is epithelial Luminal
sc.names = c("Epithelial.Basal","T-Cell","Adipocyte","Epithelial.Other","Macrophage", "CAF","Epithelial.Her2","Epithelial.Control","Epithelial.Luminal")
colnames(Prop.TCGA.SC.Model.Subset) = sc.names
colnames(Corr.TCGA.SC.Model) = sc.names
colnames(XDec.TCGA.SC.Model.norm.gene.chosenProbes) = sc.names
colnames(Prop.TCGA.SC.Model) = sc.names
```
###Single Cell Deconvolution Compare Proportions -----
```{r XDec vs EDec single cell}
interTCGA.SC.samples = intersect(colnames(TCGA.Beta.Trans), rownames(Prop.TCGA.SC.Model))
EDec.Proportions.SC = EDec.Proportions[interTCGA.SC.samples,]
EDec.Proportions.SC = na.omit(EDec.Proportions.SC)
Prop.TCGA.SC.Model.EDec = Prop.TCGA.SC.Model[interTCGA.SC.samples,]
Prop.TCGA.SC.Model.EDec$Samples = rownames(Prop.TCGA.SC.Model.EDec)
Prop.TCGA.SC.Model.EDec.All = merge(Prop.TCGA.SC.Model.EDec, EDec.Proportions, by = "Samples")
EDec.XDec.Epi = Prop.TCGA.SC.Model.EDec.All$EDec_Epithelial + Prop.TCGA.SC.Model.EDec.All$EDec_Cancer
XDec.Epi = Prop.TCGA.SC.Model.EDec.All$Epithelial.Basal + Prop.TCGA.SC.Model.EDec.All$Epithelial.Other + Prop.TCGA.SC.Model.EDec.All$Epithelial.Her2 + Prop.TCGA.SC.Model.EDec.All$Epithelial.Control + Prop.TCGA.SC.Model.EDec.All$Epithelial.Luminal
EDec.XDec.Immune = Prop.TCGA.SC.Model.EDec.All$EDec_Immune
XDec.Immune = Prop.TCGA.SC.Model.EDec.All$`T-Cell` + Prop.TCGA.SC.Model.EDec.All$Macrophage
EDec.XDec.Stroma = Prop.TCGA.SC.Model.EDec.All$EDec_Stromal
XDec.Stroma = Prop.TCGA.SC.Model.EDec.All$Adipocyte + Prop.TCGA.SC.Model.EDec.All$CAF
EDec.XDec.Epi.lm = lm(XDec.Epi ~ -1 + EDec.XDec.Epi)
summary(EDec.XDec.Epi.lm)
EDec.XDec.Immune.lm = lm(XDec.Immune ~ -1 + EDec.XDec.Immune)
summary(EDec.XDec.Immune.lm)
EDec.XDec.Stroma.lm = lm(XDec.Stroma ~ -1 + EDec.XDec.Stroma)
summary(EDec.XDec.Stroma.lm)
```
```{r plot correlation EDec/XDec single cell }
plot(EDec.XDec.Stroma,XDec.Stroma, xlim = c(0,1), ylim = c(0,1), xlab = "EDec proportions (%)", ylab = "XDec proportions (%)", col = "green", pch = 20)
abline(XDec.EDec.Stroma.lm, col = "green")
points(EDec.XDec.Immune,XDec.Immune, xlim = c(0,1), ylim = c(0,1), main = "Immune", xlab = "EDec proportions (%)", ylab = "XDec proportions (%)", col = "blue", pch = 20)
abline(XDec.EDec.Immune.lm, col = "blue")
points(EDec.XDec.Epi,XDec.Epi, xlim = c(0,1), ylim = c(0,1), main = "Epithelial", xlab = "EDec proportions (%)", ylab = "XDec proportions (%)", col = "black", pch = 20)
abline(XDec.EDec.Epi.lm, col = "black")
```
```{r compare to other deconvolution methods}
###FIGURE 2######
InfiniumPropsTable = read.table("BRCA_Infitium.txt", header = TRUE, check.names = FALSE, sep = "\t", row.names = NULL)
TimerPropsTable = read.table("immuneEstimation_Timer.txt", header = TRUE, check.names = FALSE, sep = "\t", row.names = 1)
TimerPropsTable$Sum = rowSums(TimerPropsTable)
TimerPropsTable$Samples = rownames(TimerPropsTable)
other_tools = read.csv("TIMER_estimation_for_tcga.csv")
#Xcell
xcell = other_tools[,c("cell_type","immune.score_XCELL","stroma.score_XCELL")]
xcell_ep = read.csv("xCell_TCGA_RSEM_all.csv")
xcell_ep = xcell_ep[xcell_ep$X %in% "Epithelial cells",,drop = FALSE]
xcell_ep = data.frame(t(xcell_ep))
colnames(xcell_ep) = xcell_ep[1,]
xcell_ep = xcell_ep[-1,,drop = FALSE]
xcell_ep$cell_type = rownames(xcell_ep)
xcell_ep$cell_type = gsub("\\.","-",xcell_ep$cell_type)
xcell = merge(xcell,xcell_ep, by = "cell_type")
colnames(xcell) = c("Samples","xcell_immune","xcell_stroma","xcell_epithelial")
epic = other_tools[,c("cell_type",colnames(other_tools)[grep("EPIC",colnames(other_tools))])]
epic$epic_immune = epic$B.cell_EPIC + epic$T.cell.CD4._EPIC + epic$T.cell.CD8._EPIC + epic$Macrophage_EPIC + epic$NK.cell_EPIC
hist(epic$epic_immune)
epic$epic_stromal = epic$Cancer.associated.fibroblast_EPIC + epic$Endothelial.cell_EPIC
epic$epic_cancer = epic$uncharacterized.cell_EPIC
epic = epic[,c("cell_type","epic_immune","epic_stromal","epic_cancer")]
colnames(epic)[1] = "Samples"
kassandra_prop = read.csv("kassandra_BRCA_BG_deconvolution.csv")
kassandra_prop_all = data.frame(t(kassandra_prop))
colnames(kassandra_prop_all) = kassandra_prop_all[1,]
kassandra_prop_all = kassandra_prop_all[-1,]
kassandra_prop_all_std = grep("std",colnames(kassandra_prop_all))
kassandra_prop_all = kassandra_prop_all[,-kassandra_prop_all_std]
kassandra_prop_all = kassandra_prop_all[,1:20]
kassandra_prop = kassandra_prop[37:39,]
kassandra_prop = data.frame(t(kassandra_prop))
colnames(kassandra_prop) = kassandra_prop[1,]
kassandra_prop = kassandra_prop[-1,]
kassandra_prop$Samples = rownames(kassandra_prop)
kassandra_prop$Samples = gsub("\\.","-",kassandra_prop$Samples)
Prop.TCGA.SC.Model.Stage2.general = Prop.TCGA.SC.Model.Stage2
Prop.TCGA.SC.Model.Stage2.general$Stroma = Prop.TCGA.SC.Model.Stage2.general$Adipocyte + Prop.TCGA.SC.Model.Stage2$CAF
Prop.TCGA.SC.Model.Stage2.general$Adipocyte = NULL
Prop.TCGA.SC.Model.Stage2.general$CAF = NULL
Prop.TCGA.SC.Model.Stage2.general$Samples = rownames(Prop.TCGA.SC.Model.Stage2.general)
colnames(Prop.TCGA.SC.Model.Stage2.general) = paste("XDec", colnames(Prop.TCGA.SC.Model.Stage2.general),sep = "_")
colnames(Prop.TCGA.SC.Model.Stage2.general)[4] = "Samples"
colnames(kassandra_prop) = paste("Kassandra",colnames(kassandra_prop), sep = "_")
colnames(kassandra_prop)[4] = "Samples"
kassandra_prop[,1:3] = sapply(kassandra_prop[,1:3], as.numeric)
XDec.kassandra = merge(Prop.TCGA.SC.Model.Stage2.general,kassandra_prop,by = "Samples", all.x = TRUE)
XDec.epic = merge(Prop.TCGA.SC.Model.Stage2.general,epic, by = "Samples")
XDec.xcell = merge(Prop.TCGA.SC.Model.Stage2.general,xcell, by = "Samples")
XDec.xcell$xcell_epithelial = as.numeric(XDec.xcell$xcell_epithelial)
CibersortPropsTable = read.table("SC_CibersortX.txt", header = TRUE, check.names = FALSE, sep = "\t", row.names = NULL)
XDec.Inf = merge(Prop.TCGA.SC.Model.EDec.All, InfiniumPropsTable, by = "Samples", all.x = TRUE)
XDec.TIMER = merge(Prop.TCGA.SC.Model.EDec.All, TimerPropsTable, by = "Samples", all.x = TRUE)
XDec.TIMER = na.omit(XDec.TIMER)
XDec.Cibersort = merge(Prop.TCGA.SC.Model.EDec.All, CibersortPropsTable, by = "Samples", all.x = TRUE)
Infinium.Props = XDec.Inf$Purity_InfiniumPurify
XDec.Purity = XDec.Inf$Epithelial.Basal + XDec.Inf$Epithelial.Other + XDec.Inf$Epithelial.Her2 + XDec.Inf$Epithelial.Control + XDec.Inf$Epithelial.Luminal
XDec.Purity.lm = lm(XDec.Purity ~ -1 + Infinium.Props)
summary(XDec.Purity.lm)
Timer.Props = XDec.TIMER$Sum
XDec.Immune.Timer = XDec.TIMER$`T-Cell` + XDec.TIMER$Macrophage.x
XDec.Timer.lm = lm(XDec.Immune.Timer ~ -1 + Timer.Props)
summary(XDec.Timer.lm)
CIBERSORT.Stroma = XDec.Cibersort$stroma + XDec.Cibersort$endothelial
XDec.Stroma = XDec.Cibersort$Adipocyte + XDec.Cibersort$CAF
CIBERSORT.Immune = XDec.Cibersort$Tcell + XDec.Cibersort$Bcell + XDec.Cibersort$macrophage
XDec.Immune = XDec.Cibersort$`T-Cell` + XDec.Cibersort$Macrophage
CIBERSORT.Epi = XDec.Cibersort$epithelial
XDec.Epi = XDec.Cibersort$Epithelial.Basal + XDec.Cibersort$Epithelial.Other + XDec.Cibersort$Epithelial.Her2 + XDec.Cibersort$Epithelial.Control + XDec.Cibersort$Epithelial.Luminal
XDec.CIBERSORT.Stroma.lm = lm(XDec.Stroma ~ -1 + CIBERSORT.Stroma)
XDec.CIBERSORT.Immune.lm = lm(XDec.Immune ~ -1 + CIBERSORT.Immune)
XDec.CIBERSORT.Epi.lm = lm(XDec.Epi ~ -1 + CIBERSORT.Epi)
summary(XDec.CIBERSORT.Stroma.lm)
summary(XDec.CIBERSORT.Immune.lm)
summary(XDec.CIBERSORT.Epi.lm)
#kassandra
XDec.kassandra.Stroma.lm = lm(XDec.kassandra$XDec_Stroma ~ -1 + XDec.kassandra$`Kassandra_Stromal_sum `)
XDec.kassandra.Immune.lm = lm(XDec.kassandra$XDec_Immune ~ -1 + XDec.kassandra$`Kassandra_Immune_sum `)
XDec.kassandra.Epi.lm = lm(XDec.kassandra$XDec_Epithelial ~ -1 + XDec.kassandra$`Kassandra_Cancer_sum `)
summary(XDec.kassandra.Stroma.lm)
summary(XDec.kassandra.Immune.lm)
summary(XDec.kassandra.Epi.lm)
XDec.epic.Stroma.lm = lm(XDec.epic$XDec_Stroma ~ -1 + XDec.epic$epic_stromal)
XDec.epic.Immune.lm = lm(XDec.epic$XDec_Immune ~ -1 + XDec.epic$epic_immune)
XDec.epic.Epi.lm = lm(XDec.epic$XDec_Epithelial ~ -1 + XDec.epic$epic_cancer)
summary(XDec.epic.Stroma.lm)
summary(XDec.epic.Immune.lm)
summary(XDec.epic.Epi.lm)
XDec.xcell.Stroma.lm = lm(XDec.xcell$XDec_Stroma ~ -1 + XDec.xcell$xcell_stroma)
XDec.xcell.Immune.lm = lm(XDec.xcell$XDec_Immune ~ -1 + XDec.xcell$xcell_immune)
XDec.xcell.Epi.lm = lm(XDec.xcell$XDec_Epithelial ~ -1 + XDec.xcell$xcell_epithelial)
summary(XDec.xcell.Stroma.lm)
summary(XDec.xcell.Immune.lm)
summary(XDec.xcell.Epi.lm)
#Summary figure correlation
Prop.TCGA.SC.Model.Stage2.general
CibersortPropsTable_general = CibersortPropsTable[,c(1,11,12,13)]
all_methods_general = merge(CibersortPropsTable_general,Prop.TCGA.SC.Model.Stage2.general, by = "Samples")
all_methods_general = merge(all_methods_general,kassandra_prop, by = "Samples")
all_methods_general = merge(all_methods_general,xcell, by = "Samples")
all_methods_general = merge(all_methods_general,epic, by = "Samples")
all_methods_general_ep = all_methods_general[,c(1,4,5,10,13,16)]
all_methods_general_stromal = all_methods_general[,c(1,2,7,9,12,15)]
all_methods_general_immune = all_methods_general[,c(1,3,6,8,11,14)]
rownames(all_methods_general_ep) = all_methods_general_ep$Samples
all_methods_general_ep$Samples = NULL
rownames(all_methods_general_stromal) = all_methods_general_stromal$Samples
all_methods_general_stromal$Samples = NULL
rownames(all_methods_general_immune) = all_methods_general_immune$Samples
all_methods_general_immune$Samples = NULL
all_methods_general_ep = data.frame(lapply(all_methods_general_ep,as.numeric))
all_methods_general_stromal = data.frame(lapply(all_methods_general_stromal,as.numeric))
all_methods_general_immune = data.frame(lapply(all_methods_general_immune,as.numeric))
ep_cor = cor(all_methods_general_ep)
immune_cor = cor(all_methods_general_immune)
stroma_cor = cor(all_methods_general_stromal)
pdf("ep_cor.pdf")
corrplot(ep_cor,method = 'number',order = "alphabet")
dev.off()
pdf("ep_cor_circle.pdf")
corrplot(ep_cor,order = "alphabet")
dev.off()
pdf("immune_cor.pdf")
corrplot(immune_cor,method = 'number',order = "alphabet")
dev.off()
pdf("immune_cor_circle.pdf")
corrplot(immune_cor,order = "alphabet")
dev.off()
pdf("stroma_cor.pdf")
corrplot(stroma_cor,method = 'number',order = "alphabet")
dev.off()
pdf("stroma_cor_circle.pdf")
corrplot(stroma_cor,order = "alphabet")
dev.off()
diag(ep_cor) = NA
diag(immune_cor) = NA
diag(stroma_cor) = NA
sum = colSums(ep_cor)
colMeans(ep_cor,na.rm = TRUE)
colMeans(immune_cor,na.rm = TRUE)
colMeans(stroma_cor,na.rm = TRUE)
mean_ep_cor = ep_cor
mean_ep_cor$mean = colMeans(mean_ep_cor)
```
```{r plot XDec vs other methods - single cell}
####FIGURE 2#####
plot(Infinium.Props,XDec.Purity, xlim = c(0,1), ylim = c(0,1), main = "Infinium", xlab = "Infinium proportions (%)", ylab = "SingleCell XDec proportions (%)", col = "black", pch = 20)
abline(XDec.Purity.lm)
plot(Timer.Props,XDec.Immune.Timer, ylim = c(0,1), main = "TIMER", xlab = "TIMER proportions (%)", ylab = "SingleCell XDec proportions (%)", col = "black", pch = 20)
abline(XDec.Timer.lm)
plot(Timer.Props,XDec.Immune.Timer, ylim = c(0,1), main = "TIMER", xlab = "TIMER proportions (%)", ylab = "SingleCell XDec proportions (%)", col = "black", pch = 20)
abline(XDec.Timer.lm)
pdf("xdec_kassandra.pdf")
plot(XDec.kassandra$`Kassandra_Stromal_sum `,XDec.kassandra$XDec_Stroma, xlim = c(0,1), ylim = c(0,1), main = "kassandra", xlab = "kassandra", ylab = "SingleCell XDec proportions (%)", col = "green", pch = 20)
abline(XDec.kassandra.Stroma.lm, col = "green")
points(XDec.kassandra$`Kassandra_Immune_sum `,XDec.kassandra$XDec_Immune,xlim = c(0,1), ylim = c(0,1), main = "kassandra", xlab = "kassandraX", ylab = "SingleCell XDec proportions (%)", col = "blue", pch = 20)
abline(XDec.kassandra.Immune.lm,col = "blue")
points(XDec.kassandra$`Kassandra_Cancer_sum `,XDec.kassandra$XDec_Epithelial,xlim = c(0,1), ylim = c(0,1), main = "kassandra", xlab = "kassandraX", ylab = "SingleCell XDec proportions (%)", col = "black", pch = 20)
abline(XDec.kassandra.Epi.lm,col = "black")
dev.off()
pdf("xdec_epic.pdf")
plot(XDec.epic$epic_stromal,XDec.epic$XDec_Stroma, xlim = c(0,1), ylim = c(0,1), main = "epic", xlab = "epic", ylab = "SingleCell XDec proportions (%)", col = "green", pch = 20)
abline(XDec.epic.Stroma.lm, col = "green")
points(XDec.epic$epic_immune ,XDec.epic$XDec_Immune,xlim = c(0,1), ylim = c(0,1), main = "epic", xlab = "epicX", ylab = "SingleCell XDec proportions (%)", col = "blue", pch = 20)
abline(XDec.epic.Immune.lm,col = "blue")
points(XDec.epic$epic_cancer,XDec.epic$XDec_Epithelial,xlim = c(0,1), ylim = c(0,1), main = "epic", xlab = "epicX", ylab = "SingleCell XDec proportions (%)", col = "black", pch = 20)
abline(XDec.epic.Epi.lm,col = "black")
dev.off()
pdf("xdec_xcell.pdf")
plot(XDec.xcell$xcell_stroma,XDec.xcell$XDec_Stroma, xlim = c(0,1), ylim = c(0,1), main = "xcell", xlab = "xcell", ylab = "SingleCell XDec proportions (%)", col = "green", pch = 20)
abline(XDec.xcell.Stroma.lm, col = "green")
points(XDec.xcell$xcell_immune ,XDec.xcell$XDec_Immune,xlim = c(0,1), ylim = c(0,1), main = "xcell", xlab = "xcellX", ylab = "SingleCell XDec proportions (%)", col = "blue", pch = 20)
abline(XDec.xcell.Immune.lm,col = "blue")
points(XDec.xcell$xcell_epithelial,XDec.xcell$XDec_Epithelial,xlim = c(0,1), ylim = c(0,1), main = "xcell", xlab = "xcellX", ylab = "SingleCell XDec proportions (%)", col = "black", pch = 20)
abline(XDec.xcell.Epi.lm,col = "black")
dev.off()
###FIGURE 3 ####
plot(CIBERSORT.Stroma,XDec.Stroma, xlim = c(0,1), ylim = c(0,1), main = "Cibersort", xlab = "CibersortX", ylab = "SingleCell XDec proportions (%)", col = "green", pch = 20)
abline(XDec.CIBERSORT.Stroma.lm, col = "green")
points(CIBERSORT.Immune,XDec.Immune,xlim = c(0,1), ylim = c(0,1), main = "Cibersort", xlab = "CibersortX", ylab = "SingleCell XDec proportions (%)", col = "blue", pch = 20)
abline(XDec.CIBERSORT.Immune.lm,col = "blue")
points(CIBERSORT.Epi,XDec.Epi,xlim = c(0,1), ylim = c(0,1), main = "Cibersort", xlab = "CibersortX", ylab = "SingleCell XDec proportions (%)", col = "black", pch = 20)
abline(XDec.CIBERSORT.Immune.lm,col = "black")
```