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RNAseq_cispt_GH.Rmd
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RNAseq_cispt_GH.Rmd
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---
title: "RNAseq_cispt"
author: "C Carraro"
date: "080822"
output: html_document
---
## Install and load packages
### Install CRAN
```{r}
# CRAN packages
list.of.packages <- c("tidyr",
"ggplot2",
"ggrepel",
"gplots",
"ggbeeswarm",
"hexbin",
"reshape2",
"factoextra",
"Hmisc",
"VennDiagram",
"openxlsx",
"UpSetR")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)>0) install.packages(new.packages)
```
### Install BioConductor
```{r}
# BioconductoR packages
list.of.bioc.packages<- c("rhdf5",
"clusterProfiler",
"DOSE",
"GSEABase",
"RColorBrewer",
"ComplexHeatmap",
"tximport",
"DESeq2",
"vsn",
"pheatmap",
"genefilter",
"biomaRt",
"limma",
"sva",
"IHW",
"org.Mm.eg.db",
"org.Hs.eg.db",
"dplyr")
new.packages.bioc <- list.of.bioc.packages[!(list.of.bioc.packages %in% installed.packages()[,"Package"])]
if(length(new.packages.bioc)>0)if (!requireNamespace("BiocManager")) install.packages("BiocManager")
BiocManager::install(new.packages.bioc, update = FALSE)
```
### Load Packages
```{r load packages, results='hide',message=FALSE,warning=FALSE}
lapply(c(list.of.packages,list.of.bioc.packages), require, character.only = TRUE)
```
## Custom functions
### Heatmap colors
```{r}
scaleColors <- function(data = input_scale, # data to use
maxvalue = NULL # value at which the color is fully red / blue
){
if(is.null(maxvalue)){
maxvalue <- floor(min(abs(min(data)), max(data)))
}
if(max(data) > abs(min(data))){
if(ceiling(max(data)) == maxvalue){
myBreaks <- c(floor(-max(data)), seq(-maxvalue+0.2, maxvalue-0.2, 0.2), ceiling(max(data)))
} else{
myBreaks <- c(floor(-max(data)), seq(-maxvalue, maxvalue, 0.2), ceiling(max(data)))
}
paletteLength <- length(myBreaks)
myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
} else {
if(-floor(min(data)) == maxvalue){
myBreaks <- c(floor(min(data)), seq(-maxvalue+0.2, maxvalue-0.2, 0.2), ceiling(min(data)))
} else{
myBreaks <- c(floor(min(data)), seq(-maxvalue, maxvalue, 0.2), ceiling(abs(min(data))))
}
paletteLength <- length(myBreaks)
myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
}
return(list(breaks = myBreaks, color = myColor))
}
```
### BoxPlot of highest expressed genes
```{r}
highestGenes <- function(numGenes=10){
tmp <- norm_anno[,colnames(norm_anno) %in% sample_table$ID]
tmp <- tmp[order(rowMeans(tmp), decreasing = T),]
tmp <- tmp[1:numGenes,]
tmp <- melt(t(tmp))
colnames(tmp)<- c("sample","gene","value")
idx <- match(tmp$gene,norm_anno$GENEID)
tmp$symbol <- as.factor(norm_anno$SYMBOL[idx])
tmp$symbol <- factor(tmp$symbol, levels = rev(unique(tmp$symbol)))
ggplot(tmp, aes(x = tmp$symbol, y = value)) +
geom_boxplot()+
xlab("Gene")+
ylab("Normalized Expression")+
ggtitle(paste("Expression of", numGenes, "highest expressed genes")) +
theme_bw() +
coord_flip() +
theme(axis.text.x = element_text(size=8, angle = 90, hjust = 1),
plot.title = element_text(size = 8, face = "bold"))
}
```
### Heatmap
```{r}
plotHeatmap <- function(geneset,
title="",
keyType = "Ensembl",
show_rownames = FALSE,
cluster_cols = FALSE,
font.size=7,
max.value=2){
if(geneset[1] =="all"){
input <- norm_anno
}else{
if(keyType == "Ensembl"){
input <- norm_anno[norm_anno$GENEID %in% geneset,]
} else if(keyType == "Symbol"){
input <- norm_anno[norm_anno$SYMBOL %in% geneset,]
} else{
print("Wrong keyType. Choose Ensembl or Symbol!")
}
}
rownames(input) <- paste(input$GENEID, ":", input$SYMBOL, sep="")
input <- input[,colnames(input) %in% sample_table$ID]
input_scale <- t(scale(t(input)))
input_scale <- input_scale[,order(sample_table[[plot_order]], decreasing = FALSE)]
pheatmap(input_scale,
main=title,
show_rownames=show_rownames,
show_colnames=TRUE,
cluster_cols = cluster_cols,
fontsize = font.size,
annotation_col = plot_annotation,
annotation_colors = ann_colors,
breaks = scaleColors(data = input_scale, maxvalue = max.value)[["breaks"]],
color = scaleColors(data = input_scale, maxvalue = max.value)[["color"]])
}
```
### Heatmap of genes of specified gene sets
```{r}
plotGeneSetHeatmap <- function(cat,
term,
organism,
show_rownames =TRUE,
cluster_cols = FALSE,
font.size= 7,
max.value=2){
if(organism == "mouse"){
GO <- GO_mm
KEGG <- KEGG_mm
} else if(organism == "human"){
GO <- GO_hs
KEGG <- KEGG_hs
} else (stop("Wrong organism specified!"))
xterm <- paste("^", term, "$", sep="")
if(cat=="GO"){
genes <- unique(GO[grep(xterm,GO$TERM),"SYMBOL"])
}
if(cat=="KEGG"){
genes <- unique(KEGG[grep(xterm,KEGG$PATHWAY),"SYMBOL"])
}
if(cat=="Hallmark"){
genes <- unique(hallmark_genes[grep(xterm,hallmark_genes$ont),"gene"])
}
if(cat=="cannonicalPathways"){
genes <- unique(cannonicalPathway_genes[grep(xterm,cannonicalPathway_genes$ont),"gene"])
}
if(cat=="ImmunoSignatures"){
genes <- unique(immuno_genes[grep(xterm,immuno_genes$ont),"gene"])
}
if(cat=="Motifs"){
genes <- unique(motifs[grep(xterm,motifs$ont),"gene"])
}
if(organism == "mouse" &
cat == "Hallmark"|
cat == "cannonicalPathways"|
cat =="ImmunoSignatures"|
cat == "Motifs"){
genes <- getLDS(attributes = c("entrezgene"),
filters = "entrezgene",
values = genes,
mart = useMart("ensembl", dataset = "hsapiens_gene_ensembl"),
attributesL = c("mgi_symbol"),
martL = useMart("ensembl", dataset = "mmusculus_gene_ensembl"),
uniqueRows=T)[,2]
}
plotHeatmap(geneset = genes,
keyType = "Symbol",
title = paste("Heatmap of present genes annotated to: ",term, sep=""),
show_rownames = show_rownames,
cluster_cols = cluster_cols,
font.size=font.size,
max.value=max.value)
}
```
### PCA function
```{r}
plotPCA <- function(pca_input = dds_vst,
ntop=500,
xPC=1,
yPC=2,
color,
anno_colour,
shape="NULL",
point_size=3,
title="PCA",
label = NULL,
label_subset = NULL){
if(!is.data.frame(pca_input)){
vst_matrix <-as.matrix(assay(pca_input))
}else{
vst_matrix <- pca_input
}
if(ntop=="all"){
pca <- prcomp(t(vst_matrix))
}else{
# select the ntop genes by variance
select <- order(rowVars(vst_matrix), decreasing=TRUE)[c(1:ntop)]
pca <- prcomp(t(vst_matrix[select,]))
}
#calculate explained variance per PC
explVar <- pca$sdev^2/sum(pca$sdev^2)
# transform variance to percent
percentVar <- round(100 * explVar[c(xPC,yPC)], digits=1)
# Define data for plotting
pcaData <- data.frame(xPC=pca$x[,xPC],
yPC=pca$x[,yPC],
color = sample_table[[color]],
name= as.character(sample_table$ID),
stringsAsFactors = F)
#plot PCA
if(is.factor(pcaData$color) || is.character(pcaData$color)|| is.integer(pcaData$color)){
if(shape == "NULL"){
pca_plot <- ggplot(pcaData, aes(x = xPC, y = yPC, colour=color)) +
geom_point(size =point_size)
}else{
pcaData$shape = sample_table[[shape]]
pca_plot <- ggplot(pcaData, aes(x = xPC, y = yPC, colour=color, shape=shape)) +
geom_point(size =point_size) +
scale_shape_discrete(name=shape)
}
if(anno_colour[1] == "NULL"){
pca_plot <- pca_plot + scale_color_discrete(name=color)
}else{
pca_plot <- pca_plot + scale_color_manual(values=anno_colour, name=color)
}
}else if(is.numeric(pcaData$color)){
if(shape == "NULL"){
pca_plot <- ggplot(pcaData, aes(x = xPC, y = yPC, colour=color)) +
geom_point(size =point_size) +
scale_color_gradientn(colours = bluered(100),name=color)
}else{
pcaData$shape = sample_table[[shape]]
pca_plot <- ggplot(pcaData, aes(x = xPC, y = yPC, colour=color, shape=shape)) +
geom_point(size =point_size) +
scale_color_gradientn(colours = bluered(100),name=color)+
scale_shape_discrete(name=shape)
}
}
# adds a label to the plot. To label only specific points, put them in the arument label_subset
if (!is.null(label) == TRUE){
pcaData$label <- sample_table[[label]]
if(!is.null(label_subset) == TRUE){
pcaData_labeled <- pcaData[pcaData$label %in% label_subset,]
} else {
pcaData_labeled <- pcaData
}
pca_plot <- pca_plot +
geom_text_repel(data = pcaData_labeled, aes(label = label), nudge_x = 2, nudge_y = 2, colour = "black")
}
pca_plot <- pca_plot+
xlab(paste0("PC ",xPC, ": ", percentVar[1], "% variance")) +
ylab(paste0("PC ",yPC,": ", percentVar[2], "% variance")) +
coord_fixed()+
theme_classic()+
theme(aspect.ratio = 1)+
ggtitle(title)
pca_plot
}
```
### Heatmaps of PC loadings
```{r}
plotLoadings <- function(PC,
ntop,
font.size=7,
cluster_cols=TRUE){
if(ntop=="all"){
pca <- prcomp(t(assay(dds_vst)))
}else{
select <- order(rowVars(assay(dds_vst)), decreasing=TRUE)[c(1:ntop)]
pca <- prcomp(t(assay(dds_vst)[select,]))
}
Loadings <- pca$rotation[,PC]
Loadings <- Loadings[order(Loadings, decreasing = T)]
Loadings <- names(Loadings[c(1:20,(length(Loadings)-19):length(Loadings))])
heatmap <- norm_anno[norm_anno$GENEID %in% Loadings,]
rownames(heatmap) <- paste(heatmap$GENEID,": ",heatmap$SYMBOL,sep="")
heatmap <- heatmap[,colnames(heatmap) %in% sample_table$ID]
heatmap_scale <- as.matrix(t(scale(t(heatmap))))
heatmap_scale <- heatmap_scale[,order(sample_table[[plot_order]], decreasing = FALSE)]
# Heatmap
pheatmap(heatmap_scale,
main=paste("Hierarchical Clustering of top20 ",PC, " loadings in both directions",sep=""),
show_rownames=TRUE,
show_colnames = TRUE,
annotation_col = plot_annotation,
annotation_colors = ann_colors,
breaks = scaleColors(heatmap_scale, 2)[["breaks"]],
color = scaleColors(heatmap_scale, 2)[["color"]],
cluster_cols = cluster_cols,
fontsize=font.size)
}
```
### MultiPlot
```{r}
multiplot<-function(plots=plots,
cols=1){
layout <- matrix(seq(1, cols * length(plots)/cols),
ncol = cols,
nrow = length(plots)/cols)
if (length(plots)==1) {
print(plots[[1]])
}else{
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
for (i in 1:length(plots)){
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
```
### Boxplot of normalized or batch-corrected counts for a single gene
```{r}
plotSingleGene <-function(data=norm_anno,
symbol,
condition="Genotype_Age",
anno_colour=col_genotype_age,
shape = NULL) {
input<-as.data.frame(data)
rownames(input)<- input$GENEID
if(sum(input$SYMBOL == symbol) == 0){
stop("Gene not present")
}else{
plots<-list()
for (i in 1:sum(input$SYMBOL == symbol)) {
geneCounts <- as.data.frame(t(input[input$SYMBOL == symbol, colnames(input) %in% sample_table$ID]))
geneCounts$condition <- sample_table[[condition]]
GENEID<-colnames(geneCounts)[i]
colnames(geneCounts)[i]<-"y"
if(!is.null(anno_colour)){
if (is.null(shape)){
plot<-ggplot(geneCounts, aes(x = condition, y = y, colour=condition)) +
scale_color_manual(values=anno_colour)+
geom_beeswarm(cex = 3, na.rm=T)
}else{
geneCounts$shape <- sample_table[[shape]]
legend_shape<-paste0(shape)
plot<-ggplot(geneCounts, aes(x = condition, y = y, colour=condition)) +
scale_color_manual(values=anno_colour)+
geom_beeswarm(cex = 3, na.rm=T, aes(shape=shape)) +
scale_shape(name=legend_shape)
}
}else{
if (is.null(shape_opt)){
plot<-ggplot(geneCounts, aes(x = condition, y = y, colour=condition)) +
scale_color_brewer(palette = "Spectral")+
geom_beeswarm(cex = 3, na.rm=T, aes(size=3))
}else{
geneCounts$shape <- sample_table[[shape]]
legend_shape<-paste0(shape)
plot<-ggplot(geneCounts, aes(x = condition, y = y, colour=condition)) +
scale_color_brewer(palette = "Spectral")+
geom_beeswarm(cex = 3, na.rm=T, aes(shape=shape)) +
scale_shape(name=legend_shape)
}
}
plots[[i]]<-plot+
geom_boxplot(width=.5,alpha=0) +
stat_boxplot(geom ='errorbar',width=.25) +
ylab("Normalized counts") +
scale_y_continuous(expand=c(0.05,0.25)) +
expand_limits(y=0) +
labs(title=paste(symbol, GENEID, sep=": "),colour=condition)+
theme_classic()+
theme(plot.title = element_text(hjust=0.5))
}
if(sum(input$SYMBOL== symbol)>1){
print("Selected gene symbol assigned to more than one gene (Ensembl ID)")
multiplot(plots)
}else{
print("Selected gene symbol assigned to one gene (Ensembl ID)")
multiplot(plots)
}
}
}
```
### Remove potential batch effects using the removeBatchEffect function from limma
```{r}
limmaBatchEffectRemoval <- function(input=dds_vst,
batchfactor, # name of batch effect column in sample_table
batchfactor_2=NULL,
modelfactor){ # name of model effect column in sample_table
# rlog-transformed input
x <- as.matrix(assay(input))
# design matrix
model <- model.matrix(~sample_table[,c(modelfactor)])
# run batch remocal function
if(is.numeric(sample_table[,colnames(sample_table) == batchfactor[1]])==T){
as.data.frame(removeBatchEffect(x,
covariates = sample_table[,colnames(sample_table) %in% batchfactor],
design = model))
}else{
if(is.null(batchfactor_2)){
as.data.frame(removeBatchEffect(x=x,
batch = sample_table[,colnames(sample_table) == batchfactor],
design = model))
}else{
as.data.frame(removeBatchEffect(x=x,
batch = sample_table[,colnames(sample_table) == batchfactor],
batch2 = sample_table[,colnames(sample_table) == batchfactor_2],
design = model))
}
}
}
```
### DESeq2 output
```{r}
# Specify structure of DESeq2_analysis_object
setClass(Class = "DESeq2_analysis_object",
slots = c(results="data.frame", DE_genes="list", Number_DE_genes="list"))
# Wrapper Function to perform DESeq2 differential testing
DEAnalysis <- function(condition,
alpha = 0.05,
lfcThreshold = 0,
sigFC = 2,
multiple_testing = "IHW",
independentFiltering="TRUE",
shrinkage = TRUE,
shrinkType = "normal"){
# create results_list
results_list <- list()
# print parameters
results_list$parameters <-list(multiple_testing = multiple_testing,
p_value_threshold = alpha,
log2_FC_threshold = lfcThreshold,
shrinkage = shrinkage,
shrinkage_type = shrinkType)
# Run results() function on comparisons defined in comparison table
for (i in 1:nrow(comparison_table)){
# create DE_object
DE_object <- new(Class = "DESeq2_analysis_object")
# IHW
if (multiple_testing=="IHW") {
res_deseq_lfc <- results(dds,
contrast = c(condition,
paste(comparison_table$comparison[i]),
paste(comparison_table$control[i])),
lfcThreshold = lfcThreshold,
alpha = alpha,
filterFun = ihw,
altHypothesis = "greaterAbs")
# Independent Filtering
}else {
res_deseq_lfc <- results(dds,
contrast = c(condition,
paste(comparison_table$comparison[i]),
paste(comparison_table$control[i])),
lfcThreshold = lfcThreshold,
alpha = alpha,
independentFiltering = independentFiltering,
altHypothesis = "greaterAbs",
pAdjustMethod= multiple_testing)
}
if(shrinkage == TRUE){
res_deseq_lfc <- lfcShrink(dds,
contrast = c(condition,
paste(comparison_table$comparison[i]),
paste(comparison_table$control[i])),
res=res_deseq_lfc,
type = shrinkType)
}
res_deseq_lfc <- as.data.frame(res_deseq_lfc)
# indicate significant DE genes
res_deseq_lfc$regulation <- ifelse(!is.na(res_deseq_lfc$padj)&
res_deseq_lfc$padj <= alpha&
res_deseq_lfc$log2FoldChange > log(sigFC,2),
"up",
ifelse(!is.na(res_deseq_lfc$padj)&
res_deseq_lfc$padj <= alpha&
res_deseq_lfc$log2FoldChange < -log(sigFC,2),
"down",
"n.s."))
# add gene annotation to results table
res_deseq_lfc$GENEID <- row.names(res_deseq_lfc) # ensembl-IDs as row names
res_deseq_lfc <- merge(res_deseq_lfc,
norm_anno[,c("GENEID",
"SYMBOL",
"GENETYPE",
"DESCRIPTION",
"CHR")],
by = "GENEID")
row.names(res_deseq_lfc) <- res_deseq_lfc$GENEID
res_deseq_lfc$comparison<-paste(comparison_table$comparison[i]," vs ",comparison_table$control[i],
sep="")
# re-order results table
if (multiple_testing=="IHW") {
res_deseq_lfc<-res_deseq_lfc[,c("GENEID",
"SYMBOL",
"GENETYPE",
"DESCRIPTION",
"CHR",
"comparison",
"regulation",
"baseMean",
"log2FoldChange",
"lfcSE",
"stat",
"pvalue",
"padj",
"weight")]
}else{
res_deseq_lfc<-res_deseq_lfc[,c("GENEID",
"SYMBOL",
"GENETYPE",
"DESCRIPTION",
"CHR",
"comparison",
"regulation",
"baseMean",
"log2FoldChange",
"lfcSE",
"stat",
"pvalue",
"padj")]
}
# print result table
DE_object@results <- res_deseq_lfc
# print DE genes in seperate tables
DE_object@DE_genes <- list(up_regulated_Genes = res_deseq_lfc[res_deseq_lfc$regulation =="up",],
down_regulated_Genes= res_deseq_lfc[res_deseq_lfc$regulation =="down",])
# print the numbers of DE genes
DE_object@Number_DE_genes <- list(up_regulated_Genes = nrow(DE_object@DE_genes$up_regulated_Genes),
down_regulated_Genes= nrow(DE_object@DE_genes$down_regulated_Genes))
# write DE_object into results_list
results_list[[paste(comparison_table$comparison[i], "vs", comparison_table$control[i], sep="_")]] <- DE_object
}
return(results_list)
}
```
### Union of DEgenes
```{r}
uDEG <- function(comparisons){
uDEGs <- NULL
tmp <- DEresults[names(DEresults) %in% comparisons]
for(i in 1:length(comparisons)){
DEGs <- as.data.frame(tmp[[i]]@results[tmp[[i]]@results$regulation %in% c("up","down"),])
uDEGs <- unique(c(uDEGs, DEGs$GENEID))
}
uDEGs
}
```
### Union of DEgenes, SYMBOL (protein_coding)
```{r}
uDEG_S <- function(comparisons){
uDEGs <- NULL
tmp <- DEresults[names(DEresults) %in% comparisons]
for(i in 1:length(comparisons)){
DEGs <- as.data.frame(tmp[[i]]@results[tmp[[i]]@results$regulation %in% c("up","down"),])
DEGs <- subset(DEGs, GENETYPE=='protein_coding')
uDEGs <- unique(c(uDEGs, DEGs$SYMBOL))
}
uDEGs
}
```
### Venn Diagram
```{r}
plotVenn <- function(comparisons,
regulation=NULL){
venn <- NULL
for(i in 1:length(comparisons)){
res <- DEresults[names(DEresults) %in% comparisons]
comp <- as.data.frame(res[[i]]@results)
if(is.null(regulation)){
DE <- ifelse(comp$regulation %in% c("up","down"), 1, 0)
venn <- cbind(venn, DE)
colnames(venn)[i]<- paste(names(res)[[i]], "up&down", sep=": ")
} else {
DE <- ifelse(comp$regulation == regulation, 1, 0)
venn <- cbind(venn, DE)
colnames(venn)[i]<- paste(names(res)[[i]], regulation, sep=": ")
}
}
vennDiagram(venn,cex = 1, counts.col = "blue")
}
```
### Ratio Plot
```{r}
plotRatios <- function(comp1, comp2){
U <- NULL
c <- c(comp1,comp2)
U <- uDEG(c)
Ratio <- NULL
for(i in 1:length(c)){
tmp <- DEresults[names(DEresults) %in% c]
comp <- as.data.frame(tmp[[i]]@results)
DE <- as.data.frame(comp[rownames(comp) %in% U,])
Ratio <- as.data.frame(cbind(Ratio,DE$log2FoldChange))
}
colnames(Ratio)<- c
rownames(Ratio) <- U
ggplot(Ratio, aes(x=Ratio[,1], y=Ratio[,2])) +
geom_point(colour = "grey", size = 1.5) +
theme_bw() +
xlab(comp1)+
ylab(comp2) +
geom_abline(slope = c(-1,1),intercept = 0, colour="grey") +
geom_hline(yintercept = c(0,log(2,2),-(log(2,2))))+
geom_hline(yintercept = c(log(2,2),-(log(2,2))), colour="firebrick1")+
geom_vline(xintercept = c(log(2,2),-(log(2,2))))+
geom_vline(xintercept = c(log(2,2),-(log(2,2))), colour="firebrick1")+
theme(text = element_text(size=10))+
ggtitle(paste(comp1," vs ",comp2,": ",length(U)," DE genes",sep=""))
}
```
### Ranked Fold Change plot
```{r}
plotFCrank <- function(comp1,
comp2){
rank <- na.omit(DEresults[names(DEresults) == comp1][[1]]@results)
rank <- rank[rank$padj < 0.05 , c("GENEID","comparison","log2FoldChange")]
rank <- rank[order(rank$log2FoldChange,decreasing = TRUE),]
rank$rank <- c(1:nrow(rank))
rank2 <- DEresults[names(DEresults) == comp2][[1]]@results
rank2 <- rank2[rownames(rank),c("GENEID","comparison","log2FoldChange")]
rank2$rank <- rank$rank
rank <- rbind(rank, rank2)
ggplot(rank,aes(x=rank,y=log2FoldChange,color=comparison)) +
geom_point(alpha=0.5) +
geom_line(aes(group=GENEID),color="grey",alpha=0.2)+
theme_bw() +
ylab("log2(FoldChange)")+
xlab(paste("FC rank of " ,comp1, sep=""))+
geom_hline(yintercept = 0)+
geom_hline(yintercept = c(log(2,2),-(log(2,2))), colour="firebrick1")+
theme(text = element_text(size=10))+
ggtitle("Comparison of fold changes (comp1 padj<0.05")+
theme(legend.position="bottom")
}
```
### GO & KEGG enrichment across comparisons
```{r}
compareGSEA <- function(comparisons,
organism, # chose organism
GeneSets =c("GO","KEGG"), # choose gene sets for enrichment
ontology= "BP", # define GO subset
pCorrection = "bonferroni", # choose the p-value adjustment method
pvalueCutoff = 0.05, # set the unadj. or adj. p-value cutoff (depending on correction method)
qvalueCutoff = 0.05, # set the q-value cutoff (FDR corrected)
showMax = 20){
if(organism == "mouse") {
OrgDb = org.Mm.eg.db
} else if(organism == "human"){
OrgDb = org.Hs.eg.db
} else {stop("Wrong Organism. Select mouse or human.")}
ENTREZlist <- list()
for(i in 1:length(comparisons)){
res <- DEresults[names(DEresults) %in% comparisons]
DE_up <- as.data.frame(res[[i]]@DE_genes$up_regulated_Genes)$SYMBOL
entrez_up <- bitr(DE_up, fromType = "SYMBOL", toType="ENTREZID", OrgDb=OrgDb)$ENTREZID
DE_down <- as.data.frame(res[[i]]@DE_genes$down_regulated_Genes)$SYMBOL
entrez_down <- bitr(DE_down, fromType = "SYMBOL", toType="ENTREZID", OrgDb=OrgDb)$ENTREZID
x <- setNames(list(entrez_up, entrez_down),
c(paste(names(res[i]),"_up",sep=""),
paste(names(res[i]),"_down",sep="")))
ENTREZlist <- c(ENTREZlist,x)
}
list <- list()
# Compare the Clusters regarding their GO enrichment
if("GO" %in% GeneSets){
print("Performing GO enrichment")
CompareClusters_GO <- compareCluster(geneCluster = ENTREZlist,
fun = "enrichGO",
universe = universe_Entrez,
OrgDb = OrgDb,
ont = ontology,
pvalueCutoff = pvalueCutoff,
pAdjustMethod = pCorrection,
qvalueCutoff = pvalueCutoff,
readable = T)
list$GOresults <- as.data.frame(CompareClusters_GO)
list$GOplot <- clusterProfiler::dotplot(CompareClusters_GO, showCategory = showMax, by = "geneRatio", font.size=10)
}
if("KEGG" %in% GeneSets){
print("Performing KEGG enrichment")
if(organism == "mouse"){org = "mmu"}
if(organism == "human"){org = "hsa"}
# Compare the Clusters regarding their KEGG enrichment
CompareClusters_KEGG <- compareCluster(geneCluster = ENTREZlist,
fun = "enrichKEGG",
universe = universe_Entrez,
organism = org,
pvalueCutoff = pvalueCutoff,
pAdjustMethod = pCorrection,
qvalueCutoff = pvalueCutoff)
list$KEGGresults <- as.data.frame(CompareClusters_KEGG)
list$KEGGplot <- clusterProfiler::dotplot(CompareClusters_KEGG, showCategory = showMax, by = "geneRatio", font.size=10)
}
list
}
```
### Plot baseMean versus fold change (MAplot)
```{r}
plotMA <- function(comparison,
ylim=c(-2,2),
padjThreshold=0.05,
xlab = "mean of normalized counts",
ylab = expression(log[2]~fold~change),
log = "x",
cex=0.45){
x <- as.data.frame(DEresults[[comparison]]@results)
if (!(is.data.frame(x) && all(c("baseMean", "log2FoldChange") %in% colnames(x)))){
stop("'x' must be a data frame with columns named 'baseMean', 'log2FoldChange'.")
}
col = ifelse(x$padj>=padjThreshold, "gray32", "red3")
py = x$log2FoldChange
if(missing(ylim)){
ylim = c(-1,1) * quantile(abs(py[is.finite(py)]), probs=0.99) * 1.1
}
plot(x=x$baseMean,
y=pmax(ylim[1], pmin(ylim[2], py)),
log=log,
pch=ifelse(py<ylim[1], 6, ifelse(py>ylim[2], 2, 16)),
cex=cex,
col=col,
xlab=xlab,
ylab=ylab,
ylim=ylim,
main=comparison)
abline(h=0, lwd=4, col="#ff000080")
abline(h=c(-1,1), lwd=2, col="dodgerblue")
}
```
### p-value distribution
```{r}
plotPvalues <- function(comparison){
res <- as.data.frame(DEresults[[comparison]]@results)
ggplot(na.omit(res), aes(x=pvalue)) +
geom_histogram(aes(y=..count..),
binwidth = 0.01) +
theme_bw()+
ggtitle(paste("p value histogram of: ",comparison,sep=""))
}
```
### Heatmaps of DE genes based on pheatmap
```{r}
plotDEHeatmap <- function(comparison,
factor,
conditions="all",
show_rownames = FALSE,
cluster_cols = FALSE,
font.size=7,
max.value=2){
geneset <- DEresults[[comparison]]@results[DEresults[[comparison]]@results$regulation %in% c("up","down"),"GENEID"]
input <- norm_anno[norm_anno$GENEID %in% geneset,]
rownames(input) <- paste(input$GENEID, ":", input$SYMBOL, sep="")
if(conditions[1] == "all"){
input <- input[,colnames(input) %in% sample_table$ID]
input_scale <- t(scale(t(input)))
input_scale <- input_scale[,order(sample_table[[plot_order]], decreasing = FALSE)]
} else {
input <- input[,colnames(input) %in% sample_table[as.vector(sample_table[[factor]]) %in% conditions,]$ID,]
input_scale <- t(scale(t(input)))
}
pheatmap(input_scale,
main=paste("Heatmap of significant DE genes in: ",comparison,sep=""),
show_rownames=show_rownames,
show_colnames=TRUE,
cluster_cols = cluster_cols,
fontsize = font.size,
annotation_col = plot_annotation,
annotation_colors = ann_colors,
breaks = scaleColors(data = input_scale, maxvalue = max.value)[["breaks"]],
color = scaleColors(data = input_scale, maxvalue = max.value)[["color"]])
}
```
### Volcano Plot
```{r}
plotVolcano <- function(comparison,
labelnum=20){
# specify labeling
upDE <- as.data.frame(DEresults[[comparison]]@results[DEresults[[comparison]]@results$regulation =="up",])
FClabel_up <- upDE[order(abs(upDE$log2FoldChange), decreasing = TRUE),]
if(nrow(FClabel_up)>labelnum){
FClabel_up <- as.character(FClabel_up[c(1:labelnum),"GENEID"])
} else {
FClabel_up <- as.character(FClabel_up$GENEID)}
plabel_up <- upDE[order(upDE$padj, decreasing = FALSE),]
if(nrow(plabel_up)>labelnum){
plabel_up <- as.character(plabel_up[c(1:labelnum),"GENEID"])
} else {
plabel_up <- as.character(plabel_up$GENEID)}
downDE <- as.data.frame(DEresults[[comparison]]@results[DEresults[[comparison]]@results$regulation =="down",])
FClabel_down <- downDE[order(abs(downDE$log2FoldChange), decreasing = TRUE),]
if(nrow(FClabel_down)>labelnum){
FClabel_down <- as.character(FClabel_down[c(1:labelnum),"GENEID"])
} else {
FClabel_down <- as.character(FClabel_down$GENEID)}
plabel_down <- downDE[order(downDE$padj, decreasing = FALSE),]
if(nrow(plabel_down)>labelnum){
plabel_down <- as.character(plabel_down[c(1:labelnum),"GENEID"])
} else {
plabel_down <- as.character(plabel_down$GENEID)}
label<- unique(c(FClabel_up, plabel_up, FClabel_down, plabel_down))
data <- DEresults[[comparison]]@results
data$label<- ifelse(data$GENEID %in% label == "TRUE",as.character(data$SYMBOL), "")
# Volcano Plot
ggplot(data=na.omit(data), aes(x=log2FoldChange, y=-log10(padj), colour=regulation)) +
geom_point(alpha=0.4, size=1.75) +
scale_color_manual(values=c("cornflowerblue","grey", "firebrick"))+
scale_x_continuous() +
scale_y_continuous() +
xlab("log2(FoldChange)") +
ylab("-log10(padj)") +
geom_vline(xintercept = 0, colour="black")+
geom_vline(xintercept = c(-log(2,2),log(2,2)), colour="red")+
geom_hline(yintercept=-log(0.05,10),colour="red")+
geom_text_repel(data=na.omit(data[!data$label =="",]),aes(label=label), size=3)+
guides(colour=FALSE) +
ggtitle(paste("Volcano Plot of: ",comparison,sep="")) +
theme_bw()
}
```
### GSEA function
```{r}
GSEA <- function(comparison,
organism,
GeneSets =c("GO","KEGG","DO","Hallmark","cannonicalPathways","Motifs","ImmunoSignatures"),
GOntology = "BP",
pCorrection = "bonferroni", # choose the p-value adjustment method
pvalueCutoff = 0.05, # set the unadj. or adj. p-value cutoff (depending on correction method)
qvalueCutoff = 0.05 # set the q-value cutoff (FDR corrected)
){
results <- list()
if(organism == "mouse") {
OrgDb = org.Mm.eg.db
} else if(organism == "human"){
OrgDb = org.Hs.eg.db
} else {print("Wrong Organism. Select mouse or human.")}
res <- DEresults[[comparison]]
DE_up <- as.data.frame(res@DE_genes$up_regulated_Genes)$SYMBOL
entrez_up <- bitr(DE_up, fromType = "SYMBOL", toType="ENTREZID", OrgDb=OrgDb)$ENTREZID
DE_down <- as.data.frame(res@DE_genes$down_regulated_Genes)$SYMBOL
entrez_down <- bitr(DE_down, fromType = "SYMBOL", toType="ENTREZID", OrgDb=OrgDb)$ENTREZID
# GO enrichment
if("GO" %in% GeneSets){
print("Performing GO enrichment")
if(length(entrez_up)<20){
print("Too few upregulated genes for GO enrichment (<20)")
results$GOup <- "Too few upregulated genes for GO enrichment (<20)"
}else{
results$GOup <- as.data.frame(enrichGO(gene = entrez_up,
universe = universe_Entrez,
OrgDb = OrgDb,
ont = GOntology,
pAdjustMethod = pCorrection,
pvalueCutoff = pvalueCutoff,
qvalueCutoff = qvalueCutoff,
readable = T))
if(nrow(results$GOup)>0){results$GOup$Enrichment <- paste("GO enrichment for genes upregulated in ",comparison,sep="")}
}
if(length(entrez_down)<20){
print("Too few downregulated genes for GO enrichment (<20)")
results$GOdown <- "Too few downregulated genes for GO enrichment (<20)"
}else{
results$GOdown <- as.data.frame(enrichGO(gene = entrez_down,
universe = universe_Entrez,
OrgDb = OrgDb,
ont = GOntology,
pAdjustMethod = pCorrection,
pvalueCutoff = pvalueCutoff,
qvalueCutoff = qvalueCutoff,
readable = T))
if(nrow(results$GOdown)>0){results$GOdown$Enrichment <- paste("GO enrichment for genes downregulated in ",comparison,sep="")}
}
}
# KEGG enrichment
if("KEGG" %in% GeneSets){
print("Performing KEGG enrichment")
if(organism == "mouse") {org = "mmu"}
if(organism == "human"){org = "hsa"}
if(length(entrez_up)<20){
print("Too few upregulated genes for KEGG enrichment (<20)")
results$KEGGup <- "Too few upregulated genes for KEGG enrichment (<20)"
}else{
results$KEGGup <- as.data.frame(enrichKEGG(gene = entrez_up,
organism = org,
universe = universe_Entrez,
pAdjustMethod = pCorrection,
pvalueCutoff = pvalueCutoff,
qvalueCutoff = qvalueCutoff))
if(nrow(results$KEGGup)>0){results$KEGGup$Enrichment <- paste("KEGG enrichment for genes upregulated in ",comparison,sep="")}