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MAAPster_functions.R
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MAAPster_functions.R
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# projectId = 'testing'
#### 1) Process GEO files function takes gseid and returns ExpressionFeatureSet object ####
processGEOfiles <- function(id,listGroups){
library(GEOquery)
library(oligo)
library(Biobase)
id = gsub(" ","",id,fixed=TRUE)
system(paste0('rm *.[cC][eE][lL].gz')) #removes previous CEL files if run consecutively
getGEOSuppFiles(id, makeDirectory = T, baseDir = getwd())
fileID = paste0(id, '_RAW.tar')
untar(paste0(getwd(),'/',id,'/',fileID))
SampleName = list.files(pattern = '/*CEL.gz', ignore.case = T) #list contents of new directory with zipped CEL files
celfiles = read.celfiles(SampleName)
gds <- getGEO(id, GSEMatrix = F,getGPL=T,AnnotGPL=T) #get meta data
tableNames=c("gsm","title","description","groups")
pData(celfiles)[tableNames] = NA
for (k in 1:length(GSMList(gds))) #fill table with meta data
{
if (is.null(Meta(GSMList(gds)[[k]])$description)) {
pData(celfiles)[k,2:4] <-c(Meta(GSMList(gds)[[k]])$geo_accession[1], Meta(GSMList(gds)[[k]])$title[1], 'No data available')
} else {
pData(celfiles)[k,2:4] <-c(Meta(GSMList(gds)[[k]])$geo_accession[1], Meta(GSMList(gds)[[k]])$title[1], Meta(GSMList(gds)[[k]])$description[1])
}
}
pData(celfiles)$groups = listGroups #assign groups to samples
####creates a list of colors specific to each group
fs = factor(pData(celfiles)$groups)
lFs=levels(fs)
numFs=length(lFs)
colors = list()
for (i in 1:numFs){
colors[which(fs==lFs[i])] = i*5
}
colors = unlist(colors)
pData(celfiles)$colors = colors
####end colors
return(celfiles)
}
#if user selects 'ANALYZE GEO FILES', call this function, input GSE ID (length of group assignments must match number of files for testing purposes):
celfiles = processGEOfiles('GSE61989', c('Ctl','Ctl','Ctl','Ctl','RNA_1','RNA_1','RNA_1','RNA_1','RNA_2','RNA_2','RNA_2','RNA_2')) #human example
celfiles = processGEOfiles('GSE37874', c('Ctl','Ctl','Ctl','Ctl','RNA_1','RNA_1','RNA_1','RNA_1','RNA_2','RNA_2','RNA_2','RNA_2')) #mouse example
#### 1) Process files function takes path to celfiles and returns ExpressionFeatureSet object ####
processCELfiles <- function(path,listGroups) {
library(tools)
library(Biobase)
library(oligo)
SampleName = list.files(path = path, pattern = '/*CEL*', ignore.case = T, full.names=T)
celfiles = read.celfiles(SampleName)
pData(celfiles)$title = basename(file_path_sans_ext(SampleName)) #add sample name to pheno
pData(celfiles)$groups = listGroups #add groups to pheno
####creates a list of colors specific to each group
fs = factor(pData(celfiles)$groups)
lFs=levels(fs)
numFs=length(lFs)
colors = list()
for (i in 1:numFs){
colors[which(fs==lFs[i])] = i*5
}
colors = unlist(colors)
pData(celfiles)$colors = colors
####end
return(celfiles)
}
#If user selects 'ANALYZE CEL FILES', call this function, input path of files (length of group assignments must match number of files for testing purposes):
celfiles = processCELfiles('/Users/valdezkm/Documents/2___Combined',c('Ctl_1','Ctl_1','Ctl_1','KO_1','KO_1','KO_1','Ctl_2','Ctl_2','Ctl_2','KO_2','KO_2','KO_2'))
#### 2) QC / Normalize data function takes ExpressionFeatureSet from above and prints pre-normalization plots, QC plots, post-normalization plots. Returns normalized data ExpressionFeatureSet ####
calc = function(raw) {
library(rgl)
library(Biobase)
library(heatmaply)
hist(raw,which="all", main =" Raw Samples distribution") #Raw histogram
nbfacs=nrow(pData(raw))
for (i in 1:nbfacs) {
MAplot(raw,which=i,plotFun=smoothScatter,refSamples=c(1:nbfacs), main='', cex=2) #Raw MAplots
}
boxplot(raw, which="all", main="Boxplots before normalization",
las=2,names=pData(raw)$title, col=pData(raw)$colors) #Raw boxplot
qc = fitProbeLevelModel(raw) #Calculate QC
RLE(qc, main="RLE plot",names=pData(raw)$title, las=2, col=pData(raw)$colors) #RLE
NUSE(qc, main="NUSE plot",names=pData(raw)$title, las=2, col=pData(raw)$colors) #NUSE
#Normalize data
if (raw@annotation=="pd.hg.u133.plus.2" | raw@annotation=="pd.clariom.s.human.ht" | raw@annotation=="pd.clariom.s.human" | raw@annotation=="pd.clariom.s.mouse.ht" | raw@annotation=="pd.clariom.s.mouse" | raw@annotation=='pd.mouse430.2' | raw@annotation=='pd.hg.u133a' | raw@annotation=='pd.hg.u133a.2' | raw@annotation=='pd.hg.u219' | raw@annotation=='pd.mg.u74av2' | raw@annotation=='pd.mouse430a.2' | raw@annotation=='pd.moe430a' | raw@annotation=='pd.hg.u95av2' | raw@annotation=='pd.hg.u133b') {
norm =rma(raw, background=TRUE, normalize=TRUE, subset=NULL)
} else {
norm =rma(raw, background=TRUE, normalize=TRUE, subset=NULL, target="core")
}
hist(norm, main ="Distribution after Normalization") #Normalized histogram
for (i in 1:nbfacs) {
MAplot(norm,which=i,plotFun=smoothScatter,refSamples=c(1:nbfacs), main='', cex=2) #Normalized MAplots
}
boxplot(norm, main="Boxplots after RMA normalization",las=2,
names=pData(raw)$title, col=pData(raw)$colors) #Normalized boxplot
# 3D PCA # #3D PCA
tedf= t(exprs(norm))
if (length(which(apply(tedf, 2, var)==0)) >= 0){
tedf = tedf[ , apply(tedf, 2, var) != 0]
}
pca=prcomp(tedf, scale. = T)
open3d()
bg3d('white')
plot3d(pca$x[,1:3], type='s',size=2, col=pData(raw)$colors)
group.v=as.vector(pData(raw)$title)
text3d(pca$x, pca$y, pca$z, group.v, cex=0.6, adj=1.5)
par3d(mouseMode = "trackball")
# END 3D PCA / BEGIN HEATMAP # #Heatmap
mat=as.matrix(dist(t(exprs(norm))))
rownames(mat)=pData(norm)$title
colnames(mat)=rownames(mat)
print(heatmaply(mat,margins = c(80,120,60,40),colorRampPalette(colors = c("red", "yellow"))))
return(norm)
}
norm_celfiles = calc(celfiles) #Call function
#### 3) Differentially Expressed Genes function takes files, group and contrast data. Returns list of DEGs for each contrast, annotated normalized data, and pheno data ####
# Output should dynamically respond to user-selected contrast
deg = function(norm, listContrasts) {
library(limma)
library(pd.mogene.2.0.st)
library(mogene20sttranscriptcluster.db)
library(pd.hg.u133.plus.2)
library(hgu133plus2.db)
library(pd.hugene.2.0.st)
library(hugene20sttranscriptcluster.db)
library(pd.clariom.s.human.ht)
library(clariomshumanhttranscriptcluster.db)
library(pd.clariom.s.human)
library(clariomshumantranscriptcluster.db)
library(pd.clariom.s.mouse.ht)
library(clariomsmousehttranscriptcluster.db)
library(pd.clariom.s.mouse)
library(clariomsmousetranscriptcluster.db)
library(pd.mouse430.2)
library(mouse4302.db)
library(pd.hg.u133a)
library(hgu133a.db)
library(pd.hugene.1.0.st.v1)
library(hugene10sttranscriptcluster.db)
library(pd.mogene.1.0.st.v1)
library(mogene10sttranscriptcluster.db)
library(pd.hg.u133a.2)
library(hgu133a2.db)
library(pd.huex.1.0.st.v2)
library(huex10sttranscriptcluster.db)
library(pd.hg.u219)
library(hgu219.db)
library(pd.mg.u74av2)
library(mgu74av2.db)
library(pd.mouse430a.2)
library(mouse430a2.db)
library(pd.moe430a)
library(moe430a.db)
library(pd.hg.u95av2)
library(hgu95av2.db)
library(pd.hta.2.0)
library(hta20transcriptcluster.db)
library(pd.moex.1.0.st.v1)
library(moex10sttranscriptcluster.db)
library(pd.hg.u133b)
library(hgu133b.db)
library(pd.hugene.1.1.st.v1)
library(hugene11sttranscriptcluster.db)
library(pd.mogene.1.1.st.v1)
library(mogene11sttranscriptcluster.db)
myfactor <- factor(pData(norm)$groups)
design1 <- model.matrix(~0+myfactor)
colnames(design1) <- levels(myfactor)
fit1 <- lmFit(norm,design1)
contrast.matrix <- makeContrasts(contrasts=cons,levels=design1)
fit2 <- contrasts.fit(fit1, contrast.matrix)
ebayes.fit2=eBayes(fit2)
if (norm@annotation=="pd.mogene.2.0.st") {
Annot <- data.frame(ACCNUM=sapply(contents(mogene20sttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(mogene20sttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(mogene20sttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(mogene20sttranscriptclusterENTREZID), paste, collapse=", "))
} else {
# if (input$Platform=="h133p2") {
if (norm@annotation=="pd.hg.u133.plus.2") {
Annot <- data.frame(ACCNUM=sapply(contents(hgu133plus2ACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hgu133plus2SYMBOL), paste, collapse=", "), DESC=sapply(contents(hgu133plus2GENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hgu133plus2ENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=="pd.hugene.2.0.st") {
Annot <- data.frame(ACCNUM=sapply(contents(hugene20sttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hugene20sttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(hugene20sttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hugene20sttranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=="pd.clariom.s.human.ht") {
Annot <- data.frame(ACCNUM=sapply(contents(clariomshumanhttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(clariomshumanhttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(clariomshumanhttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(clariomshumanhttranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=="pd.clariom.s.mouse.ht") {
Annot <- data.frame(ACCNUM=sapply(contents(clariomsmousehttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(clariomsmousehttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(clariomsmousehttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(clariomsmousehttranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=="pd.clariom.s.mouse") {
Annot <- data.frame(ACCNUM=sapply(contents(clariomsmousetranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(clariomsmousetranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(clariomsmousetranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(clariomsmousetranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=="pd.clariom.s.human") {
Annot <- data.frame(ACCNUM=sapply(contents(clariomshumantranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(clariomshumantranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(clariomshumantranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(clariomshumantranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=="pd.mouse430.2") {
Annot <- data.frame(ACCNUM=sapply(contents(mouse4302ACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(mouse4302SYMBOL), paste, collapse=", "), DESC=sapply(contents(mouse4302GENENAME), paste, collapse=", "), ENTREZ=sapply(contents(mouse4302ENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.hg.u133a') {
Annot <- data.frame(ACCNUM=sapply(contents(hgu133aACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hgu133aSYMBOL), paste, collapse=", "), DESC=sapply(contents(hgu133aGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hgu133aENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.hugene.1.0.st.v1') {
Annot <- data.frame(ACCNUM=sapply(contents(hugene10sttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hugene10sttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(hugene10sttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hugene10sttranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.mogene.1.0.st.v1') {
Annot <- data.frame(ACCNUM=sapply(contents(mogene10sttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(mogene10sttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(mogene10sttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(mogene10sttranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.hg.u133a.2') {
Annot <- data.frame(ACCNUM=sapply(contents(hgu133a2ACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hgu133a2SYMBOL), paste, collapse=", "), DESC=sapply(contents(hgu133a2GENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hgu133a2ENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.huex.1.0.st.v2') {
Annot <- data.frame(ACCNUM=sapply(contents(huex10sttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(huex10sttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(huex10sttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(huex10sttranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.hg.u219') {
Annot <- data.frame(ACCNUM=sapply(contents(hgu219ACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hgu219SYMBOL), paste, collapse=", "), DESC=sapply(contents(hgu219GENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hgu219ENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.ht.hg.u133.plus.pm') {
Annot <- data.frame(ACCNUM=sapply(contents(hgu133plus2ACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hgu133plus2SYMBOL), paste, collapse=", "), DESC=sapply(contents(hgu133plus2GENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hgu133plus2ENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.mg.u74av2') {
Annot <- data.frame(ACCNUM=sapply(contents(mgu74av2ACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(mgu74av2SYMBOL), paste, collapse=", "), DESC=sapply(contents(mgu74av2GENENAME), paste, collapse=", "), ENTREZ=sapply(contents(mgu74av2ENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.mouse430a.2') {
Annot <- data.frame(ACCNUM=sapply(contents(mouse430a2ACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(mouse430a2SYMBOL), paste, collapse=", "), DESC=sapply(contents(mouse430a2GENENAME), paste, collapse=", "), ENTREZ=sapply(contents(mouse430a2ENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.moe430a') {
Annot <- data.frame(ACCNUM=sapply(contents(moe430aACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(moe430aSYMBOL), paste, collapse=", "), DESC=sapply(contents(moe430aGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(moe430aENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.hg.u95av2') {
Annot <- data.frame(ACCNUM=sapply(contents(hgu95av2ACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hgu95av2SYMBOL), paste, collapse=", "), DESC=sapply(contents(hgu95av2GENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hgu95av2ENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.hta.2.0') {
Annot <- data.frame(ACCNUM=sapply(contents(hta20transcriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hta20transcriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(hta20transcriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hta20transcriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.moex.1.0.st.v1') {
Annot <- data.frame(ACCNUM=sapply(contents(moex10sttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(moex10sttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(moex10sttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(moex10sttranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.hg.u133b') {
Annot <- data.frame(ACCNUM=sapply(contents(hgu133bACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hgu133bSYMBOL), paste, collapse=", "), DESC=sapply(contents(hgu133bGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hgu133bENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.hugene.1.1.st.v1') {
Annot <- data.frame(ACCNUM=sapply(contents(hugene11sttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(hugene11sttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(hugene11sttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(hugene11sttranscriptclusterENTREZID), paste, collapse=", "))
} else {
if (norm@annotation=='pd.mogene.1.1.st.v1') {
Annot <- data.frame(ACCNUM=sapply(contents(mogene11sttranscriptclusterACCNUM), paste, collapse=", "), SYMBOL=sapply(contents(mogene11sttranscriptclusterSYMBOL), paste, collapse=", "), DESC=sapply(contents(mogene11sttranscriptclusterGENENAME), paste, collapse=", "), ENTREZ=sapply(contents(mogene11sttranscriptclusterENTREZID), paste, collapse=", "))
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
}
numContrasts = length(cons)
listDEGs = vector("list",numContrasts) #initialize output list for each contrast
for (i in 1:numContrasts)
{
all.genes.con = topTable(ebayes.fit2, coef = i, number=nrow(ebayes.fit2))
all <- merge(all.genes.con, Annot,by.x=0, by.y=0, all.x=T) #annotate
all=all[order(all$P.Value),]
colnames(all)[1]="probsetID"
all$FC = ifelse(all$logFC<0, -1/(2^all$logFC), 2^all$logFC) #add fold change and rearrange columns
all = all[,c(9,12,2,5,6,3,8,10,11,1,4,7)]
# Write out to a file
write.table(all,file=paste(cons[i],"_all_genes.txt",sep=""),sep="\t",row.names=F)
listDEGs[[i]]=all
}
names(listDEGs)=cons
norm_annotated <- merge(exprs(norm), Annot,by.x=0, by.y=0, all.x=T) #write out normalized annotated data
y<-paste("_", sep="")
tNorm = tempfile(pattern = "normalized_data_", tmpdir = getwd(), fileext = paste0(y,'.txt'))
write.table(norm_annotated,file=tNorm,sep="\t",row.names=F)
for (i in 1:length(listDEGs)) { #Volcano plots
dat=listDEGs[[i]]
dat = dat[dat$SYMBOL!='NA',]
log_FC=dat$logFC
log_pval=-log10(dat$P.Value)
Significant=rep("NotSignificant",length(log_FC))
Significant[which(dat$P.Value<0.05 & abs(dat$logFC)>=1)]="AbsLogFoldChange>1 & PValue<0.05"
Significant[which(dat$P.Value<0.05 & abs(dat$logFC)<1)]="PValue<0.05"
Significant[which(dat$P.Value>=0.05 & abs(dat$logFC)>=1)]="AbsLogFoldChange>1"
gene=dat$SYMBOL
volcano_data=as.data.frame(cbind(gene,log_FC,log_pval,Significant))
print(plot_ly(type='scatter', data = volcano_data, x = log_FC, y = log_pval, text = gene, mode = "markers", color = Significant) %>% layout(title=paste0('Volcano plot for: ',names(listDEGs)[i]),xaxis=list(title="Fold Change",range =c(-5,5),tickvals=c(-5,-4,-3,-2,-1,0,1,2,3,4,5),ticktext=c('-32','-16','-8','-4','-2','1','2','4','8','16','32')),yaxis=list(title="-Log10 pvalue",range =c(0,15))))
}
return(list(listDEGs=listDEGs, norm_annotated=norm_annotated, pheno=pData(norm)))
}
# if using processGEOfiles() function for test example, create this contrasts variable:
cons = c("RNA_1-Ctl","RNA_2-Ctl")
# or if using processCELfiles() function for test example, create this contrasts variable:
cons = c("KO_1-Ctl_1","KO_2-Ctl_2")
diff_expr_genes = deg(norm_celfiles,cons) #Call function
#### 4) l2p pathway analysis function, takes DEGs and species as input, returns list of up and downregulated pathways for each contrast ####
# Output should dynamically respond to user-selected contrast
pathways = function(degs,species) {
listPathways = vector("list",length(degs$listDEGs))
for (i in 1:length(degs$listDEGs)) {
up_down = vector("list",2)
all = degs$listDEGs[[i]]
iup=which(all$P.Value<0.05 & all$logFC>=0)
idw=which(all$P.Value<0.05 & all$logFC<0)
fin.up=all[iup,]
if (length(iup) > 500)
{
fin.up=fin.up[order(fin.up$P.Value),]
fin.up=fin.up[1:500,]
}
fin.dw=all[idw,]
if (length(idw) > 500)
{
fin.dw=fin.dw[order(fin.dw$P.Value),]
fin.dw=fin.dw[1:500,]
}
fin.up$SYMBOL = as.character(fin.up$SYMBOL)
fin.dw$SYMBOL = as.character(fin.dw$SYMBOL)
if (species=='human')
{
cat(fin.up$SYMBOL,file=(paste0('_',names(degs$listDEGs[i]),'_Top500_Up.txt')), sep='\n')
cat(fin.dw$SYMBOL,file=(paste0('_',names(degs$listDEGs[i]),'_Top500_Down.txt')),sep='\n')
}
else
{
cat(fin.up$SYMBOL,file=paste0(names(degs$listDEGs[i]),"_Top500temp_Up.txt"),sep='\n')
cat(fin.dw$SYMBOL,file=paste0(names(degs$listDEGs[i]),"_Top500temp_Dw.txt"),sep='\n')
system(paste0("cat ",names(degs$listDEGs[i]),"_Top500temp_Up.txt | grep -v \"^NA\" | ./m2h | grep -v XXXX | cut -f2 -d\" \" >",'_',names(degs$listDEGs[i]),"_Top500_Up.txt"))
system(paste0("cat ",names(degs$listDEGs[i]),"_Top500temp_Dw.txt | grep -v \"^NA\" | ./m2h | grep -v XXXX | cut -f2 -d\" \" >",'_',names(degs$listDEGs[i]),"_Top500_Down.txt"))
}
system(paste0("cat ",'_',names(degs$listDEGs[i]),"_Top500_Up.txt |sort | uniq | ./l2p >",'_',names(degs$listDEGs[i]),"_Pathways_Up.txt"))
system(paste0("cat ",'_',names(degs$listDEGs[i]),"_Top500_Down.txt |sort | uniq | ./l2p >",'_',names(degs$listDEGs[i]),"_Pathways_Down.txt"))
addUpCol = read.delim(paste0('_',names(degs$listDEGs[i]),"_Pathways_Up.txt"), sep = '\t')
addDwCol = read.delim(paste0('_',names(degs$listDEGs[i]),"_Pathways_Down.txt"), sep = '\t')
colnames(addUpCol)=c("pval","fdr","ratio","nb.hits","nb.genes.path","nb.user.genes","tot.back.genes","path_id","source","description","type","gene.list")
colnames(addDwCol)=c("pval","fdr","ratio","nb.hits","nb.genes.path","nb.user.genes","tot.back.genes","path_id","source","description","type","gene.list")
addUpCol = addUpCol[order(addUpCol$pval),]
addDwCol = addDwCol[order(addDwCol$pval),]
addUpCol = addUpCol[,c(8,9,10,11,1,2,3,12,4,5,6,7)]
addDwCol = addDwCol[,c(8,9,10,11,1,2,3,12,4,5,6,7)]
write.table(addUpCol, file = paste0('_',names(degs$listDEGs[i]),"_Pathways_Up.txt"), sep = '\t', row.names = F)
write.table(addDwCol, file = paste0('_',names(degs$listDEGs[i]),"_Pathways_Down.txt"), sep = '\t', row.names = F)
up_down[[1]]=addUpCol
up_down[[2]]=addDwCol
names(up_down) = c("upregulated_pathways","downregulated_pathways")
listPathways[[i]] = up_down
}
names(listPathways) = names(degs$listDEGs)
return(listPathways)
}
l2p_pathways = pathways(diff_expr_genes,'human')
#### 5) Function for gene heatmap from l2p pathways. Input: deg function output, l2p pathways, contrast, choice of up or down pathways, and name of pathway. ####
#Output should change dynamically with user-selected contrast and l2p pathway (from either up OR downregulated pathways)
geneHeatmap = function(degs, paths, contrast, upOrDown, pathway_name) {
library(pheatmap)
human2mouse = read.delim('human2mouse.csv',sep=',')
paths = paths[[contrast]][[upOrDown]]
genes = paths$gene.list[paths$description==pathway_name] #select user input pathway, extract genes
genes = strsplit(as.character(genes),' ')
genes = unlist(genes)
exp = degs$norm_annotated #extract normalized expression, subset by genes, aggregate duplicate symbols by mean
exp = exp[exp$SYMBOL %in% genes,]
if (nrow(exp)==0) {
genes = human2mouse$mouse[human2mouse$human %in% genes]
genes = as.character(genes)
exp = degs$norm_annotated
exp = exp[exp$SYMBOL %in% genes,]
}
exp = subset(exp, select = -c(ACCNUM,DESC,ENTREZ,Row.names))
exp = aggregate(.~SYMBOL,data=exp,mean)
sampleColumns = c(which(degs$pheno$groups==gsub("-.*$","",contrast)),which(degs$pheno$groups==gsub("^.*-","",contrast)))
rownames(exp) = exp$SYMBOL
exp = subset(exp, select = -c(SYMBOL))
exp = exp[,sampleColumns]
if(nrow(exp)>100){ #limit to 100 genes
exp = exp[1:100,]
}
matCol = data.frame(group=degs$pheno$groups[sampleColumns]) #set heatmap parameters
rownames(matCol) = rownames(degs$pheno)[sampleColumns]
matColors = list(group = unique(degs$pheno$colors[sampleColumns]))
names(matColors$group) = unique(degs$pheno$groups[sampleColumns])
path_name = pathway_name
exp = t(scale(t(exp))) #get z-scores by row
if (nrow(exp) > 30){
pheatmap(exp, main=path_name, annotation_col=matCol, annotation_colors=matColors, drop_levels=TRUE, fontsize_row = 6)
} else {
pheatmap(exp, main=path_name, annotation_col=matCol, annotation_colors=matColors, drop_levels=TRUE, fontsize_row = 10)
}
}
geneHeatmap(diff_expr_genes, l2p_pathways, 'RNA_1-Ctl', 'upregulated_pathways','oxidation-reduction process') #if GEO
geneHeatmap(diff_expr_genes, l2p_pathways, 'KO_1-Ctl_1', 'upregulated_pathways','oxidation-reduction process') #if CEL file upload
#### 6) ssGSEA function, takes as input: output from deg function, species, and gene set modules(.gmt). Outputs one table of enrichment scores and tables of diff expr pathways per contrast. Prints ssGSEA heatmap ####
# Output should dynamically respond to user-selected contrast
ss = function(deg_normAnnot, species, geneSet) {
library(GSEABase)
library(GSVA)
normAnnot = deg_normAnnot$norm_annotated
ssgs = normAnnot[normAnnot$SYMBOL!='NA',]
#if human or mouse, prepare data for gsva
if (species=='human') {
ssgs = subset(ssgs, select=-c(ACCNUM,DESC,Row.names,ENTREZ))
ssgs = aggregate(.~SYMBOL,data=ssgs,mean) #aggregate duplicate probes by mean
rownames(ssgs) = ssgs$SYMBOL
ssgs = subset(ssgs, select=-c(SYMBOL))
ssgs = as.matrix(ssgs)
getSet = switch(geneSet, "H: Hallmark Gene Sets"="h.all.v6.1.symbols.gmt", "C1: Positional Gene Sets"="c1.all.v6.1.symbols.gmt", "C2: Curated Gene Sets"="c2.all.v6.1.symbols.gmt",
"C3: Motif Gene Sets"="c3.all.v6.1.symbols.gmt", "C4: Computational Gene Sets"="c4.all.v6.1.symbols.gmt","C5: GO gene sets"="c5.all.v6.1.symbols.gmt",
"C6: Oncogenic Signatures"="c6.all.v6.1.symbols.gmt", "C7: Immunologic Signatures"="c7.all.v6.1.symbols.gmt")
} else {
ssgs = subset(ssgs, select=-c(ACCNUM,DESC,Row.names,SYMBOL))
ssgs = aggregate(.~ENTREZ,data=ssgs,mean) #aggregate duplicate probes by mean
rownames(ssgs) = ssgs$ENTREZ
ssgs = subset(ssgs, select=-c(ENTREZ))
ssgs = as.matrix(ssgs)
getSet = switch(geneSet, "H: Hallmark Gene Sets"="mouse_H_v5p2.gmt", "C2: Curated Gene Sets"="mouse_C2_v5p2.gmt", "C3: Motif Gene Sets"="mouse_C3_v5p2.gmt", "C4: Computational Gene Sets"="mouse_C4_v5p2.gmt",
"C5: GO gene sets"="mouse_C5_v5p2.gmt", "C6: Oncogenic Signatures"="mouse_C6_v5p2.gmt", "C7: Immunologic Signatures"="mouse_C7_v5p2.gmt")
}
gset = getGmt(getSet)
ssgsResults = gsva(ssgs, gset, method='ssgsea') #run ssGSEA
y<-paste("_", sep="") #write out results
tSS = tempfile(pattern = "ssGSEA_enrichmentScores_", tmpdir = getwd(), fileext = paste0(y,'.txt'))
write.table(ssgsResults,file=tSS,sep="\t",col.names=NA)
myfactor <- factor(deg_normAnnot$pheno$groups)
design1 <- model.matrix(~0+myfactor)
colnames(design1) <- levels(myfactor)
fit1 = lmFit(ssgsResults,design1) #DE analysis of ssGSEA enrichment scores
cons = names(deg_normAnnot$listDEGs)
contrast.matrix = makeContrasts(contrasts=cons,levels=design1)
fit2 = contrasts.fit(fit1,contrast.matrix)
ebayes.fit2 = eBayes(fit2)
DEss=vector("list",length(deg_normAnnot$listDEGs))
for (i in 1:length(deg_normAnnot$listDEGs))
{
all.pathways = topTable(ebayes.fit2, coef=i, number=nrow(ebayes.fit2)) #Determine DE pathways
all.pathways = all.pathways[order(abs(all.pathways$P.Value)),]
colnames(all.pathways)[2] = 'Avg.Enrichment.Score'
write.table(all.pathways,file=paste0("_",cons[i],"_ssGSEA_pathways.txt"),sep="\t",row.names=T,col.names=NA)
DEss[[i]] = all.pathways
}
names(DEss)=cons
for (i in 1:length(DEss)){ #Heatmap
sampleColumns = c(which(deg_normAnnot$pheno$groups==gsub("-.*$","",cons[i])),which(deg_normAnnot$pheno$groups==gsub("^.*-","",cons[i]))) #Subset columns (samples)
paths = ssgsResults[rownames(ssgsResults) %in% rownames(DEss[[i]])[1:50],] #Subset rows (pathways)
paths = paths[,sampleColumns]
matCol = data.frame(group=deg_normAnnot$pheno$groups[sampleColumns])
rownames(matCol) = rownames(deg_normAnnot$pheno)[sampleColumns]
matColors = list(group = unique(deg_normAnnot$pheno$colors[sampleColumns]))
names(matColors$group) = unique(deg_normAnnot$pheno$groups[sampleColumns])
paths = t(scale(t(paths)))
pheatmap(paths,annotation_col=matCol,annotation_colors=matColors,drop_levels=TRUE,fontsize=7, main='Enrichment Scores for Top 50 Differentially Expressed ssGSEA Pathways')
}
return(list(ssgsResults=ssgsResults, DEss=DEss))
}
ssGSEA_results = ss(diff_expr_genes,'human','C2: Curated Gene Sets')