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14_gseaSpatialAllContrasts.R
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14_gseaSpatialAllContrasts.R
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# File: 14_gseaSpatialAllContrasts.R
# Auth: umar.niazi@kcl.ac.uk
# DESC: gene set enrichment analysis for the datasets
# Date: 18/03/2019
## set variables and source libraries
## libraries to load
library(gage)
lFiles = list.files('results/', pattern='DEAnalysis*', full.names = T, ignore.case = T)
ldfData = lapply(lFiles, function(x) as.data.frame(read.csv(x, header=T, row.names=1, stringsAsFactors = F)))
names(ldfData) = lFiles
sapply(ldfData, nrow)
# put everything in one order by row names
rn = rownames(ldfData[[1]])
head(rn)
ldfData = lapply(ldfData, function(df){
df = df[rn,]
})
sapply(ldfData, function(df) identical(rownames(df), rn))
cvTitle = gsub('results//DEAnalysis', '', names(ldfData))
cvTitle = gsub('.xls', '', cvTitle)
## load spatial coordinates data
oMsigGS.c2 = readList('results/mm10GeneChromosome.gmt')
## choose a contrast to work with loop through
for (i in 1:length(ldfData)){
dfContrast = ldfData[[i]]
# for a contrats of choice create the list
iContFc = dfContrast$logFC
## add enterez ids
names(iContFc) = as.character(dfContrast$ind)
head(iContFc)
head(dfContrast)
oGage = gage(iContFc, oMsigGS.c2)
dfGreater = data.frame(oGage$greater)
#str(dfGreater)
#i = which(dfGreater$p.val < 0.01)
#rownames(dfGreater[i,])
dfLess = data.frame(oGage$less)
#str(dfLess)
#i = which(dfLess$p.val < 0.01)
#rownames(dfLess[i,])
write.csv(dfGreater[,c('p.val', 'q.val', 'set.size')], file=paste('results/', cvTitle[i], '_upregulated_spatial.xls', sep=''))
write.csv(dfLess[,c('p.val', 'q.val', 'set.size')], file=paste('results/', cvTitle[i], '_downregulated_spatial.xls', sep=''))
}