Basically I want to redo the methodology of the paper:
https://www.nature.com/articles/s41598-018-23492-2
I choose microarray data GSE75693 of 30 patients with stable kidney
transplantation and 15 with BKVN to identify differentially expressed genes
(DEGs). I performed this in GEO2R and find R script there and Runs R script
Successfully on R studio as well. The R script is :
Differential expression analysis with limma
library(Biobase) library(GEOquery) library(limma)
load series and platform data from GEO
gset <- getGEO("GSE75693", GSEMatrix =TRUE, AnnotGPL=TRUE) if
(length(gset)> 1) idx <- grep("GPL570", attr(gset, "names")) else
idx <- 1 gset <-
gset[[idx]]
make proper column names to match toptable
fvarLabels(gset) <- make.names(fvarLabels(gset))
group names for all samples
gsms <-
paste0("000000000000000000000000000000XXXXXXXXXXXXXXX11111",
"1111111111XXXXXXXXXXXXXXXXXXX") sml <- c() for (i in
1:nchar(gsms)) {
sml[i] <- substr(gsms,i,i) }
eliminate samples marked as "X"
sel <- which(sml != "X") sml <- sml[sel] gset <- gset[ ,sel]
log2 transform
exprs(gset) <- log2(exprs(gset))
set up the data and proceed with analysis
sml <- paste("G", sml, sep="") # set group names fl <-
as.factor(sml)
gset$description <- fl design <- model.matrix(~ description + 0, gset)
colnames(design) <- levels(fl) fit <- lmFit(gset, design) cont.matrix
<-
makeContrasts(G1-G0, levels=design) fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2, 0.01) tT <- topTable(fit2, adjust="fdr",
sort.by="B",
number=1250) tT <- subset(tT,
select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))
DEGs = subset(tT, P.Value < 0.01 & logFC >2)
*Problem :*
But the problem is that I can't find any DEGs based on the threshold P <
0.01 and fold change >2.0 plz help me
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