hellow ATpoint,

As you suggested i have gone through some literature and found some normalization methods of which i decided to chose calcNormFactors method (which normalization method shouls i choose : TMM or RLE). Sorry to disturb you again but i am not able to convert it into logfold change, what could be applied for this purpose: exact test?

library(edgeR)
y = read.table("counts.txt", header=T, row.names=1, com='')

sampleinfo <- read.delim("sample.txt")
group <- paste(sampleinfo$Status,sampleinfo$CellType,sep=".")
group
group <- factor(group)
table(group)
keep <- rowSums(y > 0.5)>=2
table(keep)
y <- DGEList(y, group=group, genes=y[,1])
y <- y[keep, ,keep.lib.sizes = FALSE]
y$samples

#no normalization

no_norm <- exactTest(y, dispersion=bcv^2)
table(p.adjust(no_norm$table$PValue, method="BH")<0.05)


#normalize
TMM <- calcNormFactors(y, method = "TMM") 
bcv <- 0.2
TMM <- exactTest(TMM, dispersion=bcv^2)
table(p.adjust(TMM$table$PValue, method="BH")<0.05)

RLE <- calcNormFactors(y, method = "RLE") 
bcv <- 0.2
RLE <- exactTest(RLE, dispersion=bcv^2)
table(p.adjust(RLE$table$PValue, method="BH")<0.05)

uq <- calcNormFactors(y, method = "upperquartile") 
bcv <- 0.2
uq <- exactTest(uq, dispersion=bcv^2)
table(p.adjust(uq$table$PValue, method="BH")<0.05)



library(gplots)

get_de <- function(x, pvalue){
  my_i <- p.adjust(x$PValue, method="BH") < pvalue
  row.names(x)[my_i]
}

my_no_norm <- get_de(no_norm$table, 0.05)
my_tmm <- get_de(TMM$table, 0.05)
my_rle <- get_de(RLE$table, 0.05)
my_uq <- get_de(uq$table, 0.05)

gplots::venn(list(no_norm = my_no_norm, TMM = my_tmm, RLE = my_rle, UQ = my_uq))

venn diagram

Please help, i am very confused.