Question: how to get Differentially expressed genes from TCGA normalized data.
0
gravatar for oghzzang
2.8 years ago by
oghzzang40
oghzzang40 wrote:

I'm working on datas from TCGA's pipeline. we have 'rsem_isoforms_normalized.txt', 'rsem_gene_normalized.txt', of 215 samples. and this samples have number of 20501 genes.

I have these question:

I want to get differentially expression genes from 215 data. Group 1 dim is 195 (samples)* 20501 (genes) Group 2 dim is 20 (samples)*20501 (genes)

I have already run 2 t-tests

multtest's mt.maxT in r

PP=mt.maxT(Counts, groups, test="t", B=5000)
PP$fdr=p.adjust(PP$rawp, method = "fdr")

number of FDR =< 0.05 genes are only 35.

stats's t.test in r

t.result <- apply(TotalCounts, 1, function(x) t.test(x[1:ncol(Counts.C)], x[ncol(Counts.C)+1:ncol(TotalCounts)], paired=F, var.equal = F))
f$p_value <- unlist(lapply(t.result, function(x) x$p.value))
f$fdr <- p.adjust(TotalCounts$p_value, method = "fdr")

number of FDR =< 0.05 genes are 2000.

this 2 group's adjusted p values by BH are completely different.

In this situation, how can I get DEGs?

rna-seq • 853 views
ADD COMMENTlink modified 2.8 years ago by Kevin Blighe67k • written 2.8 years ago by oghzzang40
1

You might consider using either edgeR or DESeq2, both of which are designed to deal with RNA-seq data.

ADD REPLYlink written 2.8 years ago by Sean Davis26k

Thanks Davis,

Can I use edgeR by using quartile normalized RNA-seq data?

quartile normalization is conducted to make 'rsem_gene_normalized.txt'

ADD REPLYlink modified 2.8 years ago • written 2.8 years ago by oghzzang40
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 1558 users visited in the last hour