Deleted:why do i get much more significant genes when i include more different non related samples
Entering edit mode
shaimaa • 0
Last seen 5 weeks ago
United Kingdom

I'm looking into the differentially expressed genes in heat-stressed animals versus normal ones.

When I only done the analysis on the 6 samples (3 heat stressed and 3 non-stressed) I got a few significant genes that are significantly differentially expressed on stress.

When I did the DEseq analysis using data matrix of all samples that include RNA-seq of 40 samples 3 of them are heat stressed the other 3 are non-stressed and the rest are of different other conditions like ageing and cold stress.

dds <- DESeqDataSetFromMatrix(countData = data,
                              colData = samples, 
                              design = ~0+condition)
and in my DEseq contrast

ageing <- results(dds_lrt,
                   contrast = c("condition", "5.months.old", "young"))

stressed <- results(dds_lrt,
                   contrast = c("condition", "heat-stressed", "control"))

by applying that I got hundreds of significant genes.

Is this a right way of doing it and the volcano blot is weird

volcanoplot Any recommendations?

DESeq2 RNAseq123 StatisticalMethod • 259 views
This thread is not open. No new answers may be added
Traffic: 464 users visited in the last hour
Help About
Access RSS

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6