Hi.
I've performed my DEseq2 analysis on my samples. However, when I take a look at the resulting output, all of my p-adjusted values are the same for all genes and are equal to 0.9999068. Does this mean I have created an error upstream of analysis?
Also when I take a look at the summary of the results the LFC up and down as 0 for both. Does this indicate no significant log fold changes?
I'd really appreciate any advice on this please.
Thank you!
# deseq2 analysis design
dds1 <- DESeqDataSetFromMatrix(countData = countdata,
colData = colData,
design = ~ Treatment)
# subset Athero samples
dds1c <- dds1b[,dds1b$Sample == "Athero"]
# set untreated as reference for differential gene expression
dds1c$Treatment <- relevel(dds1c$Treatment, ref = "untreated")
# Run Deseq2
FCdds <- DESeq(dds1c)
# View results of deseq2 data output
resFC <- results(FCdds)
# View summary of deseq2 data output
summary(resFC)
out of 17755 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 0, 0%
LFC < 0 (down) : 0, 0%
outliers [1] : 24, 0.14%
low counts [2] : 0, 0%
(mean count < 0)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
Hi,
Thank you so much! I've had a look at a previous response you made to the same issue someone else had where you mentioned the paper in the link below:
http://www.jstor.org/stable/2346101
Unfortunately, I don't seem to have access to this via my institution.
I was wondering if the reason for the exact same P-adj values for all genes is to do with an error on my part?
The fact that some adjusted pvalues are identical is a direct consequence of the algorithm and not a problem.