Hi everyone,
I'm doing the differential expression analysis of miRNAs by using edgeR. I have 3 normal samples and 3 cancer samples. At the end I have this result, where FDR are always 1.
Am I doing something wrong? (I'm new in bioinformatics..)
mydata <- read.table("provaR.txt",header = TRUE, check.names = FALSE)
cpm_data <- cpm(data_clean)
group <- c('normal','normal','normal','cancer','cancer','cancer')
y <- DGEList(counts = cpm_data, group = group)
y <- calcNormFactors(y)
y <- estimateDisp(y)
et <- exactTest(y)
results_edgeR <- topTags(et)
results_edgeR$table
logFC logCPM PValue FDR
hsa-miR-10395-3p 2.1245133 2.067156 0.02247458 1
hsa-miR-4479 -2.8776329 1.622607 0.03125763 1
hsa-miR-125a-3p -0.6333918 4.525262 0.04110584 1
hsa-mir-6858 -1.5911778 2.404987 0.04391790 1
hsa-mir-1272 2.0010084 2.137744 0.04611976 1
hsa-miR-654-3p -1.7058564 2.246162 0.04907249 1
hsa-miR-497-5p -0.5813002 4.440483 0.05123472 1
hsa-mir-409 1.1249962 2.713441 0.05229014 1
hsa-mir-935 -0.5925923 4.329391 0.05611091 1
hsa-mir-3187 -0.5953264 4.344462 0.06414303 1
1 answer
For starters, I'm pretty sure you are not supposed to put cpm into DGEList. It wants raw counts. So fix that first.
If your data says there are no significant differences, then that's what it says. Either there is no real difference, or your samples are so variable that you can't be sure there's a difference with only n=3. (Is there a chance that your samples are not in the order normal, normal, normal, cancer, cancer cancer?)
You can do t-tests in Excel...they are not as good as EdgeR, but they will give you a ballpark idea as to whether or not you should expect some genes to be different between the groups.
Log in to answer this question.
edgeR analysis outputs FDR equals 1
I'm very sorry but I can't understand..
What exactly? Your experiment does not have enough power to yield significant results.
Can you show the output of
y$common.dispersion?So, it is normal to have FDR always 1 in my case?
This is the output of
y$common.dispersion: