Hi,

ddsFullCountTable <- DESeqDataSetFromMatrix(countData = mouse_count, colData = mouse_pheno, design = ~ batch + age + gender + treatment)  

dds <- DESeq(ddsFullCountTable, test="LRT", reduced = ~ batch + age + gender)  

res_dds <- results(dds, contrast = c("treatment", "med", "ck"))

summary(res_dds) 
# out of 32102 with nonzero total read 
# adjusted p-value < 0.1
# LFC > 0 (up)       : 0, 0%
# LFC < 0 (down)     : 0, 0%
# outliers [1]       : 1269, 3.6%
# low counts [2]     : 0, 0%
# (mean count < 0)
# [1] see 'cooksCutoff' argument of ?results
# [2] see 'independentFiltering' argument of ?results

Here are 5 med mice and 7 check mice (so the ncols is 12 for countData) RNA-Seq data be used to do DE analysis, to find the DEGs affected by medicine, but seems no genes. This problem has confused me for a long time, I checked and tired many times, still haven't been solved. As far as I understand it, I guess:

1. Too few samples (and too many covariates?)?
2. The drug not statistically significant in this case?
3. The code, I guess not?

Or other problems?

Thanks!

Nothing wrong with the code.

There do seem to be a lot of outlier counts. Have you looked at plotPCA?

Run a plotPCA like Michael recommends you

I had an experiment with 2 conditions and 3 biological replicates. By running a PCA analysis, I noticed that two samples were exchanged (a control was actually a treated sample, and viceversa), and this was enough for not getting a single DE gene