I wanted to do differential expression analysis of about 10,340 genes collected from smoking vs non smoking mothers and the upregulated and downregulated genes results just does not make sense with both being 0. Is there something I don't understand or something I'm missing? Moreover, the data is pure counts data without any normalization so could that also be the issue? Plus do I need to adjust the alpha value because most of the adjusted p-value are really high.
Code should be placed in three backticks as shown below
dseq <- DESeqDataSetFromMatrix(countData=exp30, colData=meta30, design= ~ smoking_status) dseq dseq<- DESeq(dseq) result <-results(dseq) result summary(result)
out of 10340 with nonzero total read count adjusted p-value < 0.1 LFC > 0 (up) : 0, 0% LFC < 0 (down) : 1, 0.0097% outliers  : 0, 0% low counts  : 0, 0% (mean count < 6)  see 'cooksCutoff' argument of ?results  see 'independentFiltering' argument of ?results