Hi, I am analyzing RNA-Seq data. I have 190 PE samples (control: male, female, mutant: male, female) and generated featureCount matrix. For the differential gene expression, I used deseq2 with adjusted 0.05 p-value.
On the other hand, I used kallisto for quantifying abundances of transcripts with the same input data.
My question is that I am getting very less number of transcripts in differential expression after kallisto analysis in comparison to gene analysis results. However, the number of DE should be more in abundances of transcripts rather than a gene-based method.
Please give your opinion:
Here are the results:
Results: Deseq2 gene expression using STAR aligner
adjusted p-value < 0.05 LFC > 0 (up) : 606, 1.2% LFC < 0 (down) : 169, 0.34% outliers  : 7, 0.014% low counts  : 40617, 81%
(mean count < 6)
Deseq2 result with kallisto:
out of 138 with nonzero total read count adjusted p-value < 0.05 LFC > 0 (up) : 124, 90% LFC < 0 (down) : 14, 10% outliers  : 0, 0% low counts  : 0, 0% (mean count < 0)