I am facing a minor dilemma, and I thought perhaps you could give me some advice. I have conducted differential expression analysis of a Control vs Mutant RNAseq dataset. I conducted the analysis twice, using different pipelines: PipelineA: Kallisto --> DESeq2 PipelineB: STAR --> featureCounts --> DESeq2
I wanted to get a sense of how different the results would be when "classifying against a transcriptome" and when "quantifying against a genome". PipelineA outputs ~2000 differentially expressed genes. PipelineB outputs ~1600, of which ~1400 are also identified as differentially expressed by PipelineA. Filtering conditions for significance (e.g. FDR < 0.05) were kept the same for both.
My question is, which results should I trust? I read the transcriptome path is usually more accurate, but perhaps it doesn't hurt to be a bit conservative?
Many thanks:), Marcos