So I performed both gene level and transcript level expression analysis. I have 7 samples (matching) of normal and cancer. For gene level analysis, I use Salmon to both (pseudo)align and quantify readcount. I then use DESeq2 library from Bioconductor.
For gene level analysis, I follow Kallisto workflow and I got the beta value. I already modified the parameter to use log 2 so that the beta value can be interpreted as log fold change.
After comparing the result, I noticed that many transcript are not giving significant result while gene that are related to those transcript are found to be differentially significant.
Inspecting the data, I noticed that each transcript readcount variance are quite big bit if using gene level which accummulate the readcount frm all transcript per gene the variance are not that big. That is why on gene level, I can found which gene are found to be significantly different. Compare to transcript level, I cannot get significant result so I don't know which transcript are differentially expressed.
My target is I want to distinguish which transcript are differentially expressed. I noticed that some genes, while having many transcript, not all of the transcripts are being expressed.
My question is, is there any way to handle this insignificant result?
I haven't tried HISAT2+StringTie+Ballgown workflow though. Anyone can share their experience using this workflow to be useful?