Hi to all,
I have RNA-seq data from 31 tissues of 2 different individuals. My goal is to evaluate the expression of various genes of interest and to compare the expression level among these tissues. I read throughout a lot of papers and posts and I tried to set up my pipeline. I mean first of all after quality check of my reads, I trimmed them and mapped with HISAT2. To have an initial idea about gene expression I visualized the results on IGV (I perfectly known that with IGV it is not possible to quantify the expression but I did it just to have an idea). Then I calculated TPM with Salmon from raw reads and I realized different plots in Python (actually TPM from Salmon matched with IGV results). Now my intention is to proceed with Differential Expression Analysis with DESeq2. I have two questions:
1) is it a good pipeline to analyze data from RNA-seq data step by step? (I perfectly know that for expert bioinformaticians all these procedures seem to be like wasting time but unfortunately for me it is the first time I have to analyze RNA-seq data and I want to be sure step by step).
2) Reading different papers and posts I realized that TPM is not so good to compare gene expression among different samples (in my case tissues) and that there isn’t a real cut-off to consider a gene as low or high expressed. So it could be a good thing compare TPM of genes I m interesting in with ”housekeeping” genes to have an idea about low/medium/esxpression before DEA?
As I said before I want to proceed with DESeq2 as every people recommend to have stronger results.
Thank you in advance for your time and suggestion!