I have some RNA-Seq data from leukaemia patients. I want to do unsupervised clustering on them with some other published leukaemia RNA-Seq data and see how they cluster. There are a few problems I encountered while doing this.
I read mix messages of whether using log2(TPM) or rlog(counts) (e.g. Deseq2 rlog or limma voom transformation) for clustering. Which one should I choose?
If I am filtering out genes with low counts, should I do it prior normalisation to library size or after?
I tried using filterByExpr from edgeR for filtering but it removed many of the genes that are only expressed by subtypes with small sample number. If I am to filter the counts "manually", is there a recommendation of how the threshold should be set?
If I should use log2(TPM) for unsupervised clustering, can I treat low TPM (e.g. <2) as 0? As this paper suggested TPM < 2 basically means no expression, is it alright to treat them as 0?