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I was wondering if any of you have encountered a situation for bulk RNA-Seq where, possibly due to low sample quality or presence of dead cells, mitochondrial genes are expressed to a very large degree relative to other genes, thus skewing TPM values of all nuclear genes (by effectively scaling them down).
In such a situation, what could I potentially do to alleviate this beyond preparing fresh samples? Could I, for example, exclude 'MT-' genes from samples and then recalculate TPM based on this filtered set of genes?
My aim was to get some idea of within-samples expression levels, hence why going for TPMs as I know that counts normalised by DESeq2, edgeR and the like do not correct for gene length and are inappropriate for within-samples comparisons.
Many thanks in advance for any insight