Dear all, I have a question. It is better to perform this analysis using CPM or TPM values? Other times I performed this analysis using TPM. This time I have the replicate values only for CPM, while for TPM I have only the mean for each condition. Which is the best way?
Thanks Francesca
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francesca3 •
As a input to the Broad Institute's GSEA program, one should use any type of expression data which is properly normalised such that cross-sample differences can be faithfully gauged.
That means using any of these:
And one should not use raw counts or any of these types of expression levels: FPKM, RPKM, TPM etc.
Why would FPKM/RPKM/TPM not be acceptable? I would find those superior to using CPM.
For most intents and purpose TPM is superior to CPM (at least for short read RNA-seq; where the length of a given transcript affects the number of reads produced from said transcript). However, CPM should give comparable results to using TPM. In your case, I would try both and compare the results.
Use TPM. CPM are not normalized for gene length length meaning longer genes will appear more highly expressed when using CPM (and vise versa).
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Which tool are you using? When I use FGSEA, I pass in a differential-expression summary statistic, rather than values from individual samples. For a given gene, the statistic I use is (-log10 of unadjusted p-value) * (sign of differential expression coefficient).
As others have mentioned TPM is generally better - however the choice may be tool-specific.
For example, you cannot use TPM normalised counts for differential gene expression analysis with EdgeR.