I'm currently perform some analyses involving cross-project expression data. Because it involves linear equations, we have decided to take log(TPM) as that algorithm's input.
While using older workflows involving rsubread or htseq-count would always require us to perform between-sample normalization, newer transcript quantification tools such as RSEM, Kalisto and Salmon gives out reads and (at a minimum) TPM as their raw output.
But even in that case, should I take out the reads, normalize it with DESeq2/edgeR, and calculate the TPMs instead? I'm not particularly comfortable with not doing between-sample normalizations, but I have a feeling that it's the norm these days.
While using older workflows involving rsubread or htseq-count would always require us to perform between-sample normalization, newer transcript quantification tools such as RSEM, Kalisto and Salmon gives out reads and (at a minimum) TPM as their raw output.
But even in that case, should I take out the reads, normalize it with DESeq2/edgeR, and calculate the TPMs instead? I'm not particularly comfortable with not doing between-sample normalizations, but I have a feeling that it's the norm these days.
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