But when you normalize library mass - you sacrifice between gene measurements. Library mass normalization is performed using a different set of methods of which TMM is currently hands down the best. They are absolute on to their respective sample relative to its library mass. Since these methods do not account for library composition bias, your common divisor (library size) does not get equalized between samples,which means even though you have CPM=256.7 for Gene A in sample 1 and CPM=256.7for Gene A in sample 2, if you're library masses are different by 25% (which is totally not unreasonable to expect), then the 'absolute quantification' gets exposed as not really 'absolute' at all.
If you perform within sample normalization using CPM, TPM, FPKM (paried end), RPKM (single read), Quantile, or any other within sample normalization - these are loosely referred to as 'absolute quantification' and as such allow you to compare one gene to another gene within the same sample. There are a variety of natural biases that accompany any RNA-seq experiment that must be correct for in order to consider counts absolute quantification. Its actually a more complex situation than this and frankly, I often hear people speaking about RNAseq data in such terms and it is incorrect to do so. To be clear - raw RNA-seq data (or more specifically, the counts that may be derived from it using software such as HT-seq or Subread featureCount) is NOT absolute quantification.
0 kommentar(er)
