Search
Question: cpm on normalised counts (was "DESeq normalisation strategy")
0
gravatar for Cittaro Davide
4.5 years ago by
Cittaro Davide240 wrote:
Hi Simon, On May 29, 2013, at 11:46 AM, Simon Anders <anders at="" embl.de=""> wrote: > The notion of "calculating cpm on normalized counts" is hence a > contradiction in terms. Would you like to expand this sentence? I see it is not uncommon to evaluate counts in cpm after normalization. I'm thinking at edgeR and limma (that normalize by TMM)... Moreover, I would like to exploit this thread for another point which still is not clear to my simple mind: normalizing counts (either by TMM or by geommean) makes the comparison at feature level possible, that's why we all trust DESeq (edgeR and limma::voom) and we agree RPKM is evil for that purpose :-) But. Once you have normalized counts, how would you rank features according to their abundance "within" the sample? How can you tell feature A is more represented than feature B in the same sample? Can you just use normalized counts for that? I'm asking this because I'm facing some experimental data (not RNA- seq) where the features are huge genomic domains (megabases, spotted by chip-seq) that change between conditions (in terms of abundance, position and enrichment). I can describe the differences in terms of domain length (and genomic associations to genes, for example), but what about their "height"? I cannot use classical peak height as for normal ChIP-seq data, because that makes no sense at all, and I'm forced to use RPKM. /me confused thanks d
ADD COMMENTlink written 4.5 years ago by Cittaro Davide240
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.2.0
Traffic: 182 users visited in the last hour