Entering edit mode
5.1 years ago
vm.higareda ▴ 30
I am conscious that there are different programs to detect differential expression in transcriptome data, but would be correct if I compare the cpm of the same gene between different treatments, only to have an idea about the variance betwen genes.
I can use de cpm function of edgeR
What do you think?
Hello, please try to explain better what you are trying to do.
cpm()will normalise whatever data you supply to it - you can then easily determine gene-wise variance using the
var()function, or, better, summarise the variance (rather, covariance) using principal components analysis.
I would like to take a look of counts of one specific gene along different treatments so I think that could be better do it using the cpm not the raw counts. What do you think?
The idea is detect outlier counts in a specific gene not it all the treatment
Well, yes, it is always better to use normalised counts.If you are interested in a particular gene, why not just do a differential expression analysis?
I did differential expression analysis with edgeR and Deseq, but I would like to compare one gene along different treatments not only pairwise comparison. I am confused how to do that
Would something like this help for the experimental setup that you have: https://support.bioconductor.org/p/95602/#95633