[BioC] A metric to determine best filtration in the limma package
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Thu Sep 6 20:20:15 CEST 2012
On Thu, Sep 6, 2012 at 1:52 PM, Mark Lawson <mlawsonvt09 at gmail.com> wrote:
> Hello Bioconductor Gurus!
> (I apologize if this goes through more than once)
> We are currently using limma (through the voom() function) to analyze
> RNA-seq data, represented as RSEM counts. We currently have 246 samples
> (including replicates) and our design matrix has 65 columns.
> My question is in regard to how much we should be filtering our data before
> running it through the analysis pipeline. Our current approach is to look
> for a CPM of greater than 2 in at least half of the samples. The code is:
> keep <- rowSums(cpm(dge) > 2) >= round(ncol(dge)/2)
I'm guessing you are using "normal" rna-seq data (ie. it's not a tag
sequencing something), so just a quick thought (apologies in advance
if I am misunderstanding your setup):
If you are filtering by counts per million without normalizing for
approximate length of your transcript (like an R/FPKM-like measure),
aren't you biasing your filter (and, therefore, data)?
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
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