Question: edgeR normalization and partial correlation matrix calculation
gravatar for moxu
3.3 years ago by
moxu460 wrote:

I am learning "" to study gene-gene-interactoin (GGI). can generate a partial correlation matrix (PCM) based on gene expression levels (RNA-seq gene counts), and the PCM can be used for building GGI. However, the raw or expected gene counts need to normalized to create a PCM for better results, I'd assume.

I have to admit that I fell in love with edgeR for RNA-seq analysis. It's really powerful. So I am wondering if the raw or expected gene counts can be normalized for the purpose of creating a PCM. A few questions regarding such the normalization:

  • Is cpm a good normalization for such a purpose? It seems CPM only changes the original gene count by scaling. How about the fancy Bayes shrinkage modification and dispersion estimate and such? Aren't these important as well?
  • should "logcpm <- cpm(y, prior.count=2, normalized.lib.sizes=TRUE, log=TRUE)" be called after

    y <- DGEList(counts=d);

    Or after

    y <- calcNormFactors(y); # global normalization

    Or even after

     y <- estimateDisp(y, dsgn);
  • is logcpm better than cpm for the purpose of getting PCM and later on to rig out GGI? I am inclined towards logcpm.

rna-seq next-gen R gene • 1.2k views
ADD COMMENTlink written 3.3 years ago by moxu460

logCPM might not be good because I got ERCC.00112 shown on a network with the strongest edges (branch factor 3~5).

Any insight?

ADD REPLYlink modified 3.2 years ago • written 3.3 years ago by moxu460
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