Question: edgeR normalization and partial correlation matrix calculation
1
gravatar for moxu
2.4 years ago by
moxu440
moxu440 wrote:

I am learning "ridge.net" to study gene-gene-interactoin (GGI). ridge.net 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 • 956 views
ADD COMMENTlink written 2.4 years ago by moxu440

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 2.4 years ago • written 2.4 years ago by moxu440
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 2.3.0
Traffic: 2024 users visited in the last hour