[BioC] Array data vs. Next Gen with log 2 Fold Change

Gordon K Smyth smyth at wehi.EDU.AU
Thu Jul 14 00:05:49 CEST 2011

Dear John,

The limma equivalent for RNA-Seq is the edgeR package.  However, with no 
replicates, this won't do you much good.  If you only want log2-fold 
changes from your RNA-Seq data, this is easy, although you have to decide 
what you'll do with zero counts.  I suggest, read in your counts into 
variables y1 and y2, then

    lib.size1 <- sum(y1)
    lib.size2 <- sum(y2)
    logFC <- log2((y1+0.5)/(lib.size1+0.5)/(y2+0.5)*(lib.size2+0.5))

Best wishes

> Date: Mon, 11 Jul 2011 22:45:17 +0100
> From: john herbert <arraystruggles at gmail.com>
> To: bioconductor at r-project.org
> Subject: [BioC] Array data vs. Next Gen with log 2 Fold Change
> I have microarray data, which is 2 colour agilent human of 3 technical 
> replicates. Green dye case and Red dye control. I have analysed in 
> Limma, normalising within arrays and between arrays using aQuantile 
> normalisation.
> I also have some Next gen RNAseq data that has been mapped to the Refseq 
> transcriptome and I have these raw counts. However there are no 
> replicates; only one case and one control.
> I want to plot how the Log2 Fold change is correlated between the two 
> data sets as they are looking at similar samples.
> The microarray data is easy as Limma reports log2 fold change but NGS on 
> the other hand does not.
> What would be the best package/approach to generating a log2 fold change 
> of the next gen counts?
> I am thinking they should be quantile normalised as the microarray data
> is????

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