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

john herbert arraystruggles at gmail.com
Thu Jul 14 06:36:47 CEST 2011

Dear Gordon,
After experimenting in Excel, it was a typo I think.

But I am still interested why no quantile normalization? Is that
because of no replicates?

Thank you,

Kind regards,


On Thu, Jul 14, 2011 at 5:15 AM, john herbert <arraystruggles at gmail.com> wrote:
> Dear Gordan,
> Thank you for your explanation. From my simplistic point of view, I
> wonder why it is different each side of the division. So to separate
> this out.
> logFC <- log2(   (y1+0.5) / (lib.size1+0.5)    /
> (y2+0.5)*(lib.size2+0.5)   )
> On the left side you divide y1 by lib.size but on the right side you
> multiply Y2 by lib.size?
> To me, each side of the division does not look equivalent?
> Is that definitely right? I make the assumption that quantile
> normalisation is not needed?
> Please explain.
> Thanks.
> John.
> On Wed, Jul 13, 2011 at 11:05 PM, Gordon K Smyth <smyth at wehi.edu.au> wrote:
>> 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
>> Gordon
>>> 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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