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, John. 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???? >> >> ______________________________________________________________________ >> The information in this email is confidential and intended solely for the >> addressee. >> You must not disclose, forward, print or use it without the permission of >> the sender. >> ______________________________________________________________________ >> >