Dear all, I was wondering wether it would change the results if we provide the initial library size, the mapped reads number or just the sum of counts to perform an RNAseq analysis, thanks in advance, anna -- output of sessionInfo(): R version 2.15.1 (2012-06-22) Platform: i386-pc-mingw32/i386 (32-bit) locale: [1] LC_COLLATE=French_France.1252 LC_CTYPE=French_France.1252 [3] LC_MONETARY=French_France.1252 LC_NUMERIC=C [5] LC_TIME=French_France.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] edgeR_3.0.0 limma_3.14.1 -- Sent via the guest posting facility at bioconductor.org.

Hi Anna, Short answer is yes, it could/would change the results. In my experience, these different choices are typically close to each other (or, at least proportional). It is safest to use a standard analysis (column sums of count table, which you may want to call calcNormFactors on). As usual, it is always best to LOOK at your data (e.g. plotSmear, in this case) to get a sense of whether these factors have an effect. Best, Mark On 08.11.2012, at 00:58, anna [guest] wrote: > > Dear all, > I was wondering wether it would change the results if we provide the initial library size, the mapped reads number or just the sum of counts to perform an RNAseq analysis, > thanks in advance, > anna > > -- output of sessionInfo(): > > R version 2.15.1 (2012-06-22) > Platform: i386-pc-mingw32/i386 (32-bit) > > locale: > [1] LC_COLLATE=French_France.1252 LC_CTYPE=French_France.1252 > [3] LC_MONETARY=French_France.1252 LC_NUMERIC=C > [5] LC_TIME=French_France.1252 > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] edgeR_3.0.0 limma_3.14.1 > > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor ---------- Prof. Dr. Mark Robinson Bioinformatics Institute of Molecular Life Sciences University of Zurich Winterthurerstrasse 190 8057 Zurich Switzerland v: +41 44 635 4848 f: +41 44 635 6898 e: mark.robinson at imls.uzh.ch o: Y11-J-16 w: http://tiny.cc/mrobin ---------- http://www.fgcz.ch/Bioconductor2012