Dear list, I am really interested in using DESeq2 and I have carefully read the vignette, but I am not sure to have all correctly understood. I have a few questions about the dispersion estimated with the estimateDispersions() function : 1. What is the default option used with fitType ? Is it the new "mean" option ? 2. Is the dispersion estimated for each group (one value per gene for the "treated" group and another value per gene for the "untreated" group, for example) or is the dispersion estimated for all pooled samples ? I also have noticed that the results table is less complete than the one from DESeq. In particular "baseMeanA" and "baseMeanB" are not shown anymore. Can you please tell me how to access to these values ? Thank you in advance. Best regards, Amandine ----- Amandine Fournier Lyon Neuroscience Research Center and Lyon Civil Hospitals (France) [[alternative HTML version deleted]]

Hi Amandine, On May 3, 2013 6:57 PM, <amandine.fournier@chu-lyon.fr> wrote: > > Dear list, > > I am really interested in using DESeq2 and I have carefully read the vignette, but I am not sure to have all correctly understood. I have a few questions about the dispersion estimated with the estimateDispersions() function : > 1. What is the default option used with fitType ? Is it the new "mean" option ? The default fitType is "parametric". > 2. Is the dispersion estimated for each group (one value per gene for the "treated" group and another value per gene for the "untreated" group, for example) or is the dispersion estimated for all pooled samples ? The dispersion is estimated across all groups, accommodating a 'crossed' design not possible using pooling methods. More details on the methodology are provided in the help for estimateDispersions. > > I also have noticed that the results table is less complete than the one from DESeq. In particular "baseMeanA" and "baseMeanB" are not shown anymore. Can you please tell me how to access to these values ? > We have tried to generalize the two condition case, but you can recreate these columns with something along the lines of: baseMeanA <- rowMeans(counts(dds, normalized=TRUE)[,colData(dds)$condition == "A"]) best, Mike > Thank you in advance. > Best regards, > Amandine > > ----- > Amandine Fournier > Lyon Neuroscience Research Center > and Lyon Civil Hospitals (France) > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor [[alternative HTML version deleted]]