Hi, I am very new to RNA-seq and differential expression analyses, but have been attempting to use DESeq in my analyses (package maintainer: Simon Anders). I was going through the vignette provided here <http: bioconductor.org="" packages="" 2.12="" bioc="" vignettes="" deseq="" inst="" doc="" d="" eseq.p="" df=""> . Is the idea of performing the getVarianceStabilizedData to then be able to go ahead and re-analyse the transformed count data with stabilised variance? I ask because trying to use the value from getVarianceStabilizedData as input to newCountDataSet doesn't work since the "counts" are no longer integers. Thanks in advance, Jamie [[alternative HTML version deleted]]

Hi Jamie, The point of providing the variance stabilization is for applications other than DE testing, for example clustering of samples or other machine learning applications. For DE testing we suggest to use the count-based methods. Mike On Aug 24, 2013 8:07 AM, "James Floyd" <j.a.floyd@qmul.ac.uk> wrote: > Hi, > > I am very new to RNA-seq and differential expression analyses, but have > been > attempting to use DESeq in my analyses (package maintainer: Simon Anders). > > I was going through the vignette provided here > < > http://bioconductor.org/packages/2.12/bioc/vignettes/DESeq/inst/doc/ DESeq.p > df> . Is the idea of performing the getVarianceStabilizedData to then be > able to go ahead and re-analyse the transformed count data with stabilised > variance? I ask because trying to use the value from > getVarianceStabilizedData as input to newCountDataSet doesn't work since > the > "counts" are no longer integers. > > Thanks in advance, > Jamie > > > > [[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]]