I am using DESeq for RNAseq data. I am interested in comparing the distribution of my samples individually with that of the model used by DESeq for all of my data, hoping to rationalise exclusion of several samples by this method. Where can I find the estimated mean, variance for the model fitted to my data within the output? I assume it is created after the nbinomtest function but have dissected the output without success. I hope someone can point me in the right direction. Kind regards, Bruce. ---------------------------------------------------------------------- -------- Attention: This e-mail is privileged and confidential. If you are not the intended recipient please delete the message and notify the sender. Any views or opinions presented are solely those of the author. This email was scanned by Teagasc and has been certified virus free with the pattern file currently in use. This however cannot guarantee that it does not contain malicious content. Tabhair aire: Ta an r-phost seo faoi phribhleid agus faoi run. Mura tusa an duine a bhi beartaithe leis an teachtaireacht seo a fhail, scrios e le do thoil agus cuir an seoltoir ar an eolas. Is leis an udar amhain aon dearcai no tuairimi a leiritear. Scanadh an r-phost seo le Teagasc agus deimhniodh go raibh se saor o vioras leis an bpatrunchomhad ata in usaid faoi lathair. Ni feidir a rathu leis seo afach nach bhfuil abhar mailiseach ann. [[alternative HTML version deleted]]

Jan/11/12 1:47 PM, Bruce Moran(External) scripsit:: > I am using DESeq for RNAseq data. I am interested in comparing the > distribution of my samples individually with that of the model used by > DESeq for all of my data, hoping to rationalise exclusion of several > samples by this method. Where can I find the estimated mean, variance > for the model fitted to my data within the output? I assume it is > created after the nbinomtest function but have dissected the output > without success. I hope someone can point me in the right direction. > Dear Bruce thanks for the question. In DESeq, mean and variance are computed for each gene, but are computed across samples, and are not a per-sample property. If you would like to do per-sample quality control, my suggestion is to use variance stabilising transformation (see Section 6 of the vignette) and then the arrayQualityMetrics package. Best wishes Wolfgang Wolfgang Huber EMBL http://www.embl.de/research/units/genome_biology/huber