Dear all, I am new to this list so kindly forgive me if this question has been already answered - but in this case I would be grateful if you could point me to the right answer I am using edgeR for DEG analysis of SAGE tags and I noticed that, when using tagwise instead of common dispersion, the number of differentially expressed genes raises if I consider uncorrected P-values. However, apparently the number of genes called siginficant with BH-corrected drops significantly Is there a rationale behind this or it is just my impression ? Thanks in advance and kind regards, Alessandro G. -- Alessandro Guffanti - Head, Bioinformatics, Genomnia srl Via Nerviano, 31 - 20020 Lainate, Milano, Italy Ph: +39-0293305.702 Fax: +39-0293305.777 http://www.genomnia.com "When you're curious, you find lots of interesting things to do." (Walt Disney) ----------------------------------------------------------- Il Contenuto del presente messaggio potrebbe contenere informazioni confidenziali a favore dei soli destinatari del messaggio stesso. Qualora riceviate per errore questo messaggio siete pregati di cancellarlo dalla memoria del computer e di contattare i numeri sopra indicati. Ogni utilizzo o ritrasmissione dei contenuti del messaggio da parte di soggetti diversi dai destinatari ? da considerarsi vietato ed abusivo. The information transmitted is intended only for the per...{{dropped:8}}

Dear Alessandro, The phenomenon you observe is not generally true, and it is does not represent "overcorrection for multiple tests". It is likely to be specific to your data set. Please note that we do not recommend the use of common dispersion except for interpretation purposes. We always recommend tagwise dispersion. If you were to use common dispersion for differential expression when the true dispersions are heterogeneous, and I believe that they generally are, then you would under-estimate the dispersion for some genes, leading to overly small p-values, and over-estimate for others, leading to overly large p-values. So it would be perfectly possible to get a few very small p-values before correction for multiple testing but get less significance after adjustment for a particular data set. The bottom line is, use tagwise dispersion unless you have just one residual df. Best wishes Gordon > Date: Wed, 04 Apr 2012 16:30:06 +0200 > From: "alessandro.guffanti at genomnia.com" > <alessandro.guffanti at="" genomnia.com=""> > To: bioconductor at r-project.org > Subject: [BioC] edgeR and tagwise dispersion: overcorrection for > multiple tests? > > Dear all, I am new to this list so kindly forgive me if this question > has been already answered - > but in this case I would be grateful if you could point me to the right > answer > > I am using edgeR for DEG analysis of SAGE tags and I noticed that, when > using tagwise instead of common dispersion, the number of differentially > expressed genes raises if I consider uncorrected P-values. > > However, apparently the number of genes called siginficant with > BH-corrected drops significantly > > Is there a rationale behind this or it is just my impression ? > > Thanks in advance and kind regards, > > Alessandro G. > > > -- > > Alessandro Guffanti - Head, Bioinformatics, Genomnia srl > Via Nerviano, 31 - 20020 Lainate, Milano, Italy > Ph: +39-0293305.702 Fax: +39-0293305.777 > http://www.genomnia.com > "When you're curious, you find lots of interesting things to do." > (Walt Disney) ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}