Thanks a lot for the clarifications on a weekend. Sorry I could not get back earlier. It seems like what I am trying to do should work out and not introduce and significant biases. Cheers! -Abhi On Sun, Feb 5, 2012 at 6:59 AM, Wolfgang Huber <whuber at="" embl.de=""> wrote: > A clarification (after off-list request): there are two possibilties for > double counting, and with below post I'm refering to only one of them: > > 1. Creating a transcript-level count for each possible transcript of a gene, > essentially by *treating each transcript as a separate 'gene'*, and then > calling DESeq or analgous. This is what the below post refers to. > > 2. Counting the reads touching each exon, and then *summing these numbers up > over all exons of a gene* to get a per-gene (or per transcript) value. That > would be wrong, since then those reads that touch more than one exon are > multiply counted and mess up the statistical model. > > ? ? ? ?Best wishes > ? ? ? ?Wolfgang > > Feb/5/12 12:16 PM, Wolfgang Huber scripsit:: > >> Dear Abishek >> >> there was some anxiety regarding double-counting / redundancy in this >> thread. Actually, there is very little reason to worry. DESeq tests >> sequentially one hypothesis after the other. It does not matter whether >> they are correlated or not. >> >> The one consideration where the correlations / redundancy can matter is >> multiple testing correction. As long as you go for FDR, again there is >> little to worry, since the redundancy pops up both in the numerator and >> denominator of the ratio (the "R" in FDR) and at least to good enough >> approximation cancels out. >> >> If you go for family-wise error rate (FWER) and, say, Bonferroni >> correction, then the redundancy and the increase in number of tests do >> matter. But there seem few reasons to use FWER/Bonferroni in this context. >> >> Hope this helps >> Wolfgang >> >> Feb/2/12 12:46 AM, Abhishek Pratap scripsit:: >>> >>> Hi All >>> >>> I am wondering if conceptually I can use the DESeq to test for >>> differential >>> transcript expression compared to genes. In our case we have generated a >>> transcript model based on RNA-Seq and if we try to collapse those >>> transcripts to genes in order to do gene level differential expression >>> many >>> exons are collapsed to give rise to artificial exons. >>> >>> >>> eg : >>> >>> >>> Transcript 1 : ---------------------- (exon) >>> Transcript 2 : -----------------------------(exon ) >>> >>> Gene level : -------------------------------------------- (exon) >>> >>> Also another thing that comes to my mind if the effect of double counting >>> if I take the read counts at transcript level due to exon redundancy. >>> >>> I would love to hear from your experience. >>> >>> Thanks! >>> -Abhi >>> >>> [[alternative HTML version deleted]] >>> >> >> Best wishes >> Wolfgang >> >> Wolfgang Huber >> EMBL >> http://www.embl.de/research/units/genome_biology/huber >> >> _______________________________________________ >> 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 > > > > -- > Best wishes > ? ? ? ?Wolfgang > > Wolfgang Huber > EMBL > http://www.embl.de/research/units/genome_biology/huber > > _______________________________________________ > 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