Works! Thanks! -Sean
On Mon, Jul 18, 2011 at 9:54 PM, Gordon K Smyth <sm...@wehi.edu.au> wrote: > Hi Sean, > > Sorry, the code I gave works as is with the devel version edgeR. With the > official release version you have to set: > > design <- matrix(1,ncol(y),1) > > to get the same effect. > > > Best wishes > Gordon > > ------------------------------**--------------- > Professor Gordon K Smyth, > Bioinformatics Division, > Walter and Eliza Hall Institute of Medical Research, > 1G Royal Parade, Parkville, Vic 3052, Australia. > sm...@wehi.edu.au > http://www.wehi.edu.au > http://www.statsci.org/smyth > > On Mon, 18 Jul 2011, Sean Ruddy wrote: > > Hi Gordon, >> >> I wasn't able to get your suggestion to work. estimateGLMCommonDisp() >> seems >> to want explicit values for the design. If I leave the design argument >> empty >> I get the error, >> >> Error in as.matrix(design) : >> argument "design" is missing, with no default >> >> I have release 2.8 installed. My code is >> >> y <- DGEList( countMat ) >> y$offset <- log( totals ) >> y <- estimateGLMCommonDisp( y , offset = y$offset ) >> >> Sorry if I'm missing something obvious. >> >> Thanks, >> Sean >> >> >> On Fri, Jul 15, 2011 at 7:26 PM, Gordon K Smyth <sm...@wehi.edu.au> >> wrote: >> >> Hi Sean, >>> >>> On Fri, 15 Jul 2011, Sean Ruddy wrote: >>> >>> Hi Gordon, >>> >>>> >>>> Thanks for the response. One of my data sets has 8 conditions and no >>>> replicates and so I wanted to emulate DESeq's way of pooling the samples >>>> and >>>> also use an offset matrix. I was hoping to avoid doing it manually so >>>> that I >>>> don't mess it up. I could do this all in edgeR and pool the samples but >>>> I'm >>>> not sure how well this would work under edgeR vs. DESeq. >>>> >>>> >>> edgeR has a very flexible interface, so there was no need to explicitly >>> introduce a "pooled" method. Instead, this sort of thing can be handled >>> by >>> the usual functions in the usual way. Suppose you have a data object y, >>> which includes an offset matrix: >>> >>> y$offset <- your matrix >>> >>> Then you can estimate the "pooled" dispersion simply by: >>> >>> y <- estimateGLMCommonDisp(y) >>> >>> The fact that you don't supply a design matrix means that the samples are >>> automatically treated as one group, i.e., pooled. You can estimate a >>> trended or tagwise dispersions in the same way. Then >>> >>> fit <- glmFit(y,design) etc >>> >>> will do any analysis you want using dispersions estimated when the >>> samples >>> were pooled. >>> >>> I and the other edgeR authors are anxious to get feedback, so write again >>> if this doesn't turn out to be clear. >>> >>> I am curious though what sounds off to you in my previous email. I don't >>> >>>> feel entirely comfortable doing this manually but hopefully it's just >>>> because I left out some details. I was trying to follow the DESeq method >>>> and >>>> the only difference I saw was in the size factor calculations which I >>>> changed for my own needs by using the offset values for each tag and >>>> sample. >>>> >>>> >>> Even if you could estimate the variances yourself, I don't see any manual >>> way that you could perform valid statistical tests, while correctly >>> accounting for the offsets. The whole negative binomial methodology >>> requires genuine counts rather than adjusted counts. So handling the >>> offsets needs to be built-in. >>> >>> Best wishes >>> Gordon >>> >>> I appreciate the help! >>> >>>> >>>> Best, >>>> Sean >>>> >>>> On Fri, Jul 15, 2011 at 12:02 AM, Gordon K Smyth <sm...@wehi.edu.au> >>>> wrote: >>>> >>>> Hi Sean, >>>> >>>>> >>>>> I'm curious to know why not use edgeR, since edgeR does what you want >>>>> and >>>>> DESeq doesn't? >>>>> >>>>> I might be wrong, but the manual analysis that you describe doesn't >>>>> sound >>>>> right. >>>>> >>>>> Best wishes >>>>> Gordon >>>>> >>>>> Date: Thu, 14 Jul 2011 12:54:49 -0700 >>>>> >>>>> From: Sean Ruddy <srudd...@gmail.com> >>>>>> To: bioc-sig-sequencing@r-project.******org<bioc-sig-sequencing@r-** >>>>>> ** >>>>>> project.org >>>>>> <bioc-sig-sequencing@r-**project.org<bioc-sig-sequencing@r-project.org> >>>>>> >> >>>>>> >>>>>> Subject: [Bioc-sig-seq] Supplying own variance functions and adjusted >>>>>> counts to a DESeq dataset >>>>>> >>>>>> Hi, >>>>>> >>>>>> I have a RNA-Seq count data set that requires separate offset values >>>>>> for >>>>>> each tag and sample. DESeq does not appear to take a matrix of offset >>>>>> values >>>>>> (unlike edgeR) in any of its functions so I've carried out the >>>>>> analysis >>>>>> manually, ie. calculating a size factor for each tag of each sample, >>>>>> adjusting the counts, then proceeding to calculate means and variances >>>>>> of >>>>>> the adjusted counts, and finally fitting a curve for each condition to >>>>>> the >>>>>> mean-var plot using locfit(). >>>>>> >>>>>> Essentially, I'd like to put these variance functions (or at least all >>>>>> the predicted variances) and adjusted counts inside a DESeq object so >>>>>> that I >>>>>> can take advantage of the other functions DESeq offers, tests, plots, >>>>>> etc... >>>>>> >>>>>> Thanks for the help! >>>>>> >>>>>> Sean >>>>>> >>>>> > ______________________________**______________________________**__________ > The information in this email is confidential and inte...{{dropped:10}} _______________________________________________ Bioc-sig-sequencing mailing list Bioc-sig-sequencing@r-project.org https://stat.ethz.ch/mailman/listinfo/bioc-sig-sequencing
