LIMMA No residual using rmaPLM
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Ron Ophir ▴ 270
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Ben Bolstad ★ 1.1k
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I'd recommend that you use fitPLM rather than rmaPLM for these purposes. The reason why it is giving "reasonable results" in the first case and an error in the other case, is that it is using your user supplied weights in the earlier situation and trying to derive some sensible weights based on the PLM model fit. Unfortunately rmaPLM does not return the information that is used to construct these but fitPLM does. Ben On Mon, 2005-09-05 at 13:51 +0300, Ron Ophir wrote: > Hi > I am analyzing affymetrix data having the following experimental > design: > > analysis$design > Normal IOP IOPC > L91RAE230 2.CEL 0 1 0 > L92RAE230 2.CEL 0 1 0 > L93RAE230 2.CEL 0 0 1 > L94RAE230 2.CEL 0 0 1 > L95RAE230 2.CEL 1 0 0 > L96RAE230 2.CEL 1 0 0 > and following contrasts > > analysis$contrasts > IOPC.IOP IOPC.Normal IOP.Normal > Normal 0 -1 -1 > IOP -1 0 1 > IOPC 1 1 0 > The preprocessing was done using affyPLM packge > analysis$data<-rmaPLM(analysis$raw) > where analysis$raw is AfyyBatch object. Then I fitted the the model > once with weighting matrix using AMP flags > fm<-as.matrix(analysis$Flags!="A") > analysis$fit<-lmFit(analysis$data,analysis$design,w=matrix(as.numeri c(fm),dim(fm)[1],dim(fm)[2],dimnames=dimnames(fm))) > and got a reasonable results and once without this matrix. What was > surprising is that fitting without the weighting matrix I got the > following error message: > > analysis$bayes<-eBayes(analysis$contrasts.fit) > Error in ebayes(fit = fit, proportion = proportion, stdev.coef.lim = > stdev.coef.lim) : > No residual degrees of freedom in linear model fits > I've checked the fit coeffecient matrix (analyis$fit object) and is all > NA. > Does someone have an idea why? I did not applied any weighting matrix > for this fitting. > Thanks > Ron > > > > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor
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Ben Bolstad ★ 1.1k
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> > Hello > > I have a general question regarding pre-processing data using RMA. > > Due to memory constraints, I am using the function justRMA() instead of > fitPLM() for pre-processing my data (I have a couple of hundred samples so > far). Is there a way to obtain standard errors of expression intensity > estimates using this function? In this case, se.exprs returns a matrix of > NA's. Unfortunately neither justRMA() nor rma() return SE values. > Also, I understand that these functions do not readily provide p-values > associated with expression intensities (similar to the ones from MAS5 for > example). I am wondering what would be a good way to check data quality in > this case. Do you have any suggestions? The P-values that MAS 5 produces are in relation to their presence absence calls (not the expression value itself). There is no similar analog for RMA in this sense. Generally speaking the MAS5 presence/absence calls are pretty decent (although the MAS5 expression values have their faults) and you could use these with RMA expression values if you wished without much problem. That said, the reason people typically use these is to screen out "noisy" genes at the low absolute intensity level. This tends to not be a problem with RMA expression values. You could do preliminary quality examination of things like chip pseudo images using recent versions of RMAExpress found at: http://www.stat.berkeley.edu/~bolstad/RMAExpress/RMAExpress.html The latest alpha versions support extremely large datasets on memory limited systems. Unfortunately things like RLE and NUSE (which are in affyPLM) are not currently implemented in RMAExpress.
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Ron Ophir ▴ 270
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Last seen 10.2 years ago
> > >>>> Gordon Smyth <smyth at="" wehi.edu.au=""> 09/07/05 5:06 AM >>> > >>Date: Mon, 05 Sep 2005 13:51:38 +0300 >>From: "Ron Ophir" <ron.ophir at="" weizmann.ac.il=""> >>Subject: [BioC] LIMMA No residual using rmaPLM >>To: <bioconductor at="" stat.math.ethz.ch=""> >>Message-ID: <s31c4d86.078 at="" wisemail.weizmann.ac.il=""> >>Content-Type: text/plain >> >>Hi >>I am analyzing affymetrix data having the following experimental >>design: >> > analysis$design >> Normal IOP IOPC >>L91RAE230 2.CEL 0 1 0 >>L92RAE230 2.CEL 0 1 0 >>L93RAE230 2.CEL 0 0 1 >>L94RAE230 2.CEL 0 0 1 >>L95RAE230 2.CEL 1 0 0 >>L96RAE230 2.CEL 1 0 0 >> and following contrasts >> > analysis$contrasts >> IOPC.IOP IOPC.Normal IOP.Normal >>Normal 0 -1 -1 >>IOP -1 0 1 >>IOPC 1 1 0 >>The preprocessing was done using affyPLM packge >>analysis$data<-rmaPLM(analysis$raw) >>where analysis$raw is AfyyBatch object. Then I fitted the model >>once with weighting matrix using AMP flags >>fm<-as.matrix(analysis$Flags!="A") >>analysis$fit<-lmFit(analysis$data,analysis$design,w=matrix(as.numeri c(fm),dim(fm)[1],dim(fm)[2],dimnames=dimnames(fm))) >> and got a reasonable results and once without this matrix. What was >>surprising is that fitting without the weighting matrix I got the >>following error message: >> > analysis$bayes<-eBayes(analysis$contrasts.fit) >>Error in ebayes(fit = fit, proportion = proportion, stdev.coef.lim = >>stdev.coef.lim) : >> No residual degrees of freedom in linear model fits >>I've checked the fit coeffecient matrix (analyis$fit object) and is all >>NA. >>Does someone have an idea why? I did not applied any weighting matrix >>for this fitting. > >As Ben has already explained, you are implicitly using a weighting matrix >here because your 'PLMset' object contains a slot 'se.chip.coefs' from >which limma is attempting to construct probe-set weights. (Basically the >weight for each expression value is the inverse squared se, with some >moderation to prevent absurd values.) You can easily turn this off by using > > lmFit(..., weights=NULL) > >The philosophy is this: If the se.chip.coefs slot is empty, then limma will >take the se's and hence the weights to be all equal. If however this slot >is set to be a matrix with entirely NA values, then limma will assume (and >this is the crucial thing) that the se's are not only missing but >non-ignorable. Hence it passes NA weights to the lmFit and hence you end up >with no useful data. > >I am open to suggestion that limma should do something different. My >feeling which motivates the current behaviour is that slots in data objects >are only set (or should only be set) when they really mean something. Hence >they should not be ignored without specific direction to do so, even if >they contain NA values. > >Gordon Thanks Gordon and Ben Conceptually, you right Gordon that is the idea of OOP. The responsible for the data is on the object encapsulates them and if there are some NAs they may have meaningful. However if sumapplyis.na(PLMset at se.chip.coefs),1,sum)) equals to dim(PLMset at se.chip.coefs)[1]*dim(PLMset at se.chip.coefs)[2] it might indicate a logical bug and hence no use for such data. I'll be aware of this next time. Ron > >>Thanks >>Ron >
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