On Thu, Jun 5, 2014 at 10:04 AM, Gordon K Smyth <smyth at="" wehi.edu.au=""> wrote: > Dear Paul, > >> Date: Wed, 4 Jun 2014 17:30:59 +1000 >> From: Paul Harrison <paul.harrison at="" monash.edu=""> >> To: Bioconductor mailing list <bioconductor at="" r-project.org=""> >> Subject: [BioC] Behaviour of weights in limma >> >> Hello, >> >> I have some data from a variant of RNA-seq which I am hoping do some >> moderated t-test differential testing on with limma. In this data, many of >> the reads have sequenced through into the poly(A) tail, and we believe this >> gives us information about changes in poly(A) tail length. >> >> For each gene and sample, we can calculate an average observed tail >> length. It seems easy enough to calculate a standard error for this average >> as well. > > > I don't think that you can actually calculate a measingful standard error. > The total error depends on both biological and technical components. You > can predict how the measurement error depends on the number of reads, but > you don't know what proportion of the total error the measurement error > makes up. > Yes. Sorry, I meant that the technical error is fairly accurately known. >> In some cases we have few reads and the standard error is high, in others >> we have quite a lot of reads and the standard error is low. >> >> What I'm hoping is that this can be translated into weights that can be >> fed to limma to make it behave correctly. Do weights have some specific >> meaning in terms of measurement variance? > > > They have a specific meaning, but it is in terms of total variance not in > terms of measurement variance. > > The meaning of weights in limma is the same as for any linear modelling or > regression procedures, which is that the total variance is assumed inversely > proportional to the weight. > Ah. >> And how does this interact with moderation between genes, > > > Intimately. > >> for example could including highly noisy measurements from some genes >> detract from the significance of other genes where the measurement is more >> precise? > > > Yes. > That would also mean that even in conventional RNA-seq data it could be worthwhile to filter out low coverage genes before applying voom and limma? > Could you not simply use voom or edgeR, both of which already do what you > seem to be asking, which is to take the number of reads into account when > estimating variability and assessing DE? > To use voom I would need to alter the voom function to take the average tail lengths as a parameter in addition to counts. This looks fairly straightforward. Given what you've said above, an alternative would be, just for the purpose of calculating weights, to come up with a constant value for the biological variance, for example by Maximum Likelihood. Thank you, Paul Harrison Victorian Bioinformatics Consortium / Monash University