Moderator: Ryan C. Thompson
Ryan C. Thompson ♦ 6.9k
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Posts by Ryan C. Thompson
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Comment:
C: DEseq2 - minimum number of genes
... Adding fake data isn't going to solve anything, it's just going to make the data worse. If you do so, you'll effectively be making an arbitrary determination of the size factors with no evidence while also disrupting the dispersion estimation.
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written 5 days ago by
Ryan C. Thompson ♦ 6.9k
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... Are you sure it makes sense to run DESeq2 on this data? Isn't this data better suited to fitting a logistic regression model for each mutation?
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written 13 days ago by
Ryan C. Thompson ♦ 6.9k
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... One thing that just occurred to me is that it might be possible to use voomWithQualityWeights to determine whether there is a data issue to begin with. Run it on the full data set with a design of ~ patient + time + batch, and then compare the weights of the shared samples against the non-shared one ...
written 18 days ago by
Ryan C. Thompson ♦ 6.9k
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... Perhaps using arrayWeights with a var.design that separates the samples into cross-batch controls and everything else?
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written 19 days ago by
Ryan C. Thompson ♦ 6.9k
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... If I understand correctly, your design is a bit different than the question you reference, because your samples shared across batches are effectively technical replicates, i.e. the same biological sample sequenced in each batch, as opposed to different biological replicates in the same treatment gro ...
written 20 days ago by
Ryan C. Thompson ♦ 6.9k
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... logCPM values are what they sound like: the logarithm (in base 2) of the counts for that gene divided by the total millions of counts. This normalizes for differences in sequencing depth between samples and nothing else. If you don't provide any further information, that's exactly what you get. If y ...
written 22 days ago by
Ryan C. Thompson ♦ 6.9k
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... The use of voom in the context of that question is extraneous. The function is calling voom and then throwing away the weights that it calculated, keeping only the logCPM values. If all you want is the logCPM values, then use the cpm function as I've described in my answer.
Looking at the other cod ...
written 22 days ago by
Ryan C. Thompson ♦ 6.9k
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... This is also documented in the help page. See the appropriately-named normalize.method argument. The default is "none", which performs no additional normalization after the logCPM transformation.
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written 22 days ago by
Ryan C. Thompson ♦ 6.9k
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... The voom function computes logCPM values using whatever normalization information you choose to provide. This is documented in the help page for voom. Generally, you should be running it on a DGEList object after running calcNormFactors on the DGEList. See the help page for calcNormFactors if you wa ...
written 22 days ago by
Ryan C. Thompson ♦ 6.9k
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... There are trade-offs to both approaches. If you fit a separate model for every gene, you lose the core feature of limma: empirical Bayes information sharing when estimating the variance for each gene. The other main difference is that limma's mixed model functionality estimates a single correlation ...
written 27 days ago by
Ryan C. Thompson ♦ 6.9k
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For A: Voom transformation from counts and normalization to negative values
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For A: RNA-seq Normalisation - normalise all samples in experiment or only the ones use
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For A: What's the relationship between "design" and "contrasts"?
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For A: Limma validity for only hundreds of genes/metabolites
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