User: maltethodberg

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Posts by maltethodberg

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sva: Parallel execution of sva()-function fails.
... I have been trying to calculate SVs using the sva-package in parallel, but no matter what backend I use, it always fails. Consider for examples this example code based on the vignette: library(parallel) library(BiocParallel) library(sva) library(bladderbatch) data(bladderdata) # Data from the vig ...
parallel sva written 16 days ago by maltethodberg20 • updated 16 days ago by Martin Morgan ♦♦ 19k
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Replacing values in an RleList
... Given an RleList (i.e. from either calling coverage() or import.bw()) what is the best way to replace certain values? I am interested in something this: x #RleList x[x < 0] <- 0 I'm doing this many times, and compared to how quick most other operations on RleLists are, this step becomes th ...
rlelist ifelse written 7 weeks ago by maltethodberg20 • updated 6 weeks ago by theobroma2210
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Comment: C: DE for genes with very low counts using limma.
... Played around a bit with the different thresholds using limma-trend, to see how it it affects the prior fit for lowly expressed genes. Made the following observations: 1) Filtering out genes with a substantial amount of zero counts across samples, removes most of the prior dip at lowly expressed gen ...
written 4 months ago by maltethodberg20
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Comment: C: DE for genes with very low counts using limma.
... That was more or less exactly what I was doing, ranking genes with aveLogCPM and then filtering out the most lowly expressed genes until the prior curve from plotSA looked decent. I will try playing around with different filtering criteria. With limma-trend my reasoning was that you could potentiall ...
written 4 months ago by maltethodberg20
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Comment: C: DE for genes with very low counts using limma.
... What specific approach of edgeR would you recommend using in this case, the standard or QL pipeline? ...
written 4 months ago by maltethodberg20
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Comment: C: DE for genes with very low counts using limma.
... Each treatment group is small (duplicates or triplicates). Several treatment groups are in larger blocks to control for various batch effects. ...
written 4 months ago by maltethodberg20
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DE for genes with very low counts using limma.
... I am analyzing a very large RNA-Seq experiment (100s of samples). I intend to use limma for the analysis, mainly due to the speed of limma compared to edgeR or DESeq2. I specifically want to keep a large number of very lowly expressed genes in the analysis. When I apply the voom, the fitted mean-va ...
voom limma limma-trend written 4 months ago by maltethodberg20 • updated 4 months ago by Gordon Smyth30k
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Comment: C: Design matrix and contrast for RNA knockdown experiment
... In that case, what would an appropriate FDR correction procedure be? In my understanding, most methods assume a  uniform distribution of p-values towards 1.0, which is not the case here. ...
written 4 months ago by maltethodberg20
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Comment: C: Design matrix and contrast for RNA knockdown experiment
... Just implemented the intersection union test. In some cases this gives rise to a p-value distribution heavily skewed towards 1.0 (similar to the scenario D here: http://varianceexplained.org/statistics/interpreting-pvalue-histogram/). Any idea of why this happens? ...
written 4 months ago by maltethodberg20
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Comment: C: Design matrix and contrast for RNA knockdown experiment
... Thank you for your detailed reply. I like your suggestion with the intersection union test, since that still produces a p-value for every gene. To test whether the mean effect across all oligos for a single gene is significant, would the following contrast for the Gene1 be correct?: c(0, 1/3, 1/3, 1 ...
written 4 months ago by maltethodberg20

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Supporter 10 months ago, voted at least 25 times.

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