Closed kleurless closed 2 years ago
Hi, this function could definitely be done better. Using limma was just a fast way of dropping rubbish genes. I'd always meant to use permutation testing to replace it. If you come up with anything better I'd be happy to pull it in and acknowledge your contribution.
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Hi, I was looking into the source code for the drop.uninformative.genes function and I saw that it uses an ANOVA test implemented in limma. And limma, together with the eBayes() function, was developed for (mainly) microarray data and also RNAseq data; but it makes me wonder if it is applicable to scRNAseq data?
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I've added some additional functions for running DGE, some of which may be more suitable for scRNAseq. The following are now available on my DelayedArray branch (will be merging these with the main branch soon):
Also added some pre-filtering steps to remove genes that don't vary at all (by computing variance), so you don't waste compute on those during the DGE steps. https://github.com/bschilder/EWCE/blob/DelayedArray/R/filter_variance_quantiles.r
Removing these DGE options besides limma for now, until we can do some proper benchmarking to how they affect EWCE performance.
Hi, I was looking into the source code for the drop.uninformative.genes function and I saw that it uses an ANOVA test implemented in limma. And limma, together with the eBayes() function, was developed for (mainly) microarray data and also RNAseq data; but it makes me wonder if it is applicable to scRNAseq data?