search

NathanSkene / EWCE

Expression Weighted Celltype Enrichment. See the package website for up-to-date instructions on usage.
https://nathanskene.github.io/EWCE/index.html
53 stars 25 forks source link

"drop.uninformative.genes" uses functions for mainly microarray data? #14

Closed kleurless closed 2 years ago

kleurless commented 4 years ago

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?

NathanSkene commented 4 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.

On Wed, 15 Jul 2020, 14:16 kleurless, notifications@github.com wrote:

This email from notifications@github.com originates from outside Imperial. Do not click on links and attachments unless you recognise the sender. If you trust the sender, add them to your safe senders list https://spam.ic.ac.uk/SpamConsole/Senders.aspx to disable email stamping for this address.

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?

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub https://github.com/NathanSkene/EWCE/issues/14, or unsubscribe https://github.com/notifications/unsubscribe-auth/AH5ZPE6B5UR4TSFSSDXSSVTR3WTZ5ANCNFSM4O2Q3ZDA .

bschilder commented 2 years ago

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

bschilder commented 2 years ago

Removing these DGE options besides limma for now, until we can do some proper benchmarking to how they affect EWCE performance.