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sequencing depth normalization #422
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Hi, Please refer to issue #268. Best, |
Hi, |
Hi, Is the data a sparse matrix? If yes, you might want to use the updated function that you can find in the develop branch of Seurat's GitHub page. If you have the read number stored in your # Calculate number of UMI per detected gene
object@meta.data$nUMI.per.gene <- object@meta.data$nUMI / object@meta.data$nGene
# Regress out technical effects using regularized Negative Binomial regression
object <- RegressOutNBreg(object = object,
latent.vars = c("nRead", "nUMI.per.gene"),
genes.regress = object@var.genes, # regress only for variable genes: speeds computing time!
pr.clip.range = c(-30, 30),
min.theta = 0.01) Best, |
Thanks Leon. We think this is a powerful method for preprocessing but is still under development (and quite slow), so we haven't integrated it into our public workflows. Hopefully Leon's answer solves the technical question you had. |
To anyone using RegressOutNBreg: prior to commit 86e4c83 in the develop branch (April 30, 2018 at 12:01:59 PM GMT+2) gene.regress would also be applied to the theta estimation step. As a result it was not advisable to set genes.regress to just variable genes as that would attenuate regularization. This has been fixed and the genes used during the first step (theta estimation) are now separate from the genes used during the second step (regression). |
Hi Christoph, I can't find the commit you mentioned. Best, |
Sorry, I did not merge the changes into the public repo. You should see the changes now. |
Hi Christoph, Could you please also export the function from Seurat in the develop branch? Thank you in advance! Best, |
Hi there, I noticed in the version 2.0.1, FindMarkers was automatically run with latent.vars="nUMI", but this has been removed in the current version (now latent.vars = NULL). Thus when I now use NegBinDETest, the nUMI are not considered as latent variables any more by default. But this used to be the case (e.g. in version 2.0.1) - and made sense to me - so my question: why was it changed? Thank you! |
AverageExpression - increase speed and grouping capacity
Hello,
I was reading your paper about cortical inhibitory interneurons https://www.nature.com/articles/nature25999 and I was wondering how you performed your normalization on sequencing depth of each cell using the theta overdispersion parameter.
Will this feature be integrated in Seurat ? I would be interested to test it on our data but I'm not sure how to get the theta parameter.
Thanks a lot !
Best regards
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