Entering edit mode
Dear all,
considering the tutorials presented by Aaron Lun, Davis McCarthy and John C. Marioni, on scRNA-seq :
https://bioconductor.org/packages/release/workflows/vignettes/simpleSingleCell/inst/doc/reads.html
how shall I compute the differential expression between 2 cell clusters in 2 distinct conditions ; for example, in the above tutorial,
to compute the differential expression between :
-- cluster 1 cells in "control" versus "oncogene induced"
-- cluster 1 cells in "control" versus cluster 2 (or any other cluster) in "oncogene induced"
thanks a lot !
-- bogdan
HI Aaron, thanks a lot for your reply and help ..
It would be great and very helpful indeed, if I could have an example of the R code from you, in order to accomplish the task of computing the differential expression between :
-- cluster 1 cells in "control" versus
-- cluster 1 cells in "oncogene induced"
I did note that in colData(sce) we do have the cluster numbers. And in order to select the cells that are associated with cluster1 (in "control" and "induced"), i would have 1) to select specifically those cells and 2) to filter them ..
Which function shall i use for selecting the cells associated with cluster 1 ? The documentation of scran includes a section on "Gene selection" (page 45), but not on "Cell selection". thank you very much ;) !
Well, you literally just
pastethem together. Likepaste(sce$cluster, sce$Oncogene). And then use the resulting vector as your grouping factor in a DE analysis, or ascluster=infindMarkers(). Then you can compare between any pair of cluster:condition combinations that you are interested in (noting my comments above about interpretation).There's no need for selecting and filtering, I don't understand why you think that's necessary. But for what it's worth, the way to select cells associated with cluster 1 (or whatever you want to select for) would be the usual way of subsetting a
SummarizedExperimentobject.Thank you Aaron. It is very helpful. Have a good weekend !
If I may, I will verify with you a bit later the entire pipeline that we have at this moment.
Thank you Aaron also for the tutorial on differential expression using pseudo-bulk factors : very useful !
https://bioconductor.org/packages/release/workflows/vignettes/simpleSingleCell/inst/doc/de.html
HI Aaron, thanks a lot for your reply and help ..
It would be great and very helpful indeed, if I could have an example of the R code from you, in order to accomplish the task of computing the differential expression between :
-- cluster 1 cells in "control" versus
-- cluster 1 cells in "oncogene induced"
I did note that in colData(sce) we do have the cluster numbers. And in order to select the cells that are associated with cluster1 (in "control" and "induced"), i would have 1) to select specifically those cells and 2) to filter them ..
Which function shall i use for selecting the cells associated with cluster 1 ? The documentation of scran includes a section on "Gene selection" (page 45), but not on "Cell selection". thank you very much ;) !
woops .. sorry for double posting. It was unintentional.
Dear Aaron, I would have a few more questions about SCRAN and the use of MNNCORRECT() and will open another question track here on the BioC website.