Open xflicsu opened 10 months ago
Hi thank you for your interest in scMerge2.
I am wondering how many batches and conditions do you have for your dataset? (assuming condition of the sample is included as sce$type
here). If you want to run scMerge2 in parallel, you can set use_bpparam = BiocParallel::MulticoreParam(workers = ncores)
.
Best wishes, Yingxin
Hi thank you for your interest in scMerge2.
I am wondering how many batches and conditions do you have for your dataset? (assuming condition of the sample is included as
sce$type
here). If you want to run scMerge2 in parallel, you can setuse_bpparam = BiocParallel::MulticoreParam(workers = ncores)
.Best wishes, Yingxin
Thanks for your quick response!
I have 150k cells with 40 samples (sce$orig.ident) and 3 conditions (sce$type). The process work in parallel.
Did you replace use_bpparam = SerialParam()
by use_bpparam = MulticoreParam(workers = ncores)
in the end?
I use scMerge2 to integrate about 150K cells. Now, it costed about 5 hours and still run the "Running RUV" step with 120 CPU. I wonder how to accelarate the process like your paper mentioned? Thanks!
################## scMerge2_res <- scMerge2(exprsMat = logcounts(sce), batch = sce$orig.ident,condition=sce$type,chosen.hvg=hgvs,return_matrix = FALSE, verbose = TRUE,use_bpparam = BiocParallel::SerialParam() ) [1] "Cluster within batch" [1] "Normalising data" [1] "Constructing pseudo-bulk" Dimension of pseudo-bulk expression: [1] 33341 16083 [1] "Identifying MNC using pseudo-bulk:" [1] "condition_mode" [1] "Running RUV"