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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!
I am wondering how many batches and conditions do you have for your dataset? (assuming condition of the sample is included as sce$type here). If you want to run scMerge2 in parallel, you can set use_bpparam = BiocParallel::MulticoreParam(workers = ncores).
I am wondering how many batches and conditions do you have for your dataset? (assuming condition of the sample is included as sce$type here). If you want to run scMerge2 in parallel, you can set use_bpparam = BiocParallel::MulticoreParam(workers = ncores).
Best wishes, Yingxin
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.
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"
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