I need to implement a parallel version of an R script because on one core it takes forever to execute. The function that takes the most of time is
scran::quickCluster() applied to an integer sparse matrix (
UMI_count) that is
26000x66000. The code is presented below:
library(scran) library(scater) sce <- newSCESet(countData=UMI_count) clusters <- quickCluster(sce) sce <- computeSumFactors(sce, cluster=clusters)
I did not find much on parallel versions of
quickCluster() function, so will it actually execute in parallel if I set the number of cores to more than one? I am trying to do normalization of heterogeneous dataset with multiple cell types, that is why I am using
And I just need to speed it up to be able to compute it.