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As indicated in the tutorial, the calculations behind varianceDecomposition() can be quite intensive. Would it be possible to provide some native functionality to perform or to simplify the parallelisation of these calculations? So far I have been able to write a work-around using the snow package and low-level scLVM functions:
Parallelisation function:
my.var.decomp.call <- function(idx) {
# parallelisation function
library(scLVM)
configLimix2(limix_path) # my corrected version of this function - see other issue thread.
# have to recreate an entire sclvm object so things are properly configured in rPython instance
# i.e. python.assign("sclvm",sclvm) fails.
mysclvm <- new("scLVM")
mysclvm_py_name <- "mysclvm" # I did not manage to get the deparsing working in here
mysclvm <- init( mysclvm,Y=t(log10(nCountsMmus+1)) ,
tech_noise = as.vector(techNoise$techNoiseLog))
rm( nCountsMmus, techNoise )
CellCycle <- fitFactor(
mysclvm, geneSet = ens_ids_cc, k=1)
rm( ens_ids_cc )
# decompose variance & get results
varianceDecomposition_py(mysclvm_py_name, K=CellCycle$K, idx=idx)
res <- getVarianceComponents_py(mysclvm_py_name)
rm(mysclvm)
return( res$var[res$conv,] ) # only returning converged estimates
}
Dear scLVM team,
As indicated in the tutorial, the calculations behind varianceDecomposition() can be quite intensive. Would it be possible to provide some native functionality to perform or to simplify the parallelisation of these calculations? So far I have been able to write a work-around using the snow package and low-level scLVM functions:
Thanks & best regards,
-- Alex
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