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I am working on epigenomic data to find outliers. I have over ~350K features. I requested for 15 cores but I keep getting the following error. Can I just use PEER covariate subtracted matrix? At what step I can implement that? or you have other suggestions to improve the implementation?
| | 0%Error in mcfork(detached) :
unable to fork, possible reason: Cannot allocate memory
In addition: Warning message:
In parallel::mccollect(wait = TRUE) :
18 parallel jobs did not deliver results
The text was updated successfully, but these errors were encountered:
gk1610
changed the title
Predefined covariates
memory error with 300K features
May 19, 2022
gk1610
changed the title
memory error with 300K features
Predefined covariates
May 19, 2022
I am working on epigenomic data to find outliers. I have over ~350K features. I requested for 15 cores but I keep getting the following error. Can I just use PEER covariate subtracted matrix? At what step I can implement that? or you have other suggestions to improve the implementation?
ncores <- 15
register(MulticoreParam(ncores, ncores*2, progressbar = TRUE))
ods <- OutriderDataSet(countData=rawReads_cpm)
ods <- estimateSizeFactors(ods)
ods <- findEncodingDim(ods, BPPARAM=bpparam(), params = seq(2,50,by=2)
| | 0%Error in mcfork(detached) :
unable to fork, possible reason: Cannot allocate memory
In addition: Warning message:
In parallel::mccollect(wait = TRUE) :
18 parallel jobs did not deliver results
The text was updated successfully, but these errors were encountered: