Question: BiocParallel and CopywriteR Error
1
2.2 years ago by
genomic832810
genomic832810 wrote:

I recently tried to use CopywriteR in Microsoft Azure cloud - Windows Server Datacenter Virtual Machine  (128 RAM and 16 cores) with R 3.3.2. Also my input data files: normal 12.67GB, tumor 11GB

## Can you suggest a work around? Maybe too many bam lines are being read at once?

Here is my code:

library("CopywriteR")
library("CopyhelpeR")
setwd("C:/Users/m/Desktop/share/data")
data.folder <- tools::file_path_as_absolute(file.path(getwd()))
preCopywriteR(output.folder=file.path(data.folder), bin.size=20000, ref.genome="hg38", prefix="chr")

list.dirs(path=file.path(data.folder), full.names=FALSE)
list.files(path=file.path(data.folder, "hg38_20kb_chr"), full.names=FALSE)
blacklist.grange

GC.mappa.grange[1001:1011]
bp.param <- SnowParam(workers = 15, type ="SOCK")
bp.param

path <- c("C:/Users/m/Desktop/share/data")
samples <- list.files(path=path, pattern="tumor.bam$", full.names=TRUE) controls <- list.files(path=path, pattern="normal.bam$", full.names=TRUE)
sample.control <- data.frame(samples,controls)

CopywriteR(sample.control = sample.control, destination.folder = file.path(data.folder), reference.folder = file.path(data.folder, "hg38_20kb_chr"), bp.param = bp.param)
modified 2.2 years ago by Martin Morgan ♦♦ 23k • written 2.2 years ago by genomic832810
0
2.2 years ago by
t.kuilman140
Netherlands
t.kuilman140 wrote:

I am not sure whether this is an issue with CopywriteR; I think this might be an issue with BiocParallel (the package in which the bplapply function is specified) and/or an memory issue. I hope someone else can help with this issue.

0
2.2 years ago by
Martin Morgan ♦♦ 23k
United States
Martin Morgan ♦♦ 23k wrote:

My guess is that the amount of data being returned by workers is too large to be represented in a serialized vector, I think probably 2^31 - 1 elements. Maybe traceback() would help understand where things are going wrong, and using SerialParam() a work-around (though obviously thwarting parallel evaluation).