feat(dataProcess): Add parallel processing functionality to dataProcess #116
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Motivation and Context
In theory, MSstats should produce more meaningful results if clients set up experiments with more bioreplicates. However, as clients begin to input larger datasets into MSstats, they have been running into issues regarding speed & memory. This pull request aims to speed up the MSstats dataProcess function by adding a parameter called
numberOfCores
that should allow users to perform data processing (specifically the summarization step) in parallel per protein.This is an extension of the parallel processing PR for groupComparison
Changes
numberOfCores
parameter to dataProcess function. Default is 1 core, which goes through the regularfor
loop approach with a progress bar indicator.MSstatsSummarizeSingleTmp
orMSstatsSummarizeSingleLinear
in parallel per protein.Testing
input_split = split(input, input$PROTEIN)
creates a variable that is listed as 35GB in R despite this dataset being 2.3GB. If this variable is created prior to exporting cluster variables, the parallel processing stalls since theinput_split
object takes up a huge amount of space in memory. As a result, I've refactored the multi-core approach to not create this object and instead represent the split using a list of indices instead (seeprotein_indices
variable).Checklist Before Requesting a Review