In DeveauP/QuantumClone: Clustering Mutations using High Throughput Sequencing (HTS) Data

knitr::opts_chunk$set(echo = TRUE)
if(!require(QuantumClone)){
  if(!require(devtools)){
    install.packages("devtools")
  }
  devtools::install_github(repo = "DeveauP/QuantumClone")
}
if(!require(knitr)) install.packages("knitr");library("knitr")
if(!require(ggplot2)) install.packages("ggplot2");library("ggplot2")
if(!require(microbenchmark)) install.packages("microbenchmark");library("microbenchmark")
library("parallel")

Parallelization handling

Only the search for expectation maximization is parallelized in QuantumClone - and used only if the number of cores set is strictly higher than 1. It parallelizes in priority the EM algorithm for different number of clusters then the iterations for a given number of clusters.

The parallelization is handled through parallel and foreach packages.

Gain of performance

First we create a data set to analyze:

set.seed(123)
Start.data<-QuantumCat(4,100,"AB")

Then we detect the number of cores on the computer, and create a reproducible analysis of the data with a different number of cores:

max.cores<-min(parallel::detectCores(),4)
print(max.cores)

analysis<-function(ncores = 1,Start.data){
  set.seed(123)
  One_step_clustering(SNV_list = Start.data,ncores = ncores,contamination = c(0,0), 
                      save_plot = FALSE, Initializations = 2,preclustering = "FLASH",nclone_range = 2:10
)
}

mb<-microbenchmark(analysis(1,Start.data),analysis(2,Start.data),analysis(max.cores,Start.data),times = 5)

print(mb)

autoplot(mb)+ylim(c(0,10))

DeveauP/QuantumClone documentation built on Oct. 29, 2021, 8:56 a.m.