utnesp/parallell_functions

Repository containing functions to do parallell computations

source_https <- function(url, ...) {
  # load package
  require(RCurl)
 
  # parse and evaluate each .R script
  sapply(c(url, ...), function(u) {
    eval(parse(text = getURL(u, followlocation = TRUE, cainfo = system.file("CurlSSL", "cacert.pem", package = "RCurl"))), envir = .GlobalEnv)
  })
}

# You will need these packages
library(survival)
library(data.table)
library(ggplot2) # only if plotting data afterwards
library(survminer) # only if plotting data afterwards
library(foreach)
library(doParallel)

source_https("https://raw.githubusercontent.com/utnesp/parallell_functions/master/R/coxph.parallell")

coxph.parallell

The coxph.parallell function is as follows:

coxph.parallell <- function(df, time.event.data, var.list = NULL, file = "test.txt", no_cores = "")

input df is a data.table with gene in rows and samples in col. first col has to contain name of gene. var.list is extracted from first column

> head(df)
var     s1  s2  s3
gene_1  1   0   1
gene_2  1   0   1
gene_3  1   1   1

The df can also contain continous values

time.event.data is data.frame / data.table with cols binary data (event) in col1 and time in col2. The row order has to match col order in df

> head(time.event.data)
    event   time
s1  1       1302
s2  0       1200
s3  1       1400

The var.list is set to NULL by default, meaning that the first column in df will be used as variables. The function writes to a number of temporary files, and when done, concatenates the files to the file specified in file argument. The function will use all.cores - 1 if not specified.

Plot data using the plot.surv function

plot.surv(genename, time.event.data, df)

If you also know which sample are e.g. highrisk, then you can subset your data in the above two functions

> head(clinicalData)
    highrisk
s1  1       
s2  0       
s3  1       

coxph.parallell(df, time.event.data, sub.idx = which(SEQC.clinicalData$highrisk == 1), file = "res.txt")
res <- fread("res.txt")

plot.surv(genename, time.event.data, df, sub.idx = which(SEQC.clinicalData$highrisk == 1))