add run_R.rb

README.md

@@ -86,6 +86,17 @@ G x D matrix, which corresponds to W of linear regression.
 #### RSS.txt
 The residual sum of squares of linear regression.
 
+## Running SCODE several times
+We recommend runnning SCODE several times and averaging the result (A) to obtain reliable relationships.
+```
+ruby run_R.rb <Input_file1> <Input_file2> <Output_dir> <G> <D> <C> <I> <R>
+```
+
+* R : The number of traials
+* Output_dir : Result files of each trial is outputted in the directory
+
+The averaged A (meanA.txt) is outputted in the Output_dir.
+
 <br>
 <br>
 # Downstream analysis

averageA.R

@@ -0,0 +1,12 @@
+args <- commandArgs(trailingOnly = T)
+dir <- args[1]
+repnum <- as.numeric(args[2])
+
+meanA <- as.matrix(read.table(paste(dir,"/out_1/A.txt",sep="")))
+for(i in 2:repnum){
+	tmp <- as.matrix(read.table(paste(dir,"/out_",i,"/A.txt",sep="")))
+	meanA <- meanA + tmp
+}
+meanA <- meanA / repnum
+
+write.table(meanA, paste(dir,"/meanA.txt",sep=""), sep="\t", col.names=F, row.names=F)

run_R.rb

@@ -0,0 +1,16 @@
+#!/usr/bin/ruby
+
+fdata = ARGV[0]
+ftime = ARGV[1]
+dir = ARGV[2]
+tfnum = ARGV[3]
+pnum = ARGV[4]
+cnum = ARGV[5]
+maxite = ARGV[6]
+repnum = ARGV[7].to_i
+
+system("mkdir #{dir}")
+for i in 1..repnum
+	system("Rscript SCODE.R #{fdata} #{ftime} #{dir}/out_#{i} #{tfnum} #{pnum} #{cnum} #{maxite}")
+end
+system("Rscript averageA.R #{dir} #{repnum}")