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library(ctl)
library(rjson)
options(stringsAsFactors = FALSE);
# The genotypes.csv file containing the genotype matrix is stored individuals (rows) x genetic marker (columns):
args = commandArgs(trailingOnly=TRUE)
if (length(args)==0) {
stop("Argument for the data file", call.=FALSE)
} else {
# default output file
json_file_path = args[1]
}
# add validation for the files
input <- fromJSON(file = json_file_path)
genotypes <- read.csv(input$geno_file,row.names=1, header=FALSE, sep="\t")
# The phenotypes.csv file containing individuals (rows) x traits (columns) measurements:
traits <- read.csv(input$pheno_file,row.names=1, header=FALSE, sep="\t")
ctls <- CTLscan(geno,traits,strategy=input$strategy,
nperm=input$nperms,parametric =input$parametric,
nthreads=6,verbose=TRUE)
# same function used in a different script:refactor
genImageRandStr <- function(prefix){
randStr <- paste(prefix,stri_rand_strings(1, 9, pattern = "[A-Za-z0-9]"),sep="_")
return(paste(randStr,".png",sep=""))
}
#output matrix significant CTL interactions with 4 columns: trait, marker, trait, lod
sign <- CTLsignificant(ctls,significance = input$significance)
# Create the lineplot
imageLoc = file.path(imgDir,genImageRandStr("CTLline"))
png(imageLoc,width=1000,height=600,type='cairo-png')
lineplot(res, significance=input$significance)
json_data <- list(significance=signs,
images=lists("image_1"=imageLoc),
network_figure_location="/location")
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