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library(ctl)
library(stringi);
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)
imgDir = Sys.getenv("GENERATED_IMAGE_DIR")
if (length(args)==0) {
stop("Argument for the data file", call.=FALSE)
} else {
# default output file
json_file_path = args[1]
}
json_file_path
# add validation for the files
input <- fromJSON(file = json_file_path)
genoData <- input$geno
phenoData <- input$pheno
formData <- input$form
# create the matixes
genoData
geno_matrix = t(matrix(unlist(genoData$genotypes),
nrow=length(genoData$markernames), ncol=length(genoData$individuals),
dimnames=list(genoData$markernames, genoData$individuals), byrow=TRUE))
pheno_matrix = t(matrix(as.numeric(unlist(phenoData$traits)), nrow=length(phenoData$trait_db_list), ncol=length(
phenoData$individuals), dimnames= list(phenoData$trait_db_list, phenoData$individuals), byrow=TRUE))
# Use a data frame to store the objects
pheno = data.frame(pheno_matrix, check.names=FALSE)
geno = data.frame(geno_matrix, check.names=FALSE)
ctls <- CTLscan(geno,pheno,verbose=TRUE)
# same function used in a different script:refactor
genImageRandStr <- function(prefix){
randStr <- paste(prefix,stri_rand_strings(1, 9, pattern = "[A-Za-z0-9]"),sep="_")
return(paste(randStr,".png",sep=""))
}
#output matrix significant CTL interactions with 4 columns: trait, marker, trait, lod
sign <- CTLsignificant(ctls,significance = formData$significance)
# Create the lineplot
imageLoc = file.path(imgDir,genImageRandStr("CTLline"))
png(imageLoc,width=1000,height=600,type='cairo-png')
ctl.lineplot(ctls, significance=formData$significance)
json_data <- list(significance=sign,
images=list("image_1"=imageLoc),
network_figure_location="/location")
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