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-rw-r--r--scripts/rqtl_wrapper.R229
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diff --git a/scripts/rqtl_wrapper.R b/scripts/rqtl_wrapper.R
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+library(optparse)
+library(qtl)
+library(stringi)
+library(stringr)
+
+tmp_dir = Sys.getenv("TMPDIR")
+
+option_list = list(
+ make_option(c("-g", "--geno"), type="character", help=".geno file containing a dataset's genotypes"),
+ make_option(c("-p", "--pheno"), type="character", help="File containing two columns - sample names and values"),
+ make_option(c("-c", "--addcovar"), action="store_true", default=NULL, help="Use covariates (included as extra columns in the phenotype input file)"),
+ make_option(c("--model"), type="character", default="normal", help="Mapping Model - Normal or Non-Parametric"),
+ make_option(c("--method"), type="character", default="hk", help="Mapping Method - hk (Haley Knott), ehk (Extended Haley Knott), mr (Marker Regression), em (Expectation-Maximization), imp (Imputation)"),
+ make_option(c("-i", "--interval"), action="store_true", default=NULL, help="Use interval mapping"),
+ make_option(c("--nperm"), type="integer", default=0, help="Number of permutations"),
+ make_option(c("--pstrata"), action="store_true", default=NULL, help="Use permutation strata (stored as final column/vector in phenotype input file)"),
+ make_option(c("-s", "--scale"), type="character", default="mb", help="Mapping scale - Megabases (Mb) or Centimorgans (cM)"),
+ make_option(c("--control"), type="character", default=NULL, help="Name of marker (contained in genotype file) to be used as a control"),
+ make_option(c("-o", "--outdir"), type="character", default=file.path(tmp_dir, "output"), help="Directory in which to write result file"),
+ make_option(c("-f", "--filename"), type="character", default=NULL, help="Name to use for result file"),
+ make_option(c("-v", "--verbose"), action="store_true", default=NULL, help="Show extra information")
+);
+
+opt_parser = OptionParser(option_list=option_list);
+opt = parse_args(opt_parser);
+
+verbose_print <- function(...){
+ if (!is.null(opt$verbose)) {
+ for(item in list(...)){
+ cat(item)
+ }
+ cat("\n")
+ }
+}
+
+if (is.null(opt$geno) || is.null(opt$pheno)){
+ print_help(opt_parser)
+ stop("Both a genotype and phenotype file must be provided.", call.=FALSE)
+}
+
+geno_file = opt$geno
+pheno_file = opt$pheno
+
+# Generate randomized filename for cross object
+cross_file = file.path(tmp_dir, "cross", paste(stri_rand_strings(1, 8), ".cross", sep = ""))
+
+trim <- function( x ) { gsub("(^[[:space:]]+|[[:space:]]+$)", "", x) }
+
+get_geno_code <- function(header, name = 'unk'){
+ mat = which(unlist(lapply(header,function(x){ length(grep(paste('@',name,sep=''), x)) })) == 1)
+ return(trim(strsplit(header[mat],':')[[1]][2]))
+}
+
+geno_to_csvr <- function(genotypes, trait_names, trait_vals, out, sex = NULL,
+ mapping_scale = "Mb", verbose = FALSE){
+ # Assume a geno header is not longer than 40 lines
+ header = readLines(genotypes, 40)
+
+ # Major hack to skip the geno headers
+ toskip = which(unlist(lapply(header, function(x){ length(grep("Chr\t", x)) })) == 1) - 1
+
+ type <- get_geno_code(header, 'type')
+
+ # Get the genotype codes
+ if(type == '4-way'){
+ genocodes <- NULL
+ } else {
+ genocodes <- c(get_geno_code(header, 'mat'), get_geno_code(header, 'het'),
+ get_geno_code(header, 'pat'))
+ }
+ genodata <- read.csv(genotypes, sep='\t', skip=toskip, header=TRUE,
+ na.strings=get_geno_code(header,'unk'),
+ colClasses='character', comment.char = '#')
+
+ verbose_print('Genodata:', toskip, " ", dim(genodata), genocodes, '\n')
+
+ # If there isn't a sex phenotype, treat all as males
+ if(is.null(sex)) sex <- rep('m', (ncol(genodata)-4))
+
+ cross_items = list()
+
+ # Add trait and covar phenotypes
+ for (i in 1:length(trait_names)){
+ cross_items[[i]] <- c(trait_names[i], '', '', unlist(trait_vals[[i]]))
+ }
+
+ # Sex phenotype for the mice
+ cross_items[[length(trait_names) + 1]] <- c('sex', '', '', sex)
+ # Genotypes
+ cross_items[[length(trait_names) + 2]] <- cbind(genodata[,c('Locus','Chr', mapping_scale)],
+ genodata[, 5:ncol(genodata)])
+
+ out_csvr <- do.call(rbind, cross_items)
+
+ # Save it to a file
+ write.table(out_csvr, file=out, row.names=FALSE, col.names=FALSE, quote=FALSE, sep=',')
+
+ # Load the created cross file using R/qtl read.cross
+ if (type == '4-way') {
+ verbose_print('Loading in as 4-WAY\n')
+ cross = read.cross(file=out, 'csvr', genotypes=NULL, crosstype="4way")
+ } else if(type == 'f2') {
+ verbose_print('Loading in as F2\n')
+ cross = read.cross(file=out, 'csvr', genotypes=genocodes, crosstype="f2")
+ } else {
+ verbose_print('Loading in as normal\n')
+ cross = read.cross(file=out, 'csvr', genotypes=genocodes)
+ }
+ if (type == 'riset') {
+ # If its a RIL, convert to a RIL in R/qtl
+ verbose_print('Converting to RISELF\n')
+ cross <- convert2riself(cross)
+ }
+
+ return(cross)
+}
+
+create_marker_covars <- function(the_cross, control_marker){
+ #' Given a string of one or more marker names (comma separated), fetch
+ #' the markers' values from the genotypes and return them as vectors/a vector
+ #' of values
+
+ # In case spaces are added in the string of marker names
+ covariate_names <- strsplit(str_replace(control_marker, " ", ""), ",")
+
+ genotypes <- pull.geno(the_cross)
+ covariates_in_geno <- which(covariate_names %in% colnames(genotypes))
+ covariate_names <- covariate_names[covariates_in_geno]
+ marker_covars <- genotypes[, unlist(covariate_names)]
+
+ return(marker_covars)
+}
+
+# Get phenotype vector from input file
+df <- read.table(pheno_file, na.strings = "x", header=TRUE, check.names=FALSE)
+sample_names <- df$Sample
+trait_names <- colnames(df)[2:length(colnames(df))]
+
+# Since there will always only be one non-covar phenotype, its name will be in the first column
+pheno_name = unlist(trait_names)[1]
+
+trait_vals <- vector(mode = "list", length = length(trait_names))
+for (i in 1:length(trait_names)) {
+ this_trait <- trait_names[i]
+ this_vals <- df[this_trait]
+ trait_vals[[i]] <- this_vals
+
+ trait_names[i] = paste("T_", this_trait, sep = "")
+}
+
+verbose_print('Generating cross object\n')
+cross_object = geno_to_csvr(geno_file, trait_names, trait_vals, cross_file)
+
+# Calculate genotype probabilities
+if (!is.null(opt$interval)) {
+ verbose_print('Calculating genotype probabilities with interval mapping\n')
+ cross_object <- calc.genoprob(cross_object, step=5, stepwidth="max")
+} else {
+ verbose_print('Calculating genotype probabilities\n')
+ cross_object <- calc.genoprob(cross_object)
+}
+
+# Pull covariates out of cross object, if they exist
+covars = vector(mode = "list", length = length(trait_names) - 1)
+if (!is.null(opt$addcovar)) {
+ #If perm strata are being used, it'll be included as the final column in the phenotype file
+ if (!is.null(opt$pstrata)) {
+ covar_names = trait_names[3:length(trait_names) - 1]
+ } else {
+ covar_names = trait_names[3:length(trait_names)]
+ }
+ covars <- pull.pheno(cross_object, covar_names)
+}
+
+# Pull permutation strata out of cross object, if it is being used
+perm_strata = vector()
+if (!is.null(opt$pstrata)) {
+ strata_col = trait_names[length(trait_names)]
+ perm_strata <- pull.pheno(cross_object, strata_col)
+}
+
+# If a marker name is supplied as covariate, get its vector of values and add them as a covariate
+if (!is.null(opt$control)) {
+ marker_covars = create_marker_covars(cross_object, opt$control)
+ covars <- cbind(covars, marker_covars)
+}
+
+# Calculate permutations
+if (opt$nperm > 0) {
+ if (!is.null(opt$filename)){
+ perm_out_file = file.path(opt$outdir, paste("PERM_", opt$filename, sep = "" ))
+ } else {
+ perm_out_file = file.path(opt$outdir, paste(pheno_name, "_PERM_", stri_rand_strings(1, 8), sep = ""))
+ }
+
+ if (!is.null(opt$addcovar) || !is.null(opt$control)){
+ if (!is.null(opt$pstrata)) {
+ verbose_print('Running permutations with cofactors and strata\n')
+ perm_results = scanone(cross_object, pheno.col=1, addcovar=covars, n.perm=opt$nperm, perm.strata=perm_strata, model=opt$model, method=opt$method)
+ } else {
+ verbose_print('Running permutations with cofactors\n')
+ perm_results = scanone(cross_object, pheno.col=1, addcovar=covars, n.perm=opt$nperm, model=opt$model, method=opt$method)
+ }
+ } else {
+ if (!is.null(opt$pstrata)) {
+ verbose_print('Running permutations with strata\n')
+ perm_results = scanone(cross_object, pheno.col=1, n.perm=opt$nperm, perm.strata=perm_strata, model=opt$model, method=opt$method)
+ } else {
+ verbose_print('Running permutations\n')
+ perm_results = scanone(cross_object, pheno.col=1, n.perm=opt$nperm, model=opt$model, method=opt$method)
+ }
+ }
+ write.csv(perm_results, perm_out_file)
+}
+
+if (!is.null(opt$filename)){
+ out_file = file.path(opt$outdir, opt$filename)
+} else {
+ out_file = file.path(opt$outdir, paste(pheno_name, "_", stri_rand_strings(1, 8), sep = ""))
+}
+
+if (!is.null(opt$addcovar) || !is.null(opt$control)){
+ verbose_print('Running scanone with cofactors\n')
+ qtl_results = scanone(cross_object, pheno.col=1, addcovar=covars, model=opt$model, method=opt$method)
+} else {
+ verbose_print('Running scanone\n')
+ qtl_results = scanone(cross_object, pheno.col=1, model=opt$model, method=opt$method)
+}
+write.csv(qtl_results, out_file)