diff options
Diffstat (limited to 'scripts')
| -rw-r--r-- | scripts/calculate_biweight.R | 43 | ||||
| -rw-r--r-- | scripts/rqtl_wrapper.R | 99 | ||||
| -rw-r--r-- | scripts/wgcna_analysis.R | 17 |
3 files changed, 109 insertions, 50 deletions
diff --git a/scripts/calculate_biweight.R b/scripts/calculate_biweight.R deleted file mode 100644 index 8d8366e..0000000 --- a/scripts/calculate_biweight.R +++ /dev/null @@ -1,43 +0,0 @@ - -library(testthat) -library(WGCNA) - -arg_values <- commandArgs(trailingOnly = TRUE) -ParseArgs <- function(args){ - - trait_vals <- as.numeric(unlist(strsplit(args[1], split=" "))) - target_vals <- as.numeric(unlist(strsplit(args[2], split=" "))) - - return(list(trait_vals= c(trait_vals),target_vals = c(target_vals))) - -} -BiweightMidCorrelation <- function(trait_val,target_val){ - - results <-bicorAndPvalue(as.numeric(unlist(trait_val)),as.numeric(unlist(target_val))) - return ((c(c(results$bicor)[1],c(results$p)[1]))) - -} - - - -test_that("biweight results"),{ - vec_1 <- c(1,2,3,4) - vec_2 <- c(1,2,3,4) - - results <- BiweightMidCorrelation(vec_1,vec_2) - expect_equal(c(1.0,0.0),results) -} - - -test_that("parsing args "),{ - my_args <- c("1 2 3 4","5 6 7 8") - results <- ParseArgs(my_args) - - expect_equal(results[1],c(1,2,3,4)) - expect_equal(results[2],c(5,6,7,8)) -} - -parsed_values <- ParseArgs(arg_values) - - -cat(BiweightMidCorrelation(parsed_values[1],parsed_values[2]))
\ No newline at end of file diff --git a/scripts/rqtl_wrapper.R b/scripts/rqtl_wrapper.R index ffff5b9..eb660b5 100644 --- a/scripts/rqtl_wrapper.R +++ b/scripts/rqtl_wrapper.R @@ -9,6 +9,7 @@ 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("--covarstruct"), type="character", help="File detailing which covariates are categorical or numerical"), 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("--pairscan"), action="store_true", default=NULL, help="Run Pair Scan - the R/qtl function scantwo"), @@ -34,6 +35,20 @@ verbose_print <- function(...){ } } +adjustXprobs <- function(cross){ + sex <- getsex(cross)$sex + pr <- cross$geno[["X"]]$prob + stopifnot(!is.null(pr), !is.null(sex)) + + for(i in 1:ncol(pr)) { + pr[sex==0,i,3:4] <- 0 + pr[sex==1,i,1:2] <- 0 + pr[,i,] <- pr[,i,]/rowSums(pr[,i,]) + } + cross$geno[["X"]]$prob <- pr + invisible(cross) +} + if (is.null(opt$geno) || is.null(opt$pheno)){ print_help(opt_parser) stop("Both a genotype and phenotype file must be provided.", call.=FALSE) @@ -52,7 +67,7 @@ get_geno_code <- function(header, name = 'unk'){ return(trim(strsplit(header[mat],':')[[1]][2])) } -geno_to_csvr <- function(genotypes, trait_names, trait_vals, out, sex = NULL, +geno_to_csvr <- function(genotypes, trait_names, trait_vals, out, type, sex = NULL, mapping_scale = "Mb", verbose = FALSE){ # Assume a geno header is not longer than 40 lines header = readLines(genotypes, 40) @@ -149,8 +164,12 @@ for (i in 1:length(trait_names)) { trait_names[i] = paste("T_", this_trait, sep = "") } +# Get type of genotypes, since it needs to be checked before calc.genoprob +header = readLines(geno_file, 40) +type <- get_geno_code(header, 'type') + verbose_print('Generating cross object\n') -cross_object = geno_to_csvr(geno_file, trait_names, trait_vals, cross_file) +cross_object = geno_to_csvr(geno_file, trait_names, trait_vals, cross_file, type) # Calculate genotype probabilities if (!is.null(opt$interval)) { @@ -164,17 +183,41 @@ if (!is.null(opt$interval)) { cross_object <- calc.genoprob(cross_object) } +# If 4way, adjust X chromosome genotype probabilities +if (type == "4-way") { + verbose_print('Adjusting genotype probabilities for 4way cross') + cross_object <- adjustXprobs(cross_object) +} + # Pull covariates out of cross object, if they exist -covars = vector(mode = "list", length = length(trait_names) - 1) +covars <- c() # Holds the covariates which should be passed to R/qtl if (!is.null(opt$addcovar)) { verbose_print('Pulling covariates out of cross object\n') - #If perm strata are being used, it'll be included as the final column in the phenotype file + # If perm strata are being used, it'll be included as the final column in the phenotype file if (!is.null(opt$pstrata)) { - covar_names = trait_names[3:length(trait_names) - 1] + covar_names = trait_names[2:(length(trait_names)-1)] } else { covar_names = trait_names[2:length(trait_names)] } covars <- pull.pheno(cross_object, covar_names) + # Read in the covar description file + covarDescr <- read.table(opt$covarstruct, sep="\t", header=FALSE) + for(x in 1:nrow(covarDescr)){ + cat(covarDescr[x, 1]) + name <- paste0("T_", covarDescr[x, 1]) # The covar description file doesn't have T_ in trait names (the cross object does) + type <- covarDescr[x, 2] + if(type == "categorical"){ + if(length(table(covars[,name])) > 2){ # More then 2 levels create the model matrix for the factor + mdata <- data.frame(toExpand = as.factor(covars[, name])) + options(na.action='na.pass') + modelmatrix <- model.matrix(~ toExpand + 0, mdata)[,-1] + covars <- cbind(covars, modelmatrix) + }else{ # 2 levels? just bind the trait as covar + verbose_print('Binding covars to covars\n') + covars <- cbind(covars, covars[,name]) + } + } + } } # Pull permutation strata out of cross object, if it is being used @@ -250,5 +293,51 @@ if (!is.null(opt$pairscan)) { write.csv(qtl_results[1], out_file) write.csv(qtl_results[2], map_out_file) } else { + # QTL main effects on adjusted longevity + getEffects <- function(sdata, gtsprob, marker = "1_24042124", model = "longevity ~ sex + site + cohort + treatment", trait = "longevity"){ + rownames(sdata) <- 1:nrow(sdata) + rownames(gtsprob) <- 1:nrow(gtsprob) + mp <- gtsprob[, grep(marker, colnames(gtsprob))] + gts <- unlist(lapply(lapply(lapply(apply(mp,1,function(x){which(x > 0.85)}),names), strsplit, ":"), function(x){ + if(length(x) > 0){ return(x[[1]][2]); }else{ return(NA) } + })) + + ismissing <- which(apply(sdata, 1, function(x){any(is.na(x))})) + if(length(ismissing) > 0){ + sdata <- sdata[-ismissing, ] + gts <- gts[-ismissing] + } + + mlm <- lm(as.formula(model), data = sdata) + pheAdj <- rep(NA, nrow(sdata)) + adj <- residuals(mlm) + mean(sdata[, trait]) + pheAdj[as.numeric(names(adj))] <- adj + means <- c(mean(pheAdj[which(gts == "AC")],na.rm=TRUE),mean(pheAdj[which(gts == "AD")],na.rm=TRUE),mean(pheAdj[which(gts == "BC")],na.rm=TRUE),mean(pheAdj[which(gts == "BD")],na.rm=TRUE)) + std <- function(x) sd(x,na.rm=TRUE)/sqrt(length(x)) + stderrs <- c(std(pheAdj[which(gts == "AC")]),std(pheAdj[which(gts == "AD")]),std(pheAdj[which(gts == "BC")]),std(pheAdj[which(gts == "BD")])) + paste0(round(means,0), " ± ", round(stderrs,2)) + } + + if (type == "4-way") { + verbose_print("Get phenotype name + genoprob + all phenotypes + models for 4-way crosses") + traitname <- colnames(pull.pheno(cross_object))[1] + gtsp <- pull.genoprob(cross_object) + allpheno <- pull.pheno(cross_object) + if (!is.null(opt$addcovar)) { + model <- paste0(traitname, " ~ ", paste0(covar_names, sep="", collapse=" + ")) + } else { + model <- paste0(traitname, " ~ 1 ") + } + + meffects <- c() + verbose_print("Getting QTL main effects for 4-way crosses") + for(marker in rownames(qtl_results)){ + meff <- getEffects(allpheno, gtsp, marker = marker, model, trait = traitname) + meffects <- rbind(meffects, meff) + } + qtl_results <- cbind(data.frame(qtl_results[,1:3]), meffects) + colnames(qtl_results)[4:7] <- c("AC", "AD", "BC", "BD") + } + write.csv(qtl_results, out_file) } diff --git a/scripts/wgcna_analysis.R b/scripts/wgcna_analysis.R index 17b3537..b0d25a9 100644 --- a/scripts/wgcna_analysis.R +++ b/scripts/wgcna_analysis.R @@ -6,11 +6,13 @@ library(rjson) options(stringsAsFactors = FALSE); -imgDir = Sys.getenv("GENERATED_IMAGE_DIR") +cat("Running the wgcna analysis script\n") + # load expression data **assumes from json files row(traits)(columns info+samples) # pass the file_path as arg # pass the file path to read json data + args = commandArgs(trailingOnly=TRUE) if (length(args)==0) { @@ -21,6 +23,7 @@ if (length(args)==0) { } inputData <- fromJSON(file = json_file_path) +imgDir = inputData$TMPDIR trait_sample_data <- do.call(rbind, inputData$trait_sample_data) @@ -83,6 +86,11 @@ network <- blockwiseModules(dataExpr, +cat("Generated network \n") + +network + + genImageRandStr <- function(prefix){ randStr <- paste(prefix,stri_rand_strings(1, 9, pattern = "[A-Za-z0-9]"),sep="_") @@ -90,14 +98,19 @@ genImageRandStr <- function(prefix){ return(paste(randStr,".png",sep="")) } + mergedColors <- labels2colors(network$colors) imageLoc <- file.path(imgDir,genImageRandStr("WGCNAoutput")) png(imageLoc,width=1000,height=600,type='cairo-png') + +cat("Generating the CLuster dendrogram\n") + + plotDendroAndColors(network$dendrograms[[1]],mergedColors[network$blockGenes[[1]]], "Module colors", -dendroLabels = FALSE, hang = 0.03, +dendroLabels = NULL, hang = 0.03, addGuide = TRUE, guideHang = 0.05) |