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annotate facets_analysis.R @ 3:d1914f4d9daf draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/facets commit 64ac36125f04497dd51028f307e059fca9ec0503
author artbio
date Sun, 05 Oct 2025 00:55:34 +0000
parents 66a56502199d
children 3f62267c4be7
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66a56502199d planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/facets commit 0176d2cc4f1caf0ab948ef72efb25ccce735461e
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1 #!/usr/bin/env Rscript
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3 # Description:
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4 # This script serves as the backend for the Galaxy FACETS Analysis tool.
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5 # It takes a SNP pileup file as input and performs allele-specific copy
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6 # number analysis using the R package 'facets'.
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7 # ==============================================================================
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9 # --- Load Libraries ---
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10 suppressPackageStartupMessages(library(argparse))
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11 suppressPackageStartupMessages(library(facets))
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13 # --- Source the external plot_facets_enhanced function ---
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14 # This finds the path of the currently running script and sources
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15 # the R function file relative to it.
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16 initial_opts <- commandArgs(trailingOnly = FALSE)
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17 script_path <- dirname(sub("--file=", "", initial_opts[grep("--file=", initial_opts)]))
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18 source(file.path(script_path, "plot_facets_enhanced-v22.R"))
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20 # --- Define and Parse Arguments ---
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22 # Create the parser
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23 parser <- ArgumentParser(description = "Run FACETS algorithm on a SNP pileup file.")
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25 # Define arguments
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26 parser$add_argument("--pileup",
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27 type = "character", required = TRUE,
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28 help = "Path to the gzipped pileup CSV file."
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29 )
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30 parser$add_argument("--sample_id",
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31 type = "character", required = TRUE,
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32 help = "Sample ID used for plot titles and metadata."
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33 )
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35 parser$add_argument("--output_seg",
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36 type = "character", required = TRUE,
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37 help = "Path for the output segmentation file (TSV)."
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38 )
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39 parser$add_argument("--output_summary",
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40 type = "character", required = TRUE,
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41 help = "Path for the output summary file (TSV)."
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42 )
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43 parser$add_argument("--output_plots",
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44 type = "character", required = TRUE,
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45 help = "Path for the main output plots file (PNG)."
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46 )
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47 parser$add_argument("--output_spider",
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48 type = "character", required = TRUE,
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49 help = "Path for the diagnostic spider plot file (PNG)."
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50 )
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51 parser$add_argument("--cval",
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52 type = "double", default = 150,
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53 help = "Critical value for segmentation."
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54 )
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55 parser$add_argument("--min_nhet",
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56 type = "integer", default = 25,
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57 help = "Minimum number of heterozygous SNPs per segment."
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58 )
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59 parser$add_argument("--snp_nbhd",
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60 type = "integer", default = 300,
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61 help = "SNP neighborhood size for pre-processing. Crucial for sparse VCFs."
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62 )
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63 parser$add_argument("--gbuild",
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64 type = "character", default = "hg38",
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65 choices = c("hg19", "hg38", "hg18", "mm9", "mm10"),
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66 help = "Genome build used for alignment."
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67 )
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68
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69 # --- Main Analysis Function ---
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70 main <- function(args) {
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71 # Set seed for reproducibility
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72 set.seed(1965)
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74 # --- Read the data with readSnpMatrix() from facets ---
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75 rcmat <- readSnpMatrix(args$pileup)
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77 # --- Pre-process sample ---
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78 xx <- preProcSample(rcmat, gbuild = args$gbuild, snp.nbhd = args$snp_nbhd)
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80 # --- Process sample (segmentation) ---
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81 oo <- procSample(xx, cval = args$cval, min.nhet = args$min_nhet)
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83 # --- Estimate ploidy/purity ---
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84 fit <- emcncf(oo)
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85
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86 # Write the main segmentation file
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87 cncf_output <- fit$cncf
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88 if (nrow(cncf_output) > 0) {
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89 cncf_output$purity <- fit$purity
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90 cncf_output$ploidy <- fit$ploidy
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91 # Reorder columns to have purity/ploidy first for clarity
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92 cncf_output <- cncf_output[, c("purity", "ploidy", setdiff(names(cncf_output), c("purity", "ploidy")))]
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93 }
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94 write.table(cncf_output, file = args$output_seg, sep = "\t", quote = FALSE, row.names = FALSE)
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95
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96 # Write a key-value summary file
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97 # A NULL value is replaced by NA to preserve vector length.
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98 summary_df <- data.frame(
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99 Parameter = c(
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100 "sample_id", "purity", "ploidy", "dipLogR", "loglik",
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101 "cval_param", "min_nhet_param", "snp_nbhd_param", "gbuild_param"
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102 ),
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103 Value = c(
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104 args$sample_id,
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105 ifelse(is.null(fit$purity), NA, fit$purity),
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106 ifelse(is.null(fit$ploidy), NA, fit$ploidy),
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107 ifelse(is.null(fit$dipLogR), NA, fit$dipLogR),
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108 ifelse(is.null(fit$loglik), NA, fit$loglik),
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109 args$cval,
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110 args$min_nhet,
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111 args$snp_nbhd,
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112 args$gbuild
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113 )
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114 )
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115 write.table(summary_df, file = args$output_summary, sep = "\t", quote = FALSE, row.names = FALSE)
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116
3
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117 # Generate the plots PNG
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118 png(file = args$output_plots, width = 12, height = 8, units = "in", res = 300)
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119 plotSample(x = oo, emfit = fit, sname = args$sample_id)
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120 plot_facets_enhanced(oo, emfit = fit, plot.type = "em", sname = args$sample_id)
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121 dev.off()
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122 png(file = args$output_spider, width = 8, height = 8, units = "in", res = 300)
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123 logRlogORspider(oo$out, oo$dipLogR)
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124 dev.off()
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125 }
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126
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127 # --- Execution Block ---
2
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128 if (!interactive()) {
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129 args <- parser$parse_args()
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130 main(args)
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131 }