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annotate facets_analysis.R @ 6:625038b7d764 draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/facets commit 8cced47697e5777fd60dacc60300e770bd409e9d
author artbio
date Mon, 06 Oct 2025 15:50:12 +0000
parents 3f62267c4be7
children 86bcdc94b008
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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:
66a56502199d planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/facets commit 0176d2cc4f1caf0ab948ef72efb25ccce735461e
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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
66a56502199d planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/facets commit 0176d2cc4f1caf0ab948ef72efb25ccce735461e
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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$add_argument("--output_vcf",
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52 type = "character", required = TRUE,
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53 help = "Path for the output VCF file with CNV calls."
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54 )
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55 parser$add_argument("--cval",
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56 type = "double", default = 150,
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57 help = "Critical value for segmentation."
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58 )
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59 parser$add_argument("--min_nhet",
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60 type = "integer", default = 25,
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61 help = "Minimum number of heterozygous SNPs per segment."
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62 )
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63 parser$add_argument("--snp_nbhd",
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64 type = "integer", default = 300,
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65 help = "SNP neighborhood size for pre-processing. Crucial for sparse VCFs."
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66 )
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67 parser$add_argument("--gbuild",
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68 type = "character", default = "hg38",
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69 choices = c("hg19", "hg38", "hg18", "mm9", "mm10"),
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70 help = "Genome build used for alignment."
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71 )
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72 parser$add_argument("--enable_merging",
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73 action = "store_true", default = FALSE,
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74 help = "If specified, enables the post-processing step to merge adjacent and similar CNV segments."
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75 )
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76 parser$add_argument("--merge_gap_abs",
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77 type = "integer", default = 1000000,
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78 help = "Absolute maximum gap in bp to merge adjacent CNV segments."
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79 )
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80 parser$add_argument("--merge_gap_rel",
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81 type = "double", default = 0.5,
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82 help = "Relative maximum gap (fraction of avg. segment length) to merge segments."
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83 )
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84
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85 #' Classify CNV segments based on TCN/LCN
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86 classify_cnv <- function(cncf_df) {
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87 cncf_df$sv_type <- NA_character_
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88 cncf_df$sv_type[cncf_df$tcn.em == 2 & (cncf_df$lcn.em == 1 | is.na(cncf_df$lcn.em))] <- "NEUTR"
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89 cncf_df$sv_type[is.na(cncf_df$sv_type) & cncf_df$tcn.em > 2] <- "DUP"
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90 cncf_df$sv_type[is.na(cncf_df$sv_type) & cncf_df$tcn.em < 2 & !is.na(cncf_df$lcn.em) & cncf_df$lcn.em > 0] <- "HEMIZYG_DEL"
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91 cncf_df$sv_type[is.na(cncf_df$sv_type) & cncf_df$tcn.em < 2 & !is.na(cncf_df$lcn.em) & cncf_df$lcn.em == 0] <- "HOMOZYG_DEL"
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92 cncf_df$sv_type[is.na(cncf_df$sv_type) & cncf_df$tcn.em == 2 & !is.na(cncf_df$lcn.em) & cncf_df$lcn.em == 0] <- "CN_LOH"
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93
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94 # Remplacer les NA restants (si tcn.em < 2 mais lcn.em est NA) par un type général
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95 cncf_df$sv_type[is.na(cncf_df$sv_type) & cncf_df$tcn.em < 2] <- "DEL"
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96
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97 return(cncf_df)
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98 }
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99
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100 #' Create a VCF header (explicit version)
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101 create_vcf_header <- function(sample_id, purity, ploidy) {
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102 header <- c(
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103 "##fileformat=VCFv4.2",
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104 paste0("##fileDate=", format(Sys.Date(), "%Y%m%d")),
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105 paste0("##source=FACETS_v", packageVersion("facets")),
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106 "##INFO=<ID=END,Number=1,Type=Integer,Description=\"End position of the variant\">",
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107 "##INFO=<ID=SVTYPE,Number=1,Type=String,Description=\"Type of structural variant (standard VCF tags: DEL, DUP, CNV)\">",
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108 "##INFO=<ID=SVLEN,Number=1,Type=Integer,Description=\"Length of the SV\">",
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109 # --- MODIFICATION ---
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110 "##INFO=<ID=EVENT,Number=1,Type=String,Description=\"FACETS event classification. Possible values: DUP, HEMIZYG_DEL, HOMOZYG_DEL, CN_LOH\">",
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111 # --- FIN MODIFICATION ---
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112 "##INFO=<ID=TCN,Number=1,Type=Integer,Description=\"Total Copy Number (EM fit)\">",
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113 "##INFO=<ID=LCN,Number=1,Type=Integer,Description=\"Lesser Copy Number (EM fit)\">",
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114 "##INFO=<ID=NUM_MARK,Number=1,Type=Integer,Description=\"Number of SNPs in the segment\">",
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115 "##INFO=<ID=NHET,Number=1,Type=Integer,Description=\"Number of heterozygous SNPs in the segment\">",
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116 paste0("##FACETS_PURITY=", round(purity, 4)),
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117 paste0("##FACETS_PLOIDY=", round(ploidy, 4)),
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118 "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO"
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119 )
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120 return(header)
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121 }
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122
6
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123 # ==============================================================================
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124 # Function to merge adjacent and similar CNV segments
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125 #
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126 # This function implements a merging algorithm that reflects human
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127 # by using a hybrid proximity condition. Two segments are merged
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128 # if they have the same CNV state and are close to each other, both in absolute
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129 # and relative terms.
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130 #
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131 # @param cnv_df A data frame of CNV calls, expected to have columns like
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132 # 'chrom', 'start', 'end', 'svtype', 'tcn.em', 'lcn.em', etc.
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133 # @param max_gap_abs An integer. The absolute maximum distance (in bp) allowed
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134 # between two segments to consider them for merging. Acts as a safeguard.
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135 # @param max_gap_rel A numeric value (0 to 1). The maximum relative distance,
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136 # expressed as a fraction of the average length of the two adjacent segments.
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137 # @return A new data frame with the similar adjacent segments merged.
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138 # ==============================================================================
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139 merge_segments <- function(cnv_df, max_gap_abs = 1000000, max_gap_rel = 0.5) {
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140 # If there's nothing to merge, return the original data frame
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141 if (nrow(cnv_df) < 2) {
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142 return(cnv_df)
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143 }
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144
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145 # Ensure the data frame is sorted by genomic position
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146 cnv_df <- cnv_df[order(cnv_df$chrom, cnv_df$start), ]
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147
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148 merged_rows <- list()
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149 current_row <- cnv_df[1, ]
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150
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151 for (i in 2:nrow(cnv_df)) {
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152 next_row <- cnv_df[i, ]
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153
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154 # Basic criteria: segments must be of the same type and CN state
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155 same_chrom <- current_row$chrom == next_row$chrom
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156 same_svtype <- current_row$svtype == next_row$svtype
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157 same_event <- current_row$event == next_row$event
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158 same_tcn <- current_row$tcn.em == next_row$tcn.em
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159 same_lcn <- identical(current_row$lcn.em, next_row$lcn.em) # Handles NA safely
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160
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161 # If basic criteria are met, evaluate proximity
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162 if (same_chrom && same_svtype && same_event && same_tcn && same_lcn) {
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163 gap <- next_row$start - current_row$end
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164
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165 # Calculate the relative threshold based on the average size of the two segments
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166 len_a <- current_row$end - current_row$start
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167 len_b <- next_row$end - next_row$start
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168 relative_threshold <- ((len_a + len_b) / 2) * max_gap_rel
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169
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170 # Hybrid merge condition: gap must be below BOTH absolute and relative thresholds
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171 if (gap >= 0 && gap <= max_gap_abs && gap <= relative_threshold) {
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172 # Merge: update the end of the current segment and aggregate numeric fields
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173 current_row$end <- next_row$end
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174 current_row$num.mark <- current_row$num.mark + next_row$num.mark
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175 current_row$nhet <- current_row$nhet + next_row$nhet
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176
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177 # Skip to the next iteration to try merging the newly expanded segment
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178 # with the one that follows.
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179 next
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180 }
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181 }
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182
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183 # If no merge occurred, the current segment is final. Save it.
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184 merged_rows <- append(merged_rows, list(current_row))
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185 # The next segment becomes the new current segment.
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186 current_row <- next_row
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187 }
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188
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189 # Append the very last segment (which is either a standalone or the result of the last merge)
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190 merged_rows <- append(merged_rows, list(current_row))
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191
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192 # Reconstruct a single data frame from the list of merged rows
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193 do.call(rbind, merged_rows)
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194 }
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195 # --- Main Analysis Function ---
2
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196 main <- function(args) {
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197 # Set seed for reproducibility
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198 set.seed(1965)
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199
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200 # --- Read the data with readSnpMatrix() from facets ---
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201 rcmat <- readSnpMatrix(args$pileup)
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202
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203 # --- Pre-process sample ---
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204 xx <- preProcSample(rcmat, gbuild = args$gbuild, snp.nbhd = args$snp_nbhd)
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205
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206 # --- Process sample (segmentation) ---
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207 oo <- procSample(xx, cval = args$cval, min.nhet = args$min_nhet)
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208
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209 # --- Estimate ploidy/purity ---
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210 fit <- emcncf(oo)
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211
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212 # Write the main segmentation file
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213 cncf_output <- fit$cncf
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214 if (nrow(cncf_output) > 0) {
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215 cncf_output$purity <- fit$purity
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216 cncf_output$ploidy <- fit$ploidy
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217 # Reorder columns to have purity/ploidy first for clarity
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218 cncf_output <- cncf_output[, c("purity", "ploidy", setdiff(names(cncf_output), c("purity", "ploidy")))]
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219 }
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220 write.table(cncf_output, file = args$output_seg, sep = "\t", quote = FALSE, row.names = FALSE)
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221
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222 # Write a key-value summary file
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223 # A NULL value is replaced by NA to preserve vector length.
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224 summary_df <- data.frame(
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225 Parameter = c(
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226 "sample_id", "purity", "ploidy", "dipLogR", "loglik",
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227 "cval_param", "min_nhet_param", "snp_nbhd_param", "gbuild_param"
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228 ),
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229 Value = c(
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230 args$sample_id,
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231 ifelse(is.null(fit$purity), NA, fit$purity),
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232 ifelse(is.null(fit$ploidy), NA, fit$ploidy),
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233 ifelse(is.null(fit$dipLogR), NA, fit$dipLogR),
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234 ifelse(is.null(fit$loglik), NA, fit$loglik),
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235 args$cval,
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236 args$min_nhet,
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237 args$snp_nbhd,
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238 args$gbuild
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239 )
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240 )
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241 write.table(summary_df, file = args$output_summary, sep = "\t", quote = FALSE, row.names = FALSE)
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242
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243 # Generate the plots PNG
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244 png(file = args$output_plots, width = 12, height = 8, units = "in", res = 300)
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245 plotSample(x = oo, emfit = fit, sname = args$sample_id)
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246 plot_facets_enhanced(oo, emfit = fit, plot.type = "em", sname = args$sample_id)
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247 dev.off()
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248 png(file = args$output_spider, width = 8, height = 8, units = "in", res = 300)
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249 logRlogORspider(oo$out, oo$dipLogR)
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250 dev.off()
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251
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252 # --- Generate VCF file ---
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253
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254 # Classify segments and define standard SVTYPEs + detailed EVENTs
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255 cncf_for_vcf <- fit$cncf
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256 if (nrow(cncf_for_vcf) > 0) {
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257 cncf_for_vcf$svtype <- NA_character_
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258 cncf_for_vcf$event <- NA_character_
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259
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260 # Duplications
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261 cncf_for_vcf[cncf_for_vcf$tcn.em > 2, c("svtype", "event")] <- c("DUP", "DUP")
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262
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263 # Deletions
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264 cncf_for_vcf[cncf_for_vcf$tcn.em < 2, c("svtype")] <- "DEL"
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265 cncf_for_vcf[cncf_for_vcf$tcn.em == 1, c("event")] <- "HEMIZYG_DEL"
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266 cncf_for_vcf[cncf_for_vcf$tcn.em == 0, c("event")] <- "HOMOZYG_DEL"
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267
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268 # Copy-Neutral LOH
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269 cncf_for_vcf[cncf_for_vcf$tcn.em == 2 & !is.na(cncf_for_vcf$lcn.em) & cncf_for_vcf$lcn.em == 0, c("svtype", "event")] <- c("CNV", "CN_LOH")
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270
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271 # Filter normal segments (where'svtype' is still NA)
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272 cnv_calls <- cncf_for_vcf[!is.na(cncf_for_vcf$svtype), ]
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273 } else {
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274 cnv_calls <- data.frame()
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275 }
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276
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277
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278 if (nrow(cnv_calls) > 0) {
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279 if (args$enable_merging) {
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280 cnv_calls <- merge_segments(
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281 cnv_df = cnv_calls,
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282 max_gap_abs = args$merge_gap_abs,
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283 max_gap_rel = args$merge_gap_rel
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284 )
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285 }
4
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286 vcf_header <- create_vcf_header(args$sample_id, fit$purity, fit$ploidy)
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287
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288 vcf_body <- apply(cnv_calls, 1, function(seg) {
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289 cnv_calls <- merge_segments(cnv_calls)
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290 alt_allele <- paste0("<", seg["svtype"], ">")
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291 info <- paste0(
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292 "END=", seg["end"],
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293 ";SVTYPE=", seg["svtype"],
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294 ";SVLEN=", as.integer(seg["end"]) - as.integer(seg["start"]),
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295 ";TCN=", seg["tcn.em"],
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296 ";LCN=", ifelse(is.na(seg["lcn.em"]), ".", seg["lcn.em"]),
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297 ";EVENT=", seg["event"],
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298 ";NUM_MARK=", seg["num.mark"],
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299 ";NHET=", seg["nhet"]
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300 )
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301 # Remove any space(s) immediately following an '=' sign in the INFO string.
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302 info <- gsub("=\\s+", "=", info)
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303
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304 paste(seg["chrom"], seg["start"], ".", "N", alt_allele, ".", "PASS", info, sep = "\t")
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305 })
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306
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307 writeLines(c(vcf_header, vcf_body), con = args$output_vcf)
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308 } else {
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309 vcf_header <- create_vcf_header(args$sample_id, fit$purity, fit$ploidy)
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310 writeLines(vcf_header, con = args$output_vcf)
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311 }
2
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312 }
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313
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314 # --- Execution Block ---
2
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315 if (!interactive()) {
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316 args <- parser$parse_args()
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317 main(args)
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318 }