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annotate mykrobe_parser.R @ 0:6eae14751768 draft

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author nml
date Fri, 28 Sep 2018 16:01:47 -0400
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children f2608dccd3e0
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1 # Copyright Government of Canada 2018
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2 #
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3 # Written by: National Microbiology Laboratory, Public Health Agency of Canada
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4 #
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5 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use
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6 # this work except in compliance with the License. You may obtain a copy of the
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7 # License at:
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8 #
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9 # http://www.apache.org/licenses/LICENSE-2.0
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10 #
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11 # Unless required by applicable law or agreed to in writing, software distributed
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12 # under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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13 # CONDITIONS OF ANY KIND, either express or implied. See the License for the
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14 # specific language governing permissions and limitations under the License.
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17 # Parsing JSONs from Mykrobe Predict into CSV reports
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18 # Take the JSON output from Mykrobe, rearrange, output for LIMS
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19 # Adrian Zetner
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20 # August 2018
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22 # Libraries ####
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23 library(jsonlite, quietly = T)
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24 library(here, quietly = T)
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25 suppressMessages(library(dplyr, quietly = T))
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26 suppressMessages(library(purrr, quietly = T))
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27 library(tidyr, quietly = T)
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28 library(stringr, quietly = T)
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29 library(optparse, quietly = T)
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30
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31 # Define custom functions, variables, and paths. Collect and use CL arguments ####
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33 # Here's a function to recreate that output table from the input JSON files
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34
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35 getResults <- function(listelement){
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36 # Define list levels for various elements of the json
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37 species <- names(listelement[[1]][["phylogenetics"]][["species"]])
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38 lineage <- names(listelement[[1]][["phylogenetics"]][["lineage"]])
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39 phylo_group <- names(listelement[[1]][["phylogenetics"]][["phylo_group"]])
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40 if("Non_tuberculosis_mycobacterium_complex" %in% phylo_group){
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41 warning(paste("Non-tuberculosis mycobacteria detected in file ", names(listelement), ". Skipping.", sep = ""))
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42 return()}
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43
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44 # Start building a list of all your various elements
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45 temp <- list(mykrobe_version = listelement[[1]][["version"]][["mykrobe-predictor"]],
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46 file = names(listelement), # One element
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47 plate_name = "test", # This probably needs changing
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48 sample = "sequence_calls", # Likewise change this
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49 phylo_group = phylo_group, # As above
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50 species = species, # As above
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51 lineage = lineage, # As above
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52 # The following expressions drill down into the list elements and pull out what is needed.
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53 # It's inelegant and vulnerable to changes in the input formats but if they're consistent it'll work
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54 phylo_group_per_covg = listelement[[1]][["phylogenetics"]][["phylo_group"]][[phylo_group]][["percent_coverage"]],
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55 species_per_covg = listelement[[1]][["phylogenetics"]][["species"]][[species]][["percent_coverage"]],
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56 lineage_per_covg = listelement[[1]][["phylogenetics"]][["lineage"]][[lineage]][["percent_coverage"]],
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57 phylo_group_depth = listelement[[1]][["phylogenetics"]][["phylo_group"]][[phylo_group]][["median_depth"]],
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58 species_depth = listelement[[1]][["phylogenetics"]][["species"]][[species]][["median_depth"]],
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59 lineage_depth = listelement[[1]][["phylogenetics"]][["lineage"]][[lineage]][["median_depth"]],
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60 Mykrobe_Resistance_probe_set = basename(listelement[[1]][["probe_sets"]][2]) # Is it always the second?
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61 )
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62
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63 # Super cool nested and vectorized (for SPEED!) functions to grab the predictions for drug sensitivity and gene variants
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64 # Both produce character vectors of the same length as the number of drugs tested in the same order
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65 # All of these also check if there are missing values in drug/susceptibility/variant elements and adds the column anyhow
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66
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67 if(length(map_chr(listelement[[1]][["susceptibility"]], "predict")) != 0){
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68 temp$susceptibility <- map_chr(listelement[[1]][["susceptibility"]], "predict")
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69 }else{
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70 temp$susceptibility <- NA
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71 }
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72
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73 if(length(names(listelement[[1]][["susceptibility"]])) != 0){
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74 temp$drug <- names(listelement[[1]][["susceptibility"]])
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75 }else{
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76 temp$drug <- NA
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77 }
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78
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79 mapped.variants <- map(listelement[[1]][["susceptibility"]], # Dig into the lists, pull out variants and collapse into chr vector
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80 ~ imap(.x[["called_by"]], # imap is shorthand for map2(x, names(x), ...), calling .y gets you the name / index of the current element
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81 ~ paste(.y,
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82 .x[["info"]][["coverage"]][["alternate"]][["median_depth"]],
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83 .x[["info"]][["coverage"]][["reference"]][["median_depth"]],
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84 .x[["info"]][["conf"]],
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85 sep = ":"))) %>%
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86 map_chr(~ paste(.x, collapse = "__"))
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87
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88 if(length(mapped.variants) != 0){
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89 temp$`variants (gene:alt_depth:wt_depth:conf)` <- mapped.variants
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90 }else{
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91 temp$`variants (gene:alt_depth:wt_depth:conf)` <- NA
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92 }
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93
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94 temp$`genes (prot_mut-ref_mut:percent_covg:depth)` <- NA
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95
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96 # Take that list and mash all the elements together as columns in a tibble, recycling as needed to fill in space
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97 # eg. phylo_group is repeated/recycled as many times as there are drugs tested
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98 as_tibble(temp)
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99 }
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100
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101 sink(stdout(), type = "message")
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102
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103 suppressPackageStartupMessages({
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104 library(jsonlite)
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105 library(here)
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106 library(dplyr)
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107 library(purrr)
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108 library(tidyr)
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109 library(stringr)
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110 library(optparse)
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111 })
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112
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113 # Get command line arguments with optparse
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114 option_list = list(
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115 make_option(c("-f", "--file"),
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116 type="character",
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117 default=NULL,
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118 help='dataset file name or quoted comma separated names: eg. "file1,file2,file3"',
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119 metavar="character"),
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120 make_option(c("-d", "--dir"),
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121 type="character",
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122 default=NULL,
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123 help="directory location of json files",
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124 metavar="character"),
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125 make_option(c("-v", "--version"),
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126 type="character",
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127 default="",
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128 help="Mykrobe Workflow Version",
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129 metavar="character"),
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130 make_option(c("-D", "--depth"),
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131 type="integer",
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132 default=5,
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133 help="Minimum depth of coverage [default= %default]",
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134 metavar="integer"),
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135 make_option(c("-c", "--conf"),
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136 type="integer",
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137 default=10,
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138 help="Minimum genotype confidence for variant genotyping [default= %default]",
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139 metavar="integer"),
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140 make_option(c("-n", "--name"),
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141 type="character",
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142 default="",
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143 help="Name of the run",
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144 metavar="character")
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145 )
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146
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147 opt_parser = OptionParser(option_list=option_list)
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148 opt = parse_args(opt_parser)
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149
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150 if (is.null(opt$file) & is.null(opt$dir)){
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151 print_help(opt_parser)
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152 stop("At least one argument must be supplied to input file or directory", call.=FALSE)
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153 }
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154
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155 # Parameters to take from Galaxy/CL as args or however works best
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156 params <- c("", # Lims_Comment
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157 "", # Lims_INTComment
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158 opt$version, # Mykrobe_Workflow_Version
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159 opt$depth, # Mykrobe_min_depth_default_5
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160 opt$conf, # Mykrobe_min_conf_default_10
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161 "", # LIMS_file - empty as it's an upload field in LIMS
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162 opt$name) # LIMS_filename
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163
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164 names(params) <- c("Lims_Comment",
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165 "Lims_INTComment",
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166 "Mykrobe_Workflow_Version",
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167 "Mykrobe_min_depth_default_5",
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168 "Mykrobe_min_conf_default_10",
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169 "LIMS_file",
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170 "LIMS_filename")
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171
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172
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173 # A default report in the order our LIMS requires
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174
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175 # Make a default dataframe to combine the rest into and enforce column order / fill missing ones with NAs
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176 columns <- c("file",
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177 "Mykrobe_fabG1",
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178 "Mykrobe_katG",
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179 "Mykrobe_ahpC",
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180 "Mykrobe_inhA",
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181 "Mykrobe_ndh",
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182 "Isoniazid_R_mutations",
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diff changeset
183 "Isoniazid_Prediction",
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184 "Mykrobe_rpoB",
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185 "Rifampicin_R_mutations",
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diff changeset
186 "Rifampicin_Prediction",
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187 "Mykrobe_embB",
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188 "Mykrobe_embA",
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189 "Ethambutol_R_mutations",
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190 "Ethambutol_Prediction",
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191 "Mykrobe_pncA",
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192 "Mykrobe_rpsA",
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193 "Pyrazinamide_R_mutations",
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194 "Pyrazinamide_Prediction",
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195 "Mykrobe_gyrA",
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196 "Quinolones_R_mutations",
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197 "Quinolones_Prediction",
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198 "Mykrobe_rpsL",
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199 "Mykrobe_Streptomycin_rrs",
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200 "Mykrobe_Streptomycin_gid",
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201 "Streptomycin_R_mutations",
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202 "Streptomycin_Prediction",
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203 "Mykrobe_Amikacin_rrs",
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204 "Amikacin_R_mutations",
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205 "Amikacin_Prediction",
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206 "Mykrobe_Capreomycin_rrs",
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207 "Mykrobe_Capreomycin_tlyA",
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208 "Capreomycin_R_mutations",
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209 "Capreomycin_Prediction",
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210 "Mykrobe_Kanamycin_rrs",
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211 "Mykrobe_Kanamycin_eis",
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212 "Kanamycin_R_mutations",
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213 "Kanamycin_Prediction",
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214 "Lims_Comment",
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215 "Lims_INTComment",
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216 "Mykrobe_Workflow_Version",
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217 "mykrobe_version",
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218 "Mykrobe_Resistance_probe_set",
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219 "Mykrobe_min_depth_default_5",
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220 "Mykrobe_min_conf_default_10",
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221 "LIMS_file",
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222 "LIMS_filename")
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223
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224 report <- setNames(data.frame(matrix("", ncol = length(columns), nrow = 1), stringsAsFactors = F), columns)
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225
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226
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227 # List of drugs that are tested
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228 all_drugs <- c("Isoniazid",
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229 "Rifampicin",
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230 "Ethambutol",
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231 "Pyrazinamide",
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232 "Moxifloxacin_Ofloxacin",
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233 "Streptomycin",
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234 "Amikacin",
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235 "Capreomycin",
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236 "Kanamycin")
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237
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238 # Do Stuff ####
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239
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240 # Import all the JSON files into a list of lists format ####
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241
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242 if (is.null(opt$file)){
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243 # opt$dir is used to get the list of files, a vector of non-duplicated files is then passed to map
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244 files <- list.files(path = opt$dir,
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245 pattern = "*.json",
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246 full.names = T)
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247 }else{
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248 files <- unlist(strsplit(opt$file, ","))
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249 }
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250
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251 files <- files[!duplicated(basename(files))]
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252
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253 list.of.json.files <- map(files,
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254 ~ fromJSON(.x, simplifyDataFrame = F)
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255 )
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256
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257
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258 # Apply that getResults function to each element in your list then bash it together into a final report
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259
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260 temp <- map(list.of.json.files, getResults) %>%
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261 bind_rows()
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262
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263
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264 # Predictions of resistance or susceptibility
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265 predictions.table <-
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266 temp %>%
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267 select(file, drug, susceptibility) %>%
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268 mutate(drug = paste(drug, "_Prediction", sep = "")) %>%
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269 spread(drug, susceptibility, fill = "failed") %>%
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270 select(-starts_with("NA"))
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271
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272 if (length(predictions.table) == 1){
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273 print(predictions.table)
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274 stop("No susceptibility results in files specified. Did the testing fail?", call.=FALSE)
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275 }
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276
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277 # Variants
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278 # Multiple resistance mutations and confidence per drug in the X_R_mutations column
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279 # Actual protein changes in Mykrobe_X columns
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280
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281 variants.temp <-
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282 temp %>%
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283 select(file, drug, variants = `variants (gene:alt_depth:wt_depth:conf)`) %>%
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284 mutate(variants = replace(variants, variants == "", NA)) %>% # Make missing data consistent...
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285 filter(!is.na(variants)) %>% # ...Then get rid of it
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286 mutate(tempcols = paste(drug, "R_mutations", sep = "_")) %>%
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287 mutate(R_mutations = variants) %>%
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288 mutate(variants = strsplit(variants, "__")) %>% # Split the mutations across rows (list first then split across rows)
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289 unnest(variants) %>%
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290 separate(variants, c("gene", "mutation"), "_") %>%
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291 mutate(columnname = ifelse(gene %in% c("tlyA", "rrs", "gid"), # Check for columns that include the drug name or not and paste accordingly
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292 paste("Mykrobe", drug, gene, sep = "_"),
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293 paste("Mykrobe", gene, sep = "_"))) %>%
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294 # Extract out the mutation information with a regex that covers all potential genes
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295 # This regex looks for whatever is ahead of the first colon and after the last hyphen
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296 mutate(mutation = str_match(mutation, "(.*)-.*:")[,2]) %>%
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297 select(file, tempcols, R_mutations, columnname, mutation)
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298
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299 # Split each kind of variants into its own temp table then merge
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300 variants.1 <-
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301 variants.temp %>%
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302 select(file, tempcols, R_mutations) %>%
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303 distinct() %>%
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304 spread(tempcols, R_mutations)
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305
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306 variants.2 <-
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307 variants.temp %>%
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308 select(file, columnname, mutation) %>%
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309 group_by(file, columnname) %>%
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310 summarise(mutation = paste(mutation, collapse = ";")) %>%
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311 spread(columnname, mutation)
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312
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313 variants.table <- full_join(variants.1, variants.2, by = "file")
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314
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315 # Make a report ####
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316
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317 report <-
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318 temp %>%
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319 select(file, mykrobe_version, Mykrobe_Resistance_probe_set) %>% # Get important info from initial table
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320 distinct() %>% # Drop duped rows and combine all the tables together
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321 full_join(variants.table) %>%
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322 full_join(predictions.table) %>%
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323 bind_rows(report) %>% # Use bind_rows to add columns (eg. unteseted drugs) to the final output
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324 filter(file != "")
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325
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326 # Only add the 'no mutation' replacement to the columns that actually have a result
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327 report <-
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328 report %>%
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329 filter_at(vars(ends_with("_Prediction")), any_vars(. != "failed")) %>%
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330 mutate_at(vars(starts_with("Mykrobe_")), funs(replace(., is.na(.), "No Mutation"))) %>%
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331 full_join(anti_join(report, ., by = "file")) %>%
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332 select(columns) %>%
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333 rename(Moxifloxacin_Ofloxacin_R_mutations = Quinolones_R_mutations,
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334 Moxifloxacin_Ofloxacin_Prediction = Quinolones_Prediction)
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335
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336
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337 # Add in the parameters fed from Galaxy using named character vector
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338 report <-
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339 report %>%
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340 mutate(
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341 Lims_Comment = params["Lims_Comment"],
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342 Lims_INTComment = params["Lims_INTComment"],
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343 Mykrobe_Workflow_Version = params["Mykrobe_Workflow_Version"],
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344 Mykrobe_min_depth_default_5 = params["Mykrobe_min_depth_default_5"],
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345 Mykrobe_min_conf_default_10 = params["Mykrobe_min_conf_default_10"],
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346 LIMS_file = params["LIMS_file"],
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347 LIMS_filename = params["LIMS_filename"]
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348 )
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349
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350
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351 #View(report)
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352
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353 # Write some output
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354 # Report as is
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355 write.csv(report, "output-report.csv", row.names = F)
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356 print("Writing Susceptibility report to CSV as output-report.csv")
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357
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358 # Select specific columns from temp and output them
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359 temp %>%
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360 select(file,
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361 phylo_group,
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362 species,
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363 lineage,
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364 phylo_group_per_covg,
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365 species_per_covg,
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366 lineage_per_covg,
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367 phylo_group_depth,
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368 species_depth,
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369 lineage_depth) %>%
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370 distinct() %>%
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371 write.csv("output-jsondata.csv", row.names = F)
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372 print("Writing JSON data to CSV as output-jsondata.txt")
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373 sink(NULL, type="message") # close the sink
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374
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375 quit()