assess genotype and biomarker resolution rename

pcgr/config.py

@@ -119,6 +119,7 @@ def create_config(arg_dict, workflow = "PCGR"):
     if workflow == "CPSR":        
         conf_options['sample_properties']['phenotype'] = 'None'
         conf_options['sample_properties']['site'] = 'Hereditary (blood)'
+        conf_options['sample_properties']['genotypes_available'] = 0
         conf_options['visual_reporting']['table_display'] = str(arg_dict['report_table_display'])
         conf_options['gene_panel'] = {
             'panel_id': str(arg_dict['virtual_panel_id']),

pcgr/cpsr.py

@@ -193,11 +193,10 @@ def run_cpsr(conf_options, cpsr_paths):
 
         ## Write YAML configuration file  - settings, path to files, reference bundle etc
         yaml_data = populate_config_data(conf_options, data_dir, workflow = "CPSR", logger = logger)
-
         with open(yaml_fname, "w") as outfile:
             outfile.write(yaml.dump(yaml_data))
         outfile.close()
-
+        
         ## CPSR|VEP - run Variant Effect Predictor on query VCF with LoF and NearestExonJB plugins
         vep_command = vep.get_command(file_paths = cpsr_paths, 
                                       conf_options = yaml_data, 
@@ -280,7 +279,15 @@ def run_cpsr(conf_options, cpsr_paths):
         variant_set = \
            variant.append_annotations(
               output_pass_vcf2tsv_gz, pcgr_db_dir = cpsr_paths["db_dir"], logger = logger)
-        variant_set = variant.clean_annotations(variant_set, yaml_data, germline = True, logger = logger)        
+        variant_set = variant.clean_annotations(variant_set, yaml_data, germline = True, logger = logger)
+        if {'GENOTYPE'}.issubset(variant_set.columns):
+            if variant_set.loc[variant_set['GENOTYPE'] == '.'].empty and variant_set.loc[variant_set['GENOTYPE'] == 'undefined'].empty:
+                yaml_data['conf']['sample_properties']['genotypes_available'] = 1
+
+        with open(yaml_fname, "w") as outfile:
+            outfile.write(yaml.dump(yaml_data))
+        outfile.close()
+
         variant_set.to_csv(output_pass_tsv_gz, sep="\t", compression="gzip", index=False)
         if not debug:
             utils.remove(output_pass_vcf2tsv_gz)

pcgr/variant.py

@@ -44,7 +44,8 @@ def set_genotype(variant_set: pd.DataFrame, logger) -> pd.DataFrame:
         variant_set.loc[variant_set['GT'].isin(heterozygous_states),"GENOTYPE"] = "heterozygous"
     else:
         variant_set['GENOTYPE'] = "undefined"
-
+
+    variant_set = variant_set.astype({'GENOTYPE':'string'})  
     return(variant_set)
 
 def append_annotations(vcf2tsv_gz_fname: str, pcgr_db_dir: str, logger):

pcgrr/R/biomarkers.R

@@ -1011,7 +1011,10 @@ expand_biomarker_items <- function(
         "GENOMIC_CHANGE",
         "GENOME_VERSION",
         "SAMPLE_ID",
+        "GENOTYPE",
+        "VARIANT_CLASS",
         "SYMBOL",
+        "GENENAME",
         "ENTREZGENE",
         "CONSEQUENCE",
         "PROTEIN_CHANGE",

pcgrr/R/input_data.R

@@ -247,26 +247,27 @@ load_dna_variants <- function(
                        "BIOMARKER_MATCHTYPE"),
               sep = "\\|"
             ) |>
-            dplyr::mutate(BIOMARKER_MAPPING = dplyr::case_when(
-              stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_genomic_coord") ~ "genomic",
-              !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_genomic_coord") &
-                stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_hgvsp_principal") ~ "hgvsp",
-              !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_genomic_coord") &
-                !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_hgvsp_principal") &
-                stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_codon_principal") ~ "codon",
-              !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_genomic_coord") &
-                !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_hgvsp_principal") &
-                !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_codon_principal") &
-                stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_hgvsp_nonprincipal")~ "hgvsp_nonprincipal",
-              !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_genomic_coord") &
-                !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_hgvsp_principal") &
-                !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_codon_principal") &
-                stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_exon_") ~ "exon",
-              !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_genomic_coord") &
-                !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_hgvsp_") &
-                !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_codon_") &
-                !stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_exon_") &
-                stringr::str_detect(.data$BIOMARKER_MATCHTYPE,"by_gene_") ~ "gene",
+            dplyr::rename(BIOMARKER_MATCH = BIOMARKER_MATCHTYPE) |>
+            dplyr::mutate(BIOMARKER_RESOLUTION = dplyr::case_when(
+              stringr::str_detect(.data$BIOMARKER_MATCH,"by_genomic_coord") ~ "genomic",
+              !stringr::str_detect(.data$BIOMARKER_MATCH,"by_genomic_coord") &
+                stringr::str_detect(.data$BIOMARKER_MATCH,"by_hgvsp_principal") ~ "hgvsp",
+              !stringr::str_detect(.data$BIOMARKER_MATCH,"by_genomic_coord") &
+                !stringr::str_detect(.data$BIOMARKER_MATCH,"by_hgvsp_principal") &
+                stringr::str_detect(.data$BIOMARKER_MATCH,"by_codon_principal") ~ "codon",
+              !stringr::str_detect(.data$BIOMARKER_MATCH,"by_genomic_coord") &
+                !stringr::str_detect(.data$BIOMARKER_MATCH,"by_hgvsp_principal") &
+                !stringr::str_detect(.data$BIOMARKER_MATCH,"by_codon_principal") &
+                stringr::str_detect(.data$BIOMARKER_MATCH,"by_hgvsp_nonprincipal")~ "hgvsp_nonprincipal",
+              !stringr::str_detect(.data$BIOMARKER_MATCH,"by_genomic_coord") &
+                !stringr::str_detect(.data$BIOMARKER_MATCH,"by_hgvsp_principal") &
+                !stringr::str_detect(.data$BIOMARKER_MATCH,"by_codon_principal") &
+                stringr::str_detect(.data$BIOMARKER_MATCH,"by_exon_") ~ "exon",
+              !stringr::str_detect(.data$BIOMARKER_MATCH,"by_genomic_coord") &
+                !stringr::str_detect(.data$BIOMARKER_MATCH,"by_hgvsp_") &
+                !stringr::str_detect(.data$BIOMARKER_MATCH,"by_codon_") &
+                !stringr::str_detect(.data$BIOMARKER_MATCH,"by_exon_") &
+                stringr::str_detect(.data$BIOMARKER_MATCH,"by_gene_") ~ "gene",
               TRUE ~ as.character('other')
             )) |>
             tidyr::separate_rows(

scripts/pcgrr.R

@@ -4,234 +4,8 @@
 
 suppressWarnings(suppressPackageStartupMessages(library(argparse)))
 suppressWarnings(suppressPackageStartupMessages(library(pcgrr)))
-suppressWarnings(suppressPackageStartupMessages(library(GenomeInfoDb)))
 suppressWarnings(suppressPackageStartupMessages(library(stringr)))
 
-##---- Argument Parsing ----##
-p <- argparse::ArgumentParser(description='PCGR HTML generation step', prog='pcgrr')
-
-# required args
-required_args <- c(
-  'output_dir', 'query_vcf2tsv', 'query_cna', 'query_rna_fusion',
-  'query_rna_expression', 'cpsr_report', 'sample_name',
-  'pcgr_version', 'pcgr_bundle_version','genome_assembly', 'data_dir')
-p$add_argument('output_dir')           #  1
-p$add_argument('query_vcf2tsv')        #  2
-p$add_argument('query_cna')            #  3
-p$add_argument('query_rna_fusion')     #  4
-p$add_argument('query_rna_expression') #  5
-p$add_argument('cpsr_report')          #  6
-p$add_argument('sample_name')          #  7
-p$add_argument('pcgr_version')         #  8
-p$add_argument('pcgr_bundle_version')  #  9
-p$add_argument('genome_assembly')      # 10
-p$add_argument('data_dir')             # 11
-# tumor props
-t_props_args <- c('tumor_purity', 'tumor_ploidy', 'tumor_type')
-p$add_argument('tumor_purity')         # 12
-p$add_argument('tumor_ploidy')         # 13
-p$add_argument('tumor_type')           # 14
-# assay props
-assay_props_args <- c('target_size_mb', 'type')
-p$add_argument('target_size_mb', type='double')     # 15
-p$add_argument('type')                              # 16
-# used to populate 'vcf_tumor_only' and 'mode' in the 'assay_props' list
-p$add_argument('vcf_tumor_only1')                   # 17
-p$add_argument('mode1')                             # 18
-# tumor only args
-tumor_only_args <- list(
-  onekg = c('maf_onekg_afr', 'maf_onekg_amr', 'maf_onekg_eas',
-            'maf_onekg_eur', 'maf_onekg_sas', 'maf_onekg_global'),
-  gnomad = c('maf_gnomad_afr','maf_gnomad_amr','maf_gnomad_asj',
-             'maf_gnomad_eas','maf_gnomad_fin','maf_gnomad_nfe',
-             'maf_gnomad_other','maf_gnomad_sas','maf_gnomad_global'),
-  filter = c('exclude_pon', 'exclude_likely_het_germline',
-             'exclude_likely_hom_germline', 'exclude_dbsnp_nonsomatic',
-             'exclude_nonexonic'))
-p$add_argument('maf_onekg_afr', type='double')      # 19
-p$add_argument('maf_onekg_amr', type='double')      # 20
-p$add_argument('maf_onekg_eas', type='double')      # 21
-p$add_argument('maf_onekg_eur', type='double')      # 22
-p$add_argument('maf_onekg_sas', type='double')      # 23
-p$add_argument('maf_onekg_global', type='double')   # 24
-p$add_argument('maf_gnomad_afr', type='double')     # 25
-p$add_argument('maf_gnomad_amr', type='double')     # 26
-p$add_argument('maf_gnomad_asj', type='double')     # 27
-p$add_argument('maf_gnomad_eas', type='double')     # 28
-p$add_argument('maf_gnomad_fin', type='double')     # 29
-p$add_argument('maf_gnomad_nfe', type='double')     # 30
-p$add_argument('maf_gnomad_other', type='double')   # 31
-p$add_argument('maf_gnomad_sas', type='double')     # 32
-p$add_argument('maf_gnomad_global', type='double')  # 33
-p$add_argument('exclude_pon', type='integer')                 # 34
-p$add_argument('exclude_likely_het_germline', type='integer') # 35
-p$add_argument('exclude_likely_hom_germline', type='integer') # 36
-p$add_argument('exclude_dbsnp_nonsomatic', type='integer')    # 37
-p$add_argument('exclude_nonexonic', type='integer')           # 38
-# tmb args
-p$add_argument('tmb_run', type='integer') # 39
-p$add_argument('tmb_algo')                # 40
-# msi/msigs args
-p$add_argument('msi_run', type='integer') # 41
-p$add_argument('msigs_run', type='integer')           # 42
-p$add_argument('msigs_mut_lim', type='double')        # 43
-p$add_argument('msigs_all_ref_sigs', type='integer')  # 44
-p$add_argument('msigs_incl_art_sigs', type='integer') # 45
-p$add_argument('msigs_prevalence_ref_sigs', type='integer') # 46
-# cna args
-cna_args <- c('log_r_homdel', 'log_r_gain', 'cna_overlap_pct')
-p$add_argument('log_r_homdel', type='double')      # 47
-p$add_argument('log_r_gain', type='double')        # 48
-p$add_argument('min_copies_ampl', type='integer')  # 49
-p$add_argument('cna_overlap_pct', type='double')   # 50
-# allelic support args
-allelic_support_args <- c(
-  'tumor_af_min', 'tumor_dp_min',
-  'control_dp_min', 'control_af_max',
-  'tumor_af_tag', 'tumor_dp_tag',
-  'control_af_tag', 'control_dp_tag',
-  'call_conf_tag')
-p$add_argument('tumor_af_min', type='double')   # 51
-p$add_argument('tumor_dp_min', type='double')   # 52
-p$add_argument('control_dp_min', type='double') # 53
-p$add_argument('control_af_max', type='double') # 54
-p$add_argument('tumor_af_tag')    # 55
-p$add_argument('tumor_dp_tag')    # 56
-p$add_argument('control_af_tag')  # 57
-p$add_argument('control_dp_tag')  # 58
-p$add_argument('call_conf_tag')   # 59
-# clinicaltrials
-p$add_argument('clinicaltrials_run', type='integer')  # 60
-# other
-p$add_argument('vep_n_forks', type='integer')         # 61
-p$add_argument('vep_buffer_size', type='integer')     # 62
-p$add_argument('vep_no_intergenic', type='integer')   # 63
-p$add_argument('vep_pick_order')                      # 64
-p$add_argument('vep_regulatory', type='integer')      # 65
-p$add_argument('vep_gencode_all', type='integer')     # 66
-p$add_argument('vcf2maf', type='integer')             # 67
-p$add_argument('list_noncoding', type='integer')      # 68
-# retained_info_tags
-p$add_argument('retained_info_tags')                 # 69
-# visual
-p$add_argument('report_theme')                        # 70
-p$add_argument('nonfloating_toc', type='integer')     # 71
-# other
-
-args <- p$parse_args()
-
-# main pcgr_config list (processed later)
-pcgr_config <- list(
-  required_args = args[required_args],
-  t_props = args[t_props_args],
-  assay_props = args[assay_props_args],
-  tumor_only = args[unlist(tumor_only_args)],
-  tmb = list(
-    run = as.logical(args[['tmb_run']]),
-    algorithm = args[['tmb_algo']]),
-  msi = list(run = as.logical(args[['msi_run']])),
-  msigs = list(
-    run = as.logical(args[['msigs_run']]),
-    mutation_limit = args[['msigs_mut_lim']],
-    all_reference_signatures = as.logical(args[['msigs_all_ref_sigs']]),
-    include_artefact_signatures = as.logical(args[['msigs_incl_art_sigs']]),
-    prevalence_reference_signatures = as.integer(args[['msigs_prevalence_ref_sigs']])
-  ),
-  cna = args[cna_args],
-  allelic_support = args[allelic_support_args],
-  clinicaltrials = list(run = as.logical(args[['clinicaltrials_run']])),
-  vep = list(vep_n_forks = args[['vep_n_forks']],
-               vep_buffer_size = args[['vep_buffer_size']],
-               vep_no_intergenic = as.logical(args[['vep_no_intergenic']]),
-               vep_pick_order = args[['vep_pick_order']],
-               vep_regulatory = as.logical(args[['vep_regulatory']]),
-               vep_gencode_all = as.logical(args[['vep_gencode_all']])),
-  other = list(vcf2maf = as.logical(args[['vcf2maf']]),
-               list_noncoding = as.logical(args[['list_noncoding']]),
-               vcf_no_validation = as.logical(args[['vcf_no_validation']])
-  ),
-  retained_info_tags = args[['retained_info_tags']],
-  visual = list(report_theme = args[['report_theme']],
-                nonfloating_toc = as.logical(args[['nonfloating_toc']])
-  )
-)
-
-##---- Argument Processing ----##
-# required args
-if (pcgr_config[['required_args']][['query_cna']] == "None"){
-  pcgr_config[['required_args']][['query_cna']] <- NULL
-}
-if (pcgr_config[['required_args']][['query_rna_fusion']] == "None"){
-  pcgr_config[['required_args']][['query_rna_fusion']] <- NULL
-}
-if (pcgr_config[['required_args']][['query_rna_expression']] == "None"){
-  pcgr_config[['required_args']][['query_rna_expression']] <- NULL
-}
-if (pcgr_config[['required_args']][['cpsr_report']] == "None"){
-  pcgr_config[['required_args']][['cpsr_report']] <- NULL
-}
-
-# tumor props
-# Handle case when 'NA'
-purity <- pcgr_config[['t_props']][['tumor_purity']]
-ploidy <- pcgr_config[['t_props']][['tumor_ploidy']]
-pcgr_config[['t_props']][['tumor_purity']] <-
-  ifelse(purity == 'NA', NA_real_, as.numeric(purity))
-pcgr_config[['t_props']][['tumor_ploidy']] <-
-  ifelse(ploidy == 'NA', NA_real_, as.numeric(ploidy))
-pcgr_config[['t_props']][['tumor_type']] <- stringr::str_replace_all(
-  stringr::str_replace_all(
-    args[['tumor_type']], "_", " "),
-  "@", "/")
-if (pcgr_config[['t_props']][['tumor_type']] == "Any") {
-  pcgr_config[['t_props']][['tumor_type']] <- "Cancer, NOS"
-}
-
-### Sequencing assay properties (VCF)
-### Target (WES/WGS/TARGETED), mode (tumor-control/tumor-only), coding target size
-pcgr_config[['assay_props']][['mode']] <- 'Tumor-Control'
-pcgr_config[['assay_props']][['vcf_tumor_only']] <- FALSE
-
-if (as.integer(args[['vcf_tumor_only1']]) == 1) {
-  pcgr_config[['assay_props']][['vcf_tumor_only']] <- TRUE
-  pcgr_config[['assay_props']][['mode']] <- 'Tumor-Only'
-  if (as.integer(args[['mode1']]) == 1){
-    pcgr_config[['assay_props']][['mode']] <- 'Cell line (Tumor-Only)'
-  }
-}
-
-# tumor_only maf filter
-for (mf in tumor_only_args[['filter']]) {
-  pcgr_config[['tumor_only']][[mf]] <- as.logical(args[[mf]])
-}
-
-pcgr_config_rds_fname <- file.path(
-  pcgr_config[['required_args']][['output_dir']],
-  paste0(pcgr_config[['required_args']][['sample_name']],
-         ".pcgr_config.rds"))
-pcgr_config_json_fname <- file.path(
-  pcgr_config[['required_args']][['output_dir']],
-  paste0(pcgr_config[['required_args']][['sample_name']],
-         ".pcgr_config.json"))
-pcgr_config_json <- jsonlite::toJSON(pcgr_config)
-saveRDS(pcgr_config, file = pcgr_config_rds_fname)
-jsonlite::write_json(pcgr_config_json, path = pcgr_config_json_fname)
-
-### Arg processing END
-
-#pcgr_data <- pcgrr::load_reference_data(
-#  pcgr_db_dir = pcgr_config[['required_args']][['data_dir']],
-#  genome_assembly = pcgr_config[['required_args']][['genome_assembly']])
-
-
-
-# if (pcgr_config[['other']][['vep_regulatory']] == F){
-#   for (e in c('tier4_display','tier5_display','all','tsv')){
-#     pcgr_data[['annotation_tags']][[e]] <-
-#       pcgr_data[['annotation_tags']][[e]][
-#         pcgr_data[['annotation_tags']][[e]] != "REGULATORY_ANNOTATION"]
-#   }
-# }
 
 # my_log4r_layout <- function(level, ...) {
 #   paste0(format(Sys.time()), " - pcgr-report-generation - ",
@@ -244,13 +18,6 @@ jsonlite::write_json(pcgr_config_json, path = pcgr_config_json_fname)
 # # this gets passed on to all the log4r_* functions inside the pkg
 # options("PCGRR_LOG4R_LOGGER" = log4r_logger)
 
-# ## Clinical trials
-# if (pcgr_config[['t_props']][['tumor_type']] == "Cancer, NOS"){
-#   pcgrr:::log4r_info(paste0("Clinical trials will not be included in the report when primary site is not specified - skipping"))
-#   pcgr_config[['clinicaltrials']][['run']] <- F
-# }
-
-# pcgrr:::log4r_info(paste0("Tumor primary site: ", pcgr_config[['t_props']][['tumor_type']]))
 
 # pcg_report <- NULL