comparison mykrobe_parser.R @ 2:f2608dccd3e0 draft

planemo upload for repository https://github.com/phac-nml/mykrobe-parser commit 1d77b6cae26ef3456ff6d469d71c61cab6a19906-dirty

1:05ca0dbc9f46

# Take the JSON output from Mykrobe, rearrange, output for LIMS
# Adrian Zetner
# August 2018

# Libraries ####
library(jsonlite, quietly = T)
library(here, quietly = T)
suppressMessages(library(dplyr, quietly = T))
suppressMessages(library(purrr, quietly = T))
library(tidyr, quietly = T)
library(stringr, quietly = T)
library(optparse, quietly = T)

# Define custom functions, variables, and paths. Collect and use CL arguments ####

# Here's a function to recreate that output table from the input JSON files

  
  # Take that list and mash all the elements together as columns in a tibble, recycling as needed to fill in space
  # eg. phylo_group is repeated/recycled as many times as there are drugs tested
  as_tibble(temp)
}

sink(stdout(), type = "message")

suppressPackageStartupMessages({
  library(jsonlite)
  library(here)
  library(dplyr)
  library(purrr)
  library(tidyr)
  library(stringr)
  library(optparse)
})

# Get command line arguments with optparse
option_list = list(
  make_option(c("-f", "--file"), 
              type="character", 

if (length(predictions.table) == 1){
  print(predictions.table)
  stop("No susceptibility results in files specified. Did the testing fail?", call.=FALSE)
}

# Variants
# Multiple resistance mutations and confidence per drug in the X_R_mutations column
# Actual protein changes in Mykrobe_X columns

variants.temp <- 
  temp %>% 
  select(file, drug, variants = `variants (gene:alt_depth:wt_depth:conf)`) %>% 
  mutate(variants = replace(variants, variants == "", NA)) %>% # Make missing data consistent...
  filter(!is.na(variants)) %>% # ...Then get rid of it
  mutate(tempcols = paste(drug, "R_mutations", sep = "_")) %>% 
  mutate(R_mutations = variants) %>% 
  mutate(variants = strsplit(variants, "__")) %>% # Split the mutations across rows (list first then split across rows)
  unnest(variants) %>% 
  separate(variants, c("gene", "mutation"), "_") %>% 
  mutate(columnname = ifelse(gene %in% c("tlyA", "rrs", "gid"), # Check for columns that include the drug name or not and paste accordingly
                             paste("Mykrobe", drug, gene, sep = "_"),
                             paste("Mykrobe", gene, sep = "_"))) %>% 
  # Extract out the mutation information with a regex that covers all potential genes
  # This regex looks for whatever is ahead of the first colon and after the last hyphen
  mutate(mutation = str_match(mutation, "(.*)-.*:")[,2]) %>%
  select(file, tempcols, R_mutations, columnname, mutation)

# Split each kind of variants into its own temp table then merge
variants.1 <- 
  variants.temp %>% 
  select(file, tempcols, R_mutations) %>% 
  distinct() %>% 
  spread(tempcols, R_mutations)

variants.2 <- 
  variants.temp %>% 
  select(file, columnname, mutation) %>% 
  group_by(file, columnname) %>% 
  summarise(mutation = paste(mutation, collapse = ";")) %>% 
  spread(columnname, mutation)

variants.table <- full_join(variants.1, variants.2, by = "file")

# Make a report ####

report <- 
  temp %>% 

         phylo_group_depth, 
         species_depth, 
         lineage_depth) %>%
  distinct() %>%
  write.csv("output-jsondata.csv", row.names = F)
print("Writing JSON data to CSV as output-jsondata.txt")
sink(NULL, type="message") # close the sink

quit()

2:f2608dccd3e0

# Take the JSON output from Mykrobe, rearrange, output for LIMS
# Adrian Zetner
# August 2018

# Libraries ####

sink(stdout(), type = "message")

suppressPackageStartupMessages({
  library(jsonlite)
  library(here)
  library(dplyr)
  library(purrr)
  library(tidyr)
  library(stringr)
  library(optparse)
})

# Define custom functions, variables, and paths. Collect and use CL arguments ####

# Here's a function to recreate that output table from the input JSON files

  
  # Take that list and mash all the elements together as columns in a tibble, recycling as needed to fill in space
  # eg. phylo_group is repeated/recycled as many times as there are drugs tested
  as_tibble(temp)
}

# Get command line arguments with optparse
option_list = list(
  make_option(c("-f", "--file"), 
              type="character", 

if (length(predictions.table) == 1){
  print(predictions.table)
  stop("No susceptibility results in files specified. Did the testing fail?", call.=FALSE)
}

# Variants, if present
if (0 < predictions.table %>% 
    select(ends_with("_Prediction")) %>% 
    unlist(use.names = F) %>% 
    str_count("[R,r]") %>% 
    sum()){

      # Multiple resistance mutations and confidence per drug in the X_R_mutations column
      # Actual protein changes in Mykrobe_X columns
      
      variants.temp <- 
        temp %>% 
        select(file, drug, variants = `variants (gene:alt_depth:wt_depth:conf)`) %>% 
        mutate(variants = replace(variants, variants == "", NA)) %>% # Make missing data consistent...
        filter(!is.na(variants)) %>% # ...Then get rid of it
        mutate(tempcols = paste(drug, "R_mutations", sep = "_")) %>% 
        mutate(R_mutations = variants) %>% 
        mutate(variants = strsplit(variants, "__")) %>% # Split the mutations across rows (list first then split across rows)
        unnest(variants) %>% 
        separate(variants, c("gene", "mutation"), "_") %>% 
        mutate(columnname = ifelse(gene %in% c("tlyA", "rrs", "gid"), # Check for columns that include the drug name or not and paste accordingly
                                   paste("Mykrobe", drug, gene, sep = "_"),
                                   paste("Mykrobe", gene, sep = "_"))) %>% 
        # Extract out the mutation information with a regex that covers all potential genes
        # This regex looks for whatever is ahead of the first colon and after the last hyphen
        mutate(mutation = str_match(mutation, "(.*)-.*:")[,2]) %>%
        select(file, tempcols, R_mutations, columnname, mutation)
      
      # Split each kind of variants into its own temp table then merge
      variants.1 <- 
        variants.temp %>% 
        select(file, tempcols, R_mutations) %>% 
        distinct() %>% 
        spread(tempcols, R_mutations)
      
      variants.2 <- 
        variants.temp %>% 
        select(file, columnname, mutation) %>% 
        group_by(file, columnname) %>% 
        summarise(mutation = paste(mutation, collapse = ";")) %>% 
        spread(columnname, mutation)
      
      variants.table <- full_join(variants.1, variants.2, by = "file")
}else{
  variants.table <- data.frame(file=predictions.table$file, stringsAsFactors = F)
}


# Make a report ####

report <- 
  temp %>% 

         phylo_group_depth, 
         species_depth, 
         lineage_depth) %>%
  distinct() %>%
  write.csv("output-jsondata.csv", row.names = F)
print("Writing JSON data to CSV as output-jsondata.csv")
sink(NULL, type="message") # close the sink

quit()