comparison MT2MQ.R @ 2:9c8e7137d331 draft

"planemo upload commit 59afcdaf7afdf574c475f0faae73127f0e563328"

1:e50ec3a9a3f9

# MT2MQ: prepares metatranscriptomic outputs from ASaiM (HUMAnN2 and metaphlan) for metaquantome

# Load libraries
suppressPackageStartupMessages(library(tidyverse))
#default_locale()

# Set parameters from arguments
args = commandArgs(trailingOnly = TRUE)
data <- args[1]
  # data: full path to file or directory:
  #   - if in functional or f-t mode, should be a tsv file of HUMAnN2 gene families, after regrouping and renaming to GO, joining samples, and renormalizing to CPM.
  #   - if in taxonomic mode, should be a directory of tsv files of metaphlan genus-level results
mode <- args[2]

  #   -"t": taxonomy
  #   -"ft": function-taxonomy
ontology <- unlist(strsplit(args[3], split = ","))
  # ontology: only for function or f-t mode. A string of the GO namespace(s) to include, separated by commas.
  #   ex: to include all: "molecular_function,biological_process,cellular_component"
outfile <- args[4]
  # outfile: full path with pathname and extension for output

# Functional mode
if (mode == "f"){
  out <- read.delim(file=data, header=TRUE, sep='\t') %>% 
    filter(!grepl(".+g__.+",X..Gene.Family)) %>% 
    separate(col=X..Gene.Family, into=c("id", "Extra"), sep=": ", fill="left") %>% 
    separate(col=Extra, into = c("namespace", "name"), sep = " ", fill="left", extra="merge") %>% 
    mutate(namespace = if_else(namespace == "[MF]", true = "molecular_function", false = if_else(namespace == "[BP]", true = "biological_process", false = "cellular_component"))) %>% 
    filter(namespace %in% ontology) %>% 
    select(id, name, namespace, 4:ncol(.))
}

# Taxonomic mode
if (mode == "t"){
  files <- dir(path = data)
  out <- tibble(filename = files) %>% 
    mutate(file_contents= map(filename, ~read.delim(file=file.path(data, .), header=TRUE, sep = "\t"))) %>% 
    unnest(cols = c(file_contents)) %>% 
    rename(sample = filename) %>% 
    separate(col = sample, into = c("sample",NA), sep=".tsv") %>% 
    pivot_wider(names_from = sample, values_from = abundance) %>% 
    mutate(rank = "genus") %>% 
    rename(name = genus) %>% 
    mutate(id = row_number(name)) %>% # filler for taxon id but should eventually find a way to get id from ncbi database
    select(id, name, rank, 2:ncol(.))
}

# Function-taxonomy mode
if (mode == "ft"){
  out <- read.delim(file=data, header=TRUE, sep='\t') %>% 
    filter(grepl(".+g__.+",X..Gene.Family)) %>% 
    separate(col=X..Gene.Family, into=c("id", "Extra"), sep=": ", fill="left") %>% 
    separate(col=Extra, into = c("namespace", "name"), sep = " ", fill="left", extra="merge") %>% 
    separate(col = name, into = c("name", "taxa"), sep="\\|", extra = "merge") %>%
    separate(col = taxa, into = c("Extra", "genus", "species"), sep = "__") %>% select(-"Extra") %>%
    mutate_if(is.character, str_replace_all, pattern = "\\.s", replacement = "") %>% 
    mutate_at(c("species"), str_replace_all, pattern = "_", replacement = " ") %>% 
    mutate(namespace = if_else(namespace == "[MF]", true = "molecular_function", false = if_else(namespace == "[BP]", true = "biological_process", false = "cellular_component"))) %>% 
    filter(namespace %in% ontology) %>% 
    select(id, name, namespace, 4:ncol(.))
}

# Write file
write.table(x = out, file = outfile, quote = FALSE, sep = "\t", row.names = FALSE)

2:9c8e7137d331

# MT2MQ: prepares metatranscriptomic outputs from ASaiM (HUMAnN2 and metaphlan) for metaquantome

# Load libraries
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(taxize))

# Set parameters from arguments
args <- commandArgs(trailingOnly = TRUE)
data <- args[1]
  # data: full path to file or directory:
  #   - if in functional or f-t mode, should be a tsv file of HUMAnN2 gene families, after regrouping and renaming to GO, joining samples, and renormalizing to CPM.
  #   - if in taxonomic mode, should be a directory of tsv files of metaphlan genus-level results
mode <- args[2]

  #   -"t": taxonomy
  #   -"ft": function-taxonomy
ontology <- unlist(strsplit(args[3], split = ","))
  # ontology: only for function or f-t mode. A string of the GO namespace(s) to include, separated by commas.
  #   ex: to include all: "molecular_function,biological_process,cellular_component"

int_file <- args[4]
  # int_file: full path and file name and extension to write intensity file

func_file <- args[5]
  # func_file: full path and file name and extension to write func file

tax_file <- args[6]
  # tax_file: full path and file name and extension to write tax file


# Functional mode
if (mode == "f") {
  int <- read.delim(file = data, header = TRUE, sep = "\t") %>%
    filter(!grepl(".+g__.+", X..Gene.Family)) %>%
    separate(col = X..Gene.Family, into = c("id", "Extra"), sep = ": ", fill = "left") %>%
    separate(col = Extra, into = c("namespace", "name"), sep = " ", fill = "left", extra = "merge") %>%
    mutate(namespace = if_else(namespace == "[MF]", true = "molecular_function", false = if_else(namespace == "[BP]", true = "biological_process", false = "cellular_component"))) %>%
    filter(namespace %in% ontology) %>%
    select(id, name, namespace, 4:ncol(.))
  func <- int %>%
    select(id) %>%
    mutate(gos = id)
  write.table(x = int, file = int_file, quote = FALSE, sep = "\t", row.names = FALSE)
  write.table(x = func, file = func_file, quote = FALSE, sep = "\t", row.names = FALSE)
}

# Taxonomic mode
if (mode == "t") {
  files <- dir(path = data)
  int <- tibble(filename = files) %>%
    mutate(file_contents = map(filename, ~read.delim(file = file.path(data, .), header = TRUE, sep = "\t"))) %>%
    unnest(cols = c(file_contents)) %>%
    rename(sample = filename) %>%
    separate(col = sample, into = c("sample", NA), sep = ".tsv") %>%
    pivot_wider(names_from = sample, values_from = abundance) %>%
    mutate(rank = "genus") %>%
    rename(name = genus) %>%
    mutate(name = as.character(name)) %>%
    mutate(id = get_uid(name, key = NULL, messages = FALSE)) %>%
    select(id, name, rank, 2:ncol(.))
  tax <- int %>%
    select(id) %>%
    mutate(tax = id)
  write.table(x = int, file = int_file, quote = FALSE, sep = "\t", row.names = FALSE)
  write.table(x = tax, file = tax_file, quote = FALSE, sep = "\t", row.names = FALSE)
}

# Function-taxonomy mode
if (mode == "ft") {
  ft <- read.delim(file = data, header = TRUE, sep = "\t") %>%
    filter(grepl(".+g__.+", X..Gene.Family)) %>%
    separate(col = X..Gene.Family, into = c("id", "Extra"), sep = ": ", fill = "left") %>%
    separate(col = Extra, into = c("namespace", "name"), sep = " ", fill = "left", extra = "merge") %>%
    separate(col = name, into = c("name", "taxa"), sep = "\\|", extra = "merge") %>%
    separate(col = taxa, into = c("Extra", "genus", "species"), sep = "__") %>%
    select(-"Extra") %>%
    mutate_if(is.character, str_replace_all, pattern = "\\.s", replacement = "") %>%
    mutate_at(c("species"), str_replace_all, pattern = "_", replacement = " ") %>%
    mutate(namespace = if_else(namespace == "[MF]", true = "molecular_function", false = if_else(namespace == "[BP]", true = "biological_process", false = "cellular_component"))) %>%
    filter(namespace %in% ontology) %>%
    select(id, name, namespace, 4:ncol(.))
  write.table(x = ft, file = int_file, quote = FALSE, sep = "\t", row.names = FALSE)
}