annotate mqppep_anova.R @ 0:8dfd5d2b5903 draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/mqppep commit 3a7b3609d6e514c9e8f980ecb684960c6b2252fe

#!/usr/bin/env Rscript
# libraries
library(optparse)
library(data.table)
library(stringr)

# ref for parameterizing Rmd document: https://stackoverflow.com/a/37940285

# parse options
option_list <- list(
  make_option(
    c("-i", "--inputFile"),
    action = "store",
    default = NA,
    type = "character",
    help = "Phosphopeptide Intensities sparse input file path"
  ),
  make_option(
    c("-a", "--alphaFile"),
    action = "store",
    default = NA,
    type = "character",
    help = paste0("List of alpha cutoff values for significance testing;",
             " path to text file having one column and no header")
  ),
  make_option(
    c("-S", "--preproc_sqlite"),
    action = "store",
    default = NA,
    type = "character",
    help = "Path to 'preproc_sqlite' produced by `mqppep_mrgfltr.py`"
  ),
  make_option(
    c("-K", "--ksea_sqlite"),
    action = "store",
    default = NA,
    type = "character",
    help = "Path to 'ksea_sqlite' output produced by this tool"
  ),
  make_option(
    c("-f", "--firstDataColumn"),
    action = "store",
    default = "^Intensity[^_]",
    type = "character",
    help = "First column of intensity values"
  ),
  make_option(
    c("-m", "--imputationMethod"),
    action = "store",
    default = "random",
    type = "character",
    help = paste0("Method for missing-value imputation,",
             " one of c('group-median','median','mean','random')")
  ),
  make_option(
    c("-p", "--meanPercentile"),
    action = "store",
    default = 3,
    type = "integer",
    help = paste0("Mean percentile for randomly generated imputed values;",
              ", range [1,99]")
  ),
  make_option(
    c("-d", "--sdPercentile"),
    action = "store",
    default = 3,
    type = "double",
    help = paste0("Adjustment value for standard deviation of",
              " randomly generated imputed values; real")
  ),
  make_option(
    c("-s", "--regexSampleNames"),
    action = "store",
    default = "\\.(\\d+)[A-Z]$",
    type = "character",
    help = "Regular expression extracting sample-names"
  ),
  make_option(
    c("-g", "--regexSampleGrouping"),
    action = "store",
    default = "(\\d+)",
    type = "character",
    help = paste0("Regular expression extracting sample-group",
             " from an extracted sample-name")
  ),
  make_option(
    c("-o", "--imputedDataFile"),
    action = "store",
    default = "output_imputed.tsv",
    type = "character",
    help = "Imputed Phosphopeptide Intensities output file path"
  ),
  make_option(
    c("-n", "--imputedQNLTDataFile"),
    action = "store",
    default = "output_imp_qn_lt.tsv",
    type = "character",
    help =
      paste(
        "Imputed, Quantile-Normalized Log-Transformed Phosphopeptide",
        "Intensities output file path"
        )
  ),
  make_option(
    c("-r", "--reportFile"),
    action = "store",
    default = "QuantDataProcessingScript.html",
    type = "character",
    help = "HTML report file path"
  ),
  make_option(
    c("-k", "--ksea_cutoff_statistic"),
    action = "store",
    default = "FDR",
    type = "character",
    help = paste0("Method for missing-value imputation,",
             " one of c('FDR','p.value'), but don't expect 'p.value' to work well.")
  ),
  make_option(
    c("-t", "--ksea_cutoff_threshold"),
    action = "store",
    default = 0.05,
    type = "double",
    help = paste0("Maximum score to be used to score a kinase enrichment as significant")
  ),
  make_option(
    c("-M", "--anova_ksea_metadata"),
    action = "store",
    default = "anova_ksea_metadata.tsv",
    type = "character",
    help = "Phosphopeptide metadata, ANOVA FDR, and KSEA enribhments"
  )
)
args <- parse_args(OptionParser(option_list = option_list))
print("args is:")
cat(str(args))

# Check parameter values

if (! file.exists(args$inputFile)) {
  stop((paste("Input file", args$inputFile, "does not exist")))
}
input_file             <- args$inputFile
alpha_file             <- args$alphaFile
preproc_sqlite         <- args$preproc_sqlite
imputed_data_file_name <- args$imputedDataFile
imp_qn_lt_data_filenm  <- args$imputedQNLTDataFile
anova_ksea_metadata    <- args$anova_ksea_metadata
report_file_name       <- args$reportFile
ksea_sqlite            <- args$ksea_sqlite
ksea_cutoff_statistic  <- args$ksea_cutoff_statistic
ksea_cutoff_threshold  <- args$ksea_cutoff_threshold
if (
  sum(
    grepl(
      pattern = ksea_cutoff_statistic,
      x = c("FDR", "p.value")
      )
    ) < 1
  ) {
    print(sprintf("bad ksea_cutoff_statistic argument: %s", ksea_cutoff_statistic))
    return(-1)
    }

imputation_method <- args$imputationMethod
if (
  sum(
    grepl(
      pattern = imputation_method,
      x = c("group-median", "median", "mean", "random")
      )
    ) < 1
  ) {
    print(sprintf("bad imputationMethod argument: %s", imputation_method))
    return(-1)
    }

# read with default values, when applicable
mean_percentile <- args$meanPercentile
sd_percentile   <- args$sdPercentile
# in the case of 'random" these values are ignored by the client script
if (imputation_method == "random") {
  print("mean_percentile is:")
  cat(str(mean_percentile))

  print("sd_percentile is:")
  cat(str(mean_percentile))
}

# convert string parameters that are passed in via config files:
#  - firstDataColumn
#  - regexSampleNames
#  - regexSampleGrouping
read_config_file_string <- function(fname, limit) {
  # eliminate any leading whitespace
  result    <- gsub("^[ \t\n]*", "", readChar(fname, limit))
  # eliminate any trailing whitespace
  result    <- gsub("[ \t\n]*$", "", result)
  # substitute characters escaped by Galaxy sanitizer
  result <- gsub("__lt__", "<",  result)
  result <- gsub("__le__", "<=", result)
  result <- gsub("__eq__", "==", result)
  result <- gsub("__ne__", "!=", result)
  result <- gsub("__gt__", ">",  result)
  result <- gsub("__ge__", ">=", result)
  result <- gsub("__sq__", "'",  result)
  result <- gsub("__dq__", '"',  result)
  result <- gsub("__ob__", "[",  result)
  result <- gsub("__cb__", "]",  result)
}
cat(paste0("first_data_column file: ", args$firstDataColumn, "\n"))
cat(paste0("regex_sample_names file: ", args$regexSampleNames, "\n"))
cat(paste0("regex_sample_grouping file: ", args$regexSampleGrouping, "\n"))
nc <- 1000
regex_sample_names <- read_config_file_string(args$regexSampleNames, nc)
regex_sample_grouping <- read_config_file_string(args$regexSampleGrouping, nc)
first_data_column <- read_config_file_string(args$firstDataColumn,  nc)
cat(paste0("first_data_column: ",     first_data_column,     "\n"))
cat(paste0("regex_sample_names: ",    regex_sample_names,    "\n"))
cat(paste0("regex_sample_grouping: ", regex_sample_grouping, "\n"))

# from: https://github.com/molgenis/molgenis-pipelines/wiki/
#   How-to-source-another_file.R-from-within-your-R-script
# Function location_of_this_script returns the location of this .R script
#   (may be needed to source other files in same dir)
location_of_this_script <- function() {
    this_file <- NULL
    # This file may be 'sourced'
    for (i in - (1:sys.nframe())) {
        if (identical(sys.function(i), base::source)) {
            this_file <- (normalizePath(sys.frame(i)$ofile))
        }
    }

    if (!is.null(this_file)) return(dirname(this_file))

    # But it may also be called from the command line
    cmd_args <- commandArgs(trailingOnly = FALSE)
    cmd_args_trailing <- commandArgs(trailingOnly = TRUE)
    cmd_args <- cmd_args[
      seq.int(
        from = 1,
        length.out = length(cmd_args) - length(cmd_args_trailing)
        )
      ]
    res <- gsub("^(?:--file=(.*)|.*)$", "\\1", cmd_args)

    # If multiple --file arguments are given, R uses the last one
    res <- tail(res[res != ""], 1)
    if (0 < length(res)) return(dirname(res))

    # Both are not the case. Maybe we are in an R GUI?
    return(NULL)
}

script_dir <-  location_of_this_script()

rmarkdown_params <- list(
    inputFile = input_file
  , alphaFile = alpha_file
  , preprocDb = preproc_sqlite
  , firstDataColumn = first_data_column
  , imputationMethod = imputation_method
  , meanPercentile = mean_percentile
  , sdPercentile = sd_percentile
  , regexSampleNames = regex_sample_names
  , regexSampleGrouping = regex_sample_grouping
  , imputedDataFilename = imputed_data_file_name
  , imputedQNLTDataFile = imp_qn_lt_data_filenm
  , anovaKseaMetadata = anova_ksea_metadata
  , kseaAppPrepDb = ksea_sqlite
  , kseaCutoffThreshold = ksea_cutoff_threshold
  , kseaCutoffStatistic = ksea_cutoff_statistic
  )

print("rmarkdown_params")
str(rmarkdown_params)

# freeze the random number generator so the same results will be produced
#  from run to run
set.seed(28571)

# BUG (or "opportunity")
# To render as PDF for the time being requires installing the conda
# package `r-texlive` until this issue in `texlive-core` is resolved:
#   https://github.com/conda-forge/texlive-core-feedstock/issues/19
# This workaround is detailed in the fourth comment of:
#   https://github.com/conda-forge/texlive-core-feedstock/issues/61

library(tinytex)
tinytex::install_tinytex()
rmarkdown::render(
  input = paste(script_dir, "mqppep_anova_script.Rmd", sep = "/")
, output_format = rmarkdown::pdf_document(toc = TRUE)
, output_file = report_file_name
, params = rmarkdown_params
)