annotate utils.R @ 6:149b9cc7499b draft

planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/recetox_aplcms commit 0873c036d734782d2647b0eea84cd1a0658f5e58

library(recetox.aplcms)

get_env_sample_name <- function() {
    sample_name <- Sys.getenv("SAMPLE_NAME", unset = NA)
    if (nchar(sample_name) == 0) {
        sample_name <- NA
    }
    if (is.na(sample_name)) {
        message("The mzML file does not contain run ID.")
    }
    return(sample_name)
}

save_sample_name <- function(df, sample_name) {
    attr(df, "sample_name") <- sample_name
    return(df)
}

restore_sample_name <- function(df) {
    return(df$sample_id[1])
}

load_sample_name <- function(df) {
    sample_name <- attr(df, "sample_name")
    if (is.null(sample_name)) {
        return(NA)
    } else {
        return(sample_name)
    }
}

save_data_as_parquet_file <- function(data, filename) {
    arrow::write_parquet(data, filename)
}

load_data_from_parquet_file <- function(filename) {
    return(arrow::read_parquet(filename))
}

load_parquet_collection <- function(files) {
    features <- lapply(files, arrow::read_parquet)
    features <- lapply(features, tibble::as_tibble)
    return(features)
}

save_parquet_collection <- function(feature_tables, sample_names, subdir) {
    dir.create(subdir)
    for (i in seq_len(length(feature_tables))) {
      filename <- file.path(subdir, paste0(sample_names[i], ".parquet"))
      feature_table <- as.data.frame(feature_tables[[i]])
      feature_table <- save_sample_name(feature_table, sample_names[i])
      arrow::write_parquet(feature_table, filename)
    }
}

sort_by_sample_name <- function(tables, sample_names) {
    return(tables[order(sample_names)])
}

save_tolerances <- function(table, tol_file) {
    mz_tolerance <- c(table$mz_tol_relative)
    rt_tolerance <- c(table$rt_tol_relative)
    arrow::write_parquet(data.frame(mz_tolerance, rt_tolerance), tol_file)
}

save_aligned_features <- function(aligned_features, metadata_file, rt_file, intensity_file) {
    save_data_as_parquet_file(aligned_features$metadata, metadata_file)
    save_data_as_parquet_file(aligned_features$rt, rt_file)
    save_data_as_parquet_file(aligned_features$intensity, intensity_file)
}

select_table_with_sample_name <- function(tables, sample_name) {
    sample_names <- lapply(tables, load_sample_name)
    index <- which(sample_names == sample_name)
    if (length(index) > 0) {
        return(tables[[index]])
    } else {
        stop(sprintf("Mismatch - sample name '%s' not present in %s",
                     sample_name, paste(sample_names, collapse = ", ")))
    }
}

select_adjusted <- function(recovered_features) {
    return(recovered_features$adjusted_features)
}

known_table_columns <- function() {
  c("chemical_formula", "HMDB_ID", "KEGG_compound_ID", "mass", "ion.type",
    "m.z", "Number_profiles_processed", "Percent_found", "mz_min", "mz_max",
    "RT_mean", "RT_sd", "RT_min", "RT_max", "int_mean(log)", "int_sd(log)",
    "int_min(log)", "int_max(log)")
}

save_known_table <- function(table, filename) {
  columns <- known_table_columns()
  arrow::write_parquet(table$known_table[columns], filename)
}

read_known_table <- function(filename) {
  arrow::read_parquet(filename, col_select = known_table_columns())
}

save_pairing <- function(table, filename) {
  df <- table$pairing %>% as_tibble() %>% setNames(c("new", "old"))
  arrow::write_parquet(df, filename)
}

join_tables_to_list <- function(metadata, rt_table, intensity_table) {
  features <- new("list")
  features$metadata <- metadata
  features$intensity <- intensity_table
  features$rt <- rt_table
  return(features)
}

validate_sample_names <- function(sample_names) {
    if ((any(is.na(sample_names))) || (length(unique(sample_names)) != length(sample_names))) {
        stop(sprintf("Sample names absent or not unique - provided sample names: %s",
                     paste(sample_names, collapse = ", ")))
    }
}

determine_sigma_ratios <- function(sigma_ratio_lim_min = NA, sigma_ratio_lim_max = NA) {
    if (is.na(sigma_ratio_lim_min)) {
        sigma_ratio_lim_min <- 0
    }
    if (is.na(sigma_ratio_lim_max)) {
        sigma_ratio_lim_max <- Inf
    }
    return(c(sigma_ratio_lim_min, sigma_ratio_lim_max))
}