| Previous changeset 7:12bf74dd09f1 (2023-10-11) Next changeset 9:ffbabefbd2e2 (2024-08-01) |
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Commit message:
planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/recetox_aplcms commit bc3445f7c41271b0062c7674108f57708d08dd28 |
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modified:
help.xml utils.R |
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| diff -r 12bf74dd09f1 -r b5a1f35abd8a help.xml --- a/help.xml Wed Oct 11 11:19:22 2023 +0000 +++ b/help.xml Thu May 30 14:53:21 2024 +0000 |
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| @@ -49,7 +49,7 @@ tolerance levels are estimated from the data. A run-filter is used to detect peaks and remove noise. Non-parametric statistical methods are used to find-tune peak selection and grouping. After retention time correction, a feature table is generated by aligning peaks across spectra. For further information on apLCMS -please refer to https://mypage.cuhk.edu.cn/academics/yutianwei/apLCMS/. +please refer to the official tutorial. </token> <token name="@REMOVE_NOISE_HELP@"> |
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| diff -r 12bf74dd09f1 -r b5a1f35abd8a utils.R --- a/utils.R Wed Oct 11 11:19:22 2023 +0000 +++ b/utils.R Thu May 30 14:53:21 2024 +0000 |
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| @@ -1,139 +1,139 @@ 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) + 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) + attr(df, "sample_name") <- sample_name + return(df) } restore_sample_name <- function(df) { - return(df$sample_id[1]) + return(df$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) - } + 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) + arrow::write_parquet(data, filename) } load_data_from_parquet_file <- function(filename) { - return(arrow::read_parquet(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) + 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) - } + 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)]) + 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) + 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$intensity, intensity_file) + save_data_as_parquet_file(aligned_features$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 = ", ") - )) - } + 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$adjusted_features) + return(recovered_features$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)" - ) + 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$known_table[columns], filename) + columns <- known_table_columns() + arrow::write_parquet(table$known_table[columns], filename) } read_known_table <- function(filename) { - arrow::read_parquet(filename, col_select = known_table_columns()) + arrow::read_parquet(filename, col_select = known_table_columns()) } save_pairing <- function(table, filename) { - df <- table$pairing %>% - as_tibble() %>% - setNames(c("new", "old")) - arrow::write_parquet(df, filename) + df <- table$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$rt <- rt_table - return(features) + features <- new("list") + features$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 = ", ") - )) - } + 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)) + 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)) } |