Mercurial > repos > lain > xseekerpreparator
view galaxy/tools/LC-MSMS/XSeekerPreparator.R @ 13:26f01380145d draft
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| author | lain |
|---|---|
| date | Wed, 07 Apr 2021 13:28:09 +0000 |
| parents | bdb2878ee189 |
| children | 00079fadc240 |
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TOOL_NAME <- "XSeekerPreparator" VERSION <- "1.2.1" OUTPUT_SPECIFIC_TOOL <- "XSeeker_Galaxy" ENRICHED_RDATA_VERSION <- paste("1.1.2", OUTPUT_SPECIFIC_TOOL, sep="-") ENRICHED_RDATA_DOC <- sprintf(" Welcome to the enriched <Version %s> of the output of CAMERA/xcms. This doc was generated by the tool: %s - Version %s To show the different variables contained in this rdata, type: - `load('this_rdata.rdata', rdata_env <- new.env())` - `names(rdata_env)` Sections ###### This tools helpers ------ The version number is somewhat special because the evolution of the rdata's format is non-linear. There may be different branches, each evolving separatly. To reflect these branches's diversions, there may be a prepended branch name following this format: major.minor.patch-branch_name Like this, we can process rdata with the same tool, and output rdata formated differently, for each tool. - enriched_rdata: - Description: flag created by that tool to tell it was enriched. - Retrieval method: enriched_rdata <- TRUE - enriched_rdata_version: - Description: A flag created by that tool to tell which version of this tool has enriched the rdata. - Retrieval method: enriched_rdata_version <- sprintf(\"%s\", ENRICHED_RDATA_VERSION) - enriched_rdata_doc: - Description: Contains the documentation string. Data from original mzxml file ------ - tic: - Description: Those are the tic values from the original mzxml file, extracted using xcms 2. - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@tic - xcms version: 2.0 - mz: - Description: Those are the m/z values from the original mzxml file, extracted using xcms 2. - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@env$mz - xcms version: 2.0 - scanindex: - Description: Those are the scanindex values from the original mzxml file, extracted using xcms 2. - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@scanindex - xcms version: 2.0 - scantime: - Description: Those are the scantime values from the original mzxml file, extracted using xcms 2. - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@scantime - xcms version: 2.0 - intensity: - Description: Those are the intensity values from the original mzxml file, extracted using xcms 2. - Retrieval method: xcms::xcmsRaw('original_file.mzxml')@env$intensity - xcms version: 2.0 - polarity: - Description: Those are the polarity values from the original mzxml file, extracted using xcms 2. - Retrieval method: as.character(xcms::xcmsRaw('original_file.mzxml')@polarity[[1]]) - xcms version: 2.0 Data taken from incoming rdata ------ - variableMetadata: - Description: Unmodified copy of variableMetadata from incoming rdata. - Retrieval method: rdata_file$variableMetadata - process_params: - Description: Those are the processing parameters values from the curent rdata. They have been simplified to allow easy access like: for (params in process_params) { if (params[[\"xfunction\"]] == \"annotatediff\") { process_peak_picking_params(params) } } - Retrieval method: ## just he same list, but simplified process_params <- list() for (list_name in names(rdata_file$listOFlistArguments)) { param_list <- list() for (param_name in names(rdata_file$listOFlistArguments[[list_name]])) { param_list[[param_name]] <- rdata_file$listOFlistArguments[[list_name]][[param_name]] } process_params[[length(process_params)+1]] <- param_list } ", ENRICHED_RDATA_VERSION, TOOL_NAME, VERSION, ENRICHED_RDATA_VERSION) get_models <- function(path) { if (is.null(path)) { stop("No models to define the database schema") } else { message(sprintf("Loading models from %s", path)) } ## galaxy mangles the "@" to a "__at__" if (substr(path, 1, 9) == "git__at__") { path <- sub("^git__at__", "git@", path, perl=TRUE) } if ( substr(path, 1, 4) == "git@" || substr(path, length(path)-4, 4) == ".git" ) { return (get_models_from_git(path)) } if (substr(path, 1, 4) == "http") { return (get_models_from_url(path)) } return (source(path)$value) } get_models_from_git <- function (url, target_file="models.R", rm=TRUE) { tmp <- tempdir() message(sprintf("Cloning %s", url)) system2("git", c("clone", url, tmp)) result <- search_tree(file.path(tmp, dir), target_file) if (!is.null(result)) { models <- source(result)$value if (rm) { unlink(tmp, recursive=TRUE) } return (models) } if (rm) { unlink(tmp, recursive=TRUE) } stop(sprintf( "Could not find any file named \"%s\" in this repo", target_file )) } get_models_from_url <- function (url, target_file="models.R", rm=TRUE) { tmp <- tempdir() message(sprintf("Downloading %s", url)) result <- file.path(tmp, target_file) if (download.file(url, destfile=result) == 0) { models <- source(result)$value if (rm) { unlink(tmp, recursive=TRUE) } return (models) } if (rm) { unlink(tmp, recursive=TRUE) } stop("Could not download any file at this adress.") } search_tree <- function(path, target) { target <- tolower(target) for (file in list.files(path)) { if (is.dir(file)) { result <- search_tree(file.path(path, file), target) if (!is.null(result)) { return (result) } } else if (tolower(file) == target) { return (file.path(path, file)) } } return (NULL) } create_database <- function(orm) { orm$recreate_database(no_exists=FALSE) set_database_version(orm, "created") } insert_adducts <- function(orm) { message("Creating adducts...") adducts <- list( list("[M-H2O-H]-",1,-1,-48.992020312000001069,1,0,0.5,"H0","H1O3"), list("[M-H-Cl+O]-",1,-1,-19.981214542000000022,2,0,0.5,"O1","H1Cl1"), list("[M-Cl+O]-",1,-1,-18.973389510000000512,3,0,0.5,"O1","Cl1"), list("[M-3H]3-",1,-3,-3.0218293560000000219,4,0,1.0,"H0","H3"), list("[2M-3H]3-",2,-3,-3.0218293560000000219,4,0,0.5,"H0","H3"), list("[3M-3H]3-",3,-3,-3.0218293560000000219,4,0,0.5,"H0","H3"), list("[M-2H]2-",1,-2,-2.0145529039999998666,5,0,1.0,"H0","H2"), list("[2M-2H]2-",2,-2,-2.0145529039999998666,5,0,0.5,"H0","H2"), list("[3M-2H]2-",3,-2,-2.0145529039999998666,5,0,0.5,"H0","H2"), list("[M-H]-",1,-1,-1.0072764519999999333,6,1,1.0,"H0","H1"), list("[2M-H]-",2,-1,-1.0072764519999999333,6,0,0.5,"H0","H1"), list("[3M-H]-",3,-1,-1.0072764519999999333,6,0,0.5,"H0","H1"), list("[M]+",1,1,-0.00054858000000000000945,7,1,1.0,"H0","H0"), list("[M]-",1,-1,0.00054858000000000000945,8,1,1.0,"H0","H0"), list("[M+H]+",1,1,1.0072764519999999333,9,1,1.0,"H1","H0"), list("[2M+H]+",2,1,1.0072764519999999333,9,0,0.5,"H1","H0"), list("[3M+H]+",3,1,1.0072764519999999333,9,0,0.25,"H1","H0"), list("[M+2H]2+",1,2,2.0145529039999998666,10,0,0.75,"H2","H0"), list("[2M+2H]2+",2,2,2.0145529039999998666,10,0,0.5,"H2","H0"), list("[3M+2H]2+",3,2,2.0145529039999998666,10,0,0.25,"H2","H0"), list("[M+3H]3+",1,3,3.0218293560000000219,11,0,0.75,"H3","H0"), list("[2M+3H]3+",2,3,3.0218293560000000219,11,0,0.5,"H3","H0"), list("[3M+3H]3+",3,3,3.0218293560000000219,11,0,0.25,"H3","H0"), list("[M-2H+NH4]-",1,-1,16.019272654000001665,12,0,0.25,"N1H4","H2"), list("[2M-2H+NH4]-",2,-1,16.019272654000001665,12,0,0.0,"N1H4","H2"), list("[3M-2H+NH4]-",3,-1,16.019272654000001665,12,0,0.25,"N1H4","H2"), list("[M+NH4]+",1,1,18.033825558000000199,13,1,1.0,"N1H4","H0"), list("[2M+NH4]+",2,1,18.033825558000000199,13,0,0.5,"N1H4","H0"), list("[3M+NH4]+",3,1,18.033825558000000199,13,0,0.25,"N1H4","H0"), list("[M+H+NH4]2+",1,2,19.041102009999999467,14,0,0.5,"N1H5","H0"), list("[2M+H+NH4]2+",2,2,19.041102009999999467,14,0,0.5,"N1H5","H0"), list("[3M+H+NH4]2+",3,2,19.041102009999999467,14,0,0.25,"N1H5","H0"), list("[M+Na-2H]-",1,-1,20.974668176000001551,15,0,0.75,"Na1","H2"), list("[2M-2H+Na]-",2,-1,20.974668176000001551,15,0,0.25,"Na1","H2"), list("[3M-2H+Na]-",3,-1,20.974668176000001551,15,0,0.25,"Na1","H2"), list("[M+Na]+",1,1,22.989221080000000086,16,1,1.0,"Na1","H0"), list("[2M+Na]+",2,1,22.989221080000000086,16,0,0.5,"Na1","H0"), list("[3M+Na]+",3,1,22.989221080000000086,16,0,0.25,"Na1","H0"), list("[M+H+Na]2+",1,2,23.996497531999999353,17,0,0.5,"Na1H1","H0"), list("[2M+H+Na]2+",2,2,23.996497531999999353,17,0,0.5,"Na1H1","H0"), list("[3M+H+Na]2+",3,2,23.996497531999999353,17,0,0.25,"Na1H1","H0"), list("[M+2H+Na]3+",1,3,25.003773983999998619,18,0,0.25,"H2Na1","H0"), list("[M+CH3OH+H]+",1,1,33.033491200000000276,19,0,0.25,"C1O1H5","H0"), list("[M-H+Cl]2-",1,-2,33.962124838000001148,20,0,1.0,"Cl1","H1"), list("[2M-H+Cl]2-",2,-2,33.962124838000001148,20,0,0.5,"Cl1","H1"), list("[3M-H+Cl]2-",3,-2,33.962124838000001148,20,0,0.5,"Cl1","H1"), list("[M+Cl]-",1,-1,34.969401290000000416,21,1,1.0,"Cl1","H0"), list("[2M+Cl]-",2,-1,34.969401290000000416,21,0,0.5,"Cl1","H0"), list("[3M+Cl]-",3,-1,34.969401290000000416,21,0,0.5,"Cl1","H0"), list("[M+K-2H]-",1,-1,36.948605415999999479,22,0,0.5,"K1","H2"), list("[2M-2H+K]-",2,-1,36.948605415999999479,22,0,0.0,"K1","H2"), list("[3M-2H+K]-",3,-1,36.948605415999999479,22,0,0.0,"K1","H2"), list("[M+K]+",1,1,38.963158319999998013,23,1,1.0,"K1","H0"), list("[2M+K]+",2,1,38.963158319999998013,23,0,0.5,"K1","H0"), list("[3M+K]+",3,1,38.963158319999998013,23,0,0.25,"K1","H0"), list("[M+H+K]2+",1,2,39.970434771999997281,24,0,0.5,"K1H1","H0"), list("[2M+H+K]2+",2,2,39.970434771999997281,24,0,0.5,"K1H1","H0"), list("[3M+H+K]2+",3,2,39.970434771999997281,24,0,0.25,"K1H1","H0"), list("[M+ACN+H]+",1,1,42.033825557999996646,25,0,0.25,"C2H4N1","H0"), list("[2M+ACN+H]+",2,1,42.033825557999996646,25,0,0.25,"C2H4N1","H0"), list("[M+2Na-H]+",1,1,44.971165708000000902,26,0,0.5,"Na2","H1"), list("[2M+2Na-H]+",2,1,44.971165708000000902,26,0,0.25,"Na2","H1"), list("[3M+2Na-H]+",3,1,44.971165708000000902,26,0,0.25,"Na2","H1"), list("[2M+FA-H]-",2,-1,44.998202851999998586,27,0,0.25,"C1O2H2","H1"), list("[M+FA-H]-",1,-1,44.998202851999998586,27,0,0.5,"C1O2H2","H1"), list("[M+2Na]2+",1,2,45.978442160000000172,28,0,0.5,"Na2","H0"), list("[2M+2Na]2+",2,2,45.978442160000000172,28,0,0.5,"Na2","H0"), list("[3M+2Na]2+",3,2,45.978442160000000172,28,0,0.25,"Na2","H0"), list("[M+H+2Na]3+",1,3,46.985718611999999438,29,0,0.25,"H1Na2","H0"), list("[M+H+FA]+",1,1,47.012755755999997122,30,0,0.25,"C1O2H3","H0"), list("[M+Hac-H]-",1,-1,59.013852915999997607,31,0,0.25,"C2O2H4","H1"), list("[2M+Hac-H]-",2,-1,59.013852915999997607,31,0,0.25,"C2O2H4","H1"), list("[M+IsoProp+H]+",1,1,61.064791327999998317,32,0,0.25,"C3H9O1","H0"), list("[M+Na+K]2+",1,2,61.9523793999999981,33,0,0.5,"Na1K1","H0"), list("[2M+Na+K]2+",2,2,61.9523793999999981,33,0,0.5,"Na1K1","H0"), list("[3M+Na+K]2+",3,2,61.9523793999999981,33,0,0.25,"Na1K1","H0"), list("[M+NO3]-",1,-1,61.988366450000000895,34,0,0.5,"N1O3","H0"), list("[M+ACN+Na]+",1,1,64.015770185999997464,35,0,0.25,"C2H3N1Na1","H0"), list("[2M+ACN+Na]+",2,1,64.015770185999997464,35,0,0.25,"C2H3N1Na1","H0"), list("[M+NH4+FA]+",1,1,64.039304861999994502,36,0,0.25,"N1C1O2H6","H0"), list("[M-2H+Na+FA]-",1,-1,66.980147479999999405,37,0,0.5,"NaC1O2H2","H2"), list("[M+3Na]3+",1,3,68.967663239999993153,38,0,0.25,"Na3","H0"), list("[M+Na+FA]+",1,1,68.99470038399999794,39,0,0.25,"Na1C1O2H2","H0"), list("[M+2Cl]2-",1,-2,69.938802580000000832,40,0,1.0,"Cl2","H0"), list("[2M+2Cl]2-",2,-2,69.938802580000000832,40,0,0.5,"Cl2","H0"), list("[3M+2Cl]2-",3,-2,69.938802580000000832,40,0,0.5,"Cl2","H0"), list("[M+2K-H]+",1,1,76.919040187999996758,41,0,0.5,"K2","H1"), list("[2M+2K-H]+",2,1,76.919040187999996758,41,0,0.25,"K2","H1"), list("[3M+2K-H]+",3,1,76.919040187999996758,41,0,0.25,"K2","H1"), list("[M+2K]2+",1,2,77.926316639999996028,42,0,0.5,"K2","H0"), list("[2M+2K]2+",2,2,77.926316639999996028,42,0,0.5,"K2","H0"), list("[3M+2K]2+",3,2,77.926316639999996028,42,0,0.25,"K2","H0"), list("[M+Br]-",1,-1,78.918886479999997619,43,1,1.0,"Br1","H0"), list("[M+Cl+FA]-",1,-1,80.974880593999998268,44,0,0.5,"Cl1C1O2H2","H0"), list("[M+AcNa-H]-",1,-1,80.995797543999998426,45,0,0.25,"C2H3Na1O2","H1"), list("[M+2ACN+2H]2+",1,2,84.067651115999993292,46,0,0.25,"C4H8N2","H0"), list("[M+K+FA]+",1,1,84.968637623999995868,47,0,0.25,"K1C1O2H2","H0"), list("[M+Cl+Na+FA-H]-",1,-1,102.95682522200000619,48,0,0.5,"Cl1Na1C1O2H2","H1"), list("[2M+3H2O+2H]+",2,1,104.03153939599999944,49,0,0.25,"H8O6","H0"), list("[M+TFA-H]-",1,-1,112.98558742000000165,50,0,0.5,"C2F3O2H1","H1"), list("[M+H+TFA]+",1,1,115.00014032400000019,51,0,0.25,"C2F3O2H2","H0"), list("[M+3ACN+2H]2+",1,2,125.09420022199999778,52,0,0.25,"C6H11N3","H0"), list("[M+NH4+TFA]+",1,1,132.02668943000000468,53,0,0.25,"N1C2F3O2H5","H0"), list("[M+Na+TFA]+",1,1,136.98208495200000811,54,0,0.25,"Na1C2F3O2H1","H0"), list("[M+Cl+TFA]-",1,-1,148.96226516199999423,55,0,0.5,"Cl1C2F3O2H1","H0"), list("[M+K+TFA]+",1,1,152.95602219200000604,56,0,0.25,"K1C2F3O2H1","H0") ) dummy_adduct <- orm$adduct() for (adduct in adducts) { i <- 0 dummy_adduct$set_name(adduct[[i <- i+1]]) dummy_adduct$set_multi(adduct[[i <- i+1]]) dummy_adduct$set_charge(adduct[[i <- i+1]]) dummy_adduct$set_mass(adduct[[i <- i+1]]) dummy_adduct$set_oidscore(adduct[[i <- i+1]]) dummy_adduct$set_quasi(adduct[[i <- i+1]]) dummy_adduct$set_ips(adduct[[i <- i+1]]) dummy_adduct$set_formula_add(adduct[[i <- i+1]]) dummy_adduct$set_formula_ded(adduct[[i <- i+1]]) invisible(dummy_adduct$save()) dummy_adduct$clear(unset_id=TRUE) } message("Adducts created") } insert_base_data <- function(orm, path, archetype=FALSE) { if (archetype) { ## not implemented yet return () } base_data <- readLines(path) for (sql in strsplit(paste(base_data, collapse=" "), ";")[[1]]) { orm$execute(sql) } set_database_version(orm, "enriched") } insert_compounds <- function(orm, compounds_path) { compounds <- read.csv(file=compounds_path, sep="\t") if (is.null(compounds <- translate_compounds(compounds))) { stop("Could not find asked compound's attributes in csv file.") } dummy_compound <- orm$compound() compound_list <- list() for (i in seq_len(nrow(compounds))) { dummy_compound$set_mz(compounds[i, "mz"]) dummy_compound$set_name(compounds[i, "name"]) dummy_compound$set_common_name(compounds[i, "common_name"]) dummy_compound$set_formula(compounds[i, "formula"]) compound_list[[length(compound_list)+1]] <- as.list( dummy_compound, c("mz", "name", "common_name", "formula") ) dummy_compound$clear(unset_id=TRUE) } invisible(dummy_compound$save(bulk=compound_list)) } translate_compounds <- function(compounds) { recognized_headers <- list( c("HMDB_ID", "MzBank", "X.M.H..", "X.M.H...1", "MetName", "ChemFormula", "INChIkey") ) header_translators <- list( hmdb_header_translator ) for (index in seq_along(recognized_headers)) { headers <- recognized_headers[[index]] if (identical(colnames(compounds), headers)) { return (header_translators[[index]](compounds)) } } if (is.null(translator <- guess_translator(colnames(compounds)))) { return (NULL) } return (csv_header_translator(translator, compounds)) } guess_translator <- function(header) { result <- list( # HMDB_ID=NULL, mz=NULL, name=NULL, common_name=NULL, formula=NULL, # inchi_key=NULL ) asked_cols <- names(result) for (asked_col in asked_cols) { for (col in header) { if ((twisted <- tolower(col)) == asked_col || gsub("-", "_", twisted) == asked_col || gsub(" ", "_", twisted) == asked_col || tolower(gsub("(.)([A-Z])", "\\1_\\2", col)) == asked_col ) { result[[asked_col]] <- col next } } } if (any(mapply(is.null, result))) { return (NULL) } return (result) } hmdb_header_translator <- function(compounds) { return (csv_header_translator( list( HMDB_ID="HMDB_ID", mz="MzBank", name="MetName", common_name="MetName", formula="ChemFormula", inchi_key="INChIkey" ), compounds )) } csv_header_translator <- function(translation_table, csv) { header_names <- names(translation_table) result <- data.frame(1:nrow(csv)) for (i in seq_along(header_names)) { result[, header_names[[i]]] <- csv[, translation_table[[i]]] } result[, "mz"] <- as.numeric(result[, "mz"]) return (result) } set_database_version <- function(orm, version) { orm$set_tag( version, tag_name="database_version", tag_table_name="XSeeker_tagging_table" ) } process_rdata <- function(orm, rdata, options) { mzml_tmp_dir <- gather_mzml_files(rdata) samples <- names(rdata$singlefile) if (!is.null(options$samples)) { samples <- samples[options$samples %in% samples] } show_percent <- ( is.null(options$`not-show-percent`) || options$`not-show-percent` == FALSE ) error <- tryCatch({ process_sample_list( orm, rdata, samples, show_percent=show_percent ) NULL }, error=function(e) { message(e) e }) if (!is.null(mzml_tmp_dir)) { unlink(mzml_tmp_dir, recursive=TRUE) } if (!is.null(error)) { stop(error) } } gather_mzml_files <- function(rdata) { if (is.null(rdata$singlefile)) { message("Extracting mxml files") tmp <- tempdir() rdata$singlefile <- utils::unzip(rdata$zipfile, exdir=tmp) names(rdata$singlefile) <- tools::file_path_sans_ext(basename(rdata$singlefile)) message("Extracted") return (tmp) } else { message(sprintf("Not a zip file, loading files directly from path: %s", paste(rdata$singlefile, collapse=" ; "))) } return (NULL) } process_sample_list <- function(orm, radta, sample_names, show_percent) { file_grouping_var <- find_grouping_var(rdata$variableMetadata) message("Processing samples.") message(sprintf("File grouping variable: %s", file_grouping_var)) if(is.null(file_grouping_var)) { stop("Malformed variableMetada.") } context <- new.env() context$samples <- list() context$peaks <- rdata$xa@xcmsSet@peaks context$groupidx <- rdata$xa@xcmsSet@groupidx xcms_set <- rdata$xa@xcmsSet singlefile <- rdata$singlefile process_arg_list <- rdata$listOFlistArguments var_meta <- rdata$variableMetadata process_params <- list() if (is.null(process_arg_list)) { histories <- list() for (history in xcms_set@.processHistory) { if ( class(history@param) == "CentWaveParam" && history@type == "Peak detection" ) { params <- history@param process_params <- list(list( xfunction="annotatediff", ppm=params@ppm, peakwidth=sprintf("%s - %s", params@peakwidth[[1]], params@peakwidth[[2]]), snthresh=params@snthresh, prefilterStep=params@prefilter[[1]], prefilterLevel=params@prefilter[[2]], mzdiff=params@mzdiff, fitgauss=params@fitgauss, noise=params@noise, mzCenterFun=params@mzCenterFun, integrate=params@integrate, firstBaselineCheck=params@firstBaselineCheck, snthreshIsoROIs=!identical(params@roiScales, numeric(0)) )) break } } } else { for (list_name in names(process_arg_list)) { param_list <- list() for (param_name in names(process_arg_list[[list_name]])) { param_list[[param_name]] <- process_arg_list[[list_name]][[param_name]] } process_params[[length(process_params)+1]] <- param_list } } message("Parameters from previous processes extracted.") indices <- as.numeric(unique(var_meta[, file_grouping_var])) smol_xcms_set <- orm$smol_xcms_set() mz_tab_info <- new.env() g <- xcms::groups(xcms_set) mz_tab_info$group_length <- nrow(g) mz_tab_info$dataset_path <- xcms::filepaths(xcms_set) mz_tab_info$sampnames <- xcms::sampnames(xcms_set) mz_tab_info$sampclass <- xcms::sampclass(xcms_set) mz_tab_info$rtmed <- g[,"rtmed"] mz_tab_info$mzmed <- g[,"mzmed"] mz_tab_info$smallmolecule_abundance_assay <- xcms::groupval(xcms_set, value="into") blogified <- blob::blob(fst::compress_fst(serialize(mz_tab_info, NULL), compression=100)) rm(mz_tab_info) invisible(smol_xcms_set$set_raw(blogified)$save()) smol_xcms_set_id <- smol_xcms_set$get_id() rm(smol_xcms_set) for (no in indices) { sample_name <- names(singlefile)[[no]] sample_path <- singlefile[[no]] if ( is.na(no) || is.null(sample_path) || !(sample_name %in% sample_names) ) { next } env <- new.env() ms_file <- xcms::xcmsRaw(sample_path) env$tic <- ms_file@tic env$mz <- ms_file@env$mz env$scanindex <- ms_file@scanindex env$scantime <- ms_file@scantime * 60 env$intensity <- ms_file@env$intensity env$polarity <- as.character(ms_file@polarity[[1]]) ## Again, ms file is huge, so we get rid of it quickly. rm(ms_file) env$sample_name <- sample_name env$dataset_path <- sample_path env$process_params <- process_params env$enriched_rdata <- TRUE env$enriched_rdata_version <- ENRICHED_RDATA_VERSION env$tool_name <- TOOL_NAME env$enriched_rdata_doc <- ENRICHED_RDATA_DOC sample <- add_sample_to_database(orm, env, context, smol_xcms_set_id) rm (env) context$samples[no] <- sample$get_id() rm (sample) } context$clusters <- list() context$show_percent <- show_percent context$cluster_mean_rt_abundance <- list() context$central_feature <- list() context$adducts <- list() load_variable_metadata(orm, var_meta, context) clusters <- context$clusters rm(context) message("Features enrichment") complete_features(orm, clusters, show_percent) message("Features enrichment done.") return (NULL) } find_grouping_var <- function(var_meta) { known_colnames = c( "name", "namecustom", "mz", "mzmin", "mzmax", "rt", "rtmin", "rtmax", "npeaks", "isotopes", "adduct", "pcgroup" ) col_names <- colnames(var_meta) classes = list() for (name in col_names) { if (!(name %in% known_colnames)) { classes[[length(classes)+1]] = name } } if (length(classes) > 1) { stop(sprintf("Only one class expected in the variable metadata. Found %d .", length(classes))) } if (length(classes) == 0) { stop("Could not find any class column in your variableMetadata.") } return (classes[[1]]) } add_sample_to_database <- function(orm, env, context, smol_xcms_set_id) { message(sprintf("Processing sample %s", env$sample_name)) sample <- ( orm$sample() $set_name(env$sample_name) $set_path(env$dataset_path) $set_kind("enriched_rdata") $set_polarity( if (is.null(env$polarity) || identical(env$polarity, character(0))) "" else env$polarity ) $set_raw(blob::blob(fst::compress_fst( serialize(env, NULL), compression=100 ))) ) sample[["smol_xcms_set_id"]] <- smol_xcms_set_id sample$modified__[["smol_xcms_set_id"]] <- smol_xcms_set_id sample <- sample$save() load_process_params(orm, sample, env$process_params) message(sprintf("Sample %s inserted.", env$sample_name)) return (sample) } load_variable_metadata <- function(orm, var_meta, context) { all_clusters <- orm$cluster()$all() next_feature_id <- get_next_id(orm$feature()$all(), "featureID") + 1 next_cluster_id <- 0 next_pc_group <- get_next_id(all_clusters, "pc_group") next_align_group <- get_next_id(all_clusters, "align_group") + 1 message("Extracting features") invisible(create_features( orm, var_meta, context, next_feature_id, next_cluster_id, next_pc_group, next_align_group )) message("Extracting features done.") return (NULL) } get_next_id <- function(models, attribute) { if ((id <- models$max(attribute)) == Inf || id == -Inf) { return (0) } return (id) } create_features <- function( orm, var_meta, context, next_feature_id, next_cluster_id, next_pc_group, next_align_group ) { field_names <- as.list(names(orm$feature()$fields__)) field_names[field_names=="id"] <- NULL features <- list() dummy_feature <- orm$feature() if (show_percent <- context$show_percent) { percent <- -1 total <- nrow(var_meta) } for (row in seq_len(nrow(var_meta))) { if (show_percent && (row / total) * 100 > percent) { percent <- percent + 1 message("\r", sprintf("\r%d %%", percent), appendLF=FALSE) } curent_var_meta <- var_meta[row, ] set_feature_fields_from_var_meta(dummy_feature, curent_var_meta) dummy_feature$set_featureID(next_feature_id) next_feature_id <- next_feature_id + 1 fake_iso <- dummy_feature$get_iso() iso <- extract_iso(fake_iso) clusterID <- extract_clusterID(fake_iso, next_cluster_id) context$clusterID <- clusterID dummy_feature$set_iso(iso) peak_list <- context$peaks[context$groupidx[[row]], ] if (! ("matrix" %in% class(peak_list))) { peak_list <- matrix(peak_list, nrow=1, ncol=length(peak_list), dimnames=list(c(), names(peak_list))) } clusterID <- as.character(clusterID) if (is.null(context$central_feature[[clusterID]])) { int_o <- extract_peak_var(peak_list, "into") context$central_feature[[clusterID]] <- ( peak_list[peak_list[, "into"] == int_o,]["sample"] ) } sample_peak_list <- peak_list[as.integer(peak_list[, "sample"]) == context$central_feature[[clusterID]], , drop=FALSE] if (!identical(sample_peak_list, numeric(0)) && !is.null(nrow(sample_peak_list)) && nrow(sample_peak_list) != 0) { if (!is.na(int_o <- extract_peak_var(sample_peak_list, "into"))) { dummy_feature$set_int_o(int_o) } if (!is.na(int_b <- extract_peak_var(sample_peak_list, "intb"))) { dummy_feature$set_int_b(int_b) } if (!is.na(max_o <- extract_peak_var(sample_peak_list, "maxo"))) { dummy_feature$set_max_o(max_o) } } create_associated_cluster( orm, context$central_feature[[clusterID]], dummy_feature, clusterID, context, curent_var_meta, next_pc_group, next_align_group ) next_align_group <- next_align_group + 1 features[[length(features)+1]] <- as.list(dummy_feature, field_names) dummy_feature$clear() } message("")## +\n for previous message message("Saving features") rm(var_meta) invisible(dummy_feature$save(bulk=features)) message("Saved.") return (context$clusters) } extract_peak_var <- function(peak_list, var_name, selector=max) { value <- peak_list[, var_name] names(value) <- NULL return (selector(value)) } set_feature_fields_from_var_meta <- function(feature, var_meta) { if (!is.null(mz <- var_meta[["mz"]]) && !is.na(mz)) { feature$set_mz(mz) } if (!is.null(mzmin <- var_meta[["mzmin"]]) && !is.na(mzmin)) { feature$set_mz_min(mzmin) } if (!is.null(mzmax <- var_meta[["mzmax"]]) && !is.na(mzmax)) { feature$set_mz_max(mzmax) } if (!is.null(rt <- var_meta[["rt"]]) && !is.na(rt)) { feature$set_rt(rt) } if (!is.null(rtmin <- var_meta[["rtmin"]]) && !is.na(rtmin)) { feature$set_rt_min(rtmin) } if (!is.null(rtmax <- var_meta[["rtmax"]]) && !is.na(rtmax)) { feature$set_rt_max(rtmax) } if (!is.null(isotopes <- var_meta[["isotopes"]]) && !is.na(isotopes)) { feature$set_iso(isotopes) } return (feature) } extract_iso <- function(weird_data) { if (grepl("^\\[\\d+\\]", weird_data)[[1]]) { return (sub("^\\[\\d+\\]", "", weird_data, perl=TRUE)) } return (weird_data) } extract_clusterID <- function(weird_data, next_cluster_id){ if (grepl("^\\[\\d+\\]", weird_data)[[1]]) { clusterID <- stringr::str_extract(weird_data, "^\\[\\d+\\]") clusterID <- as.numeric(stringr::str_extract(clusterID, "\\d+")) } else { clusterID <- 0 } return (clusterID + next_cluster_id) } create_associated_cluster <- function( orm, sample_no, feature, clusterID, context, curent_var_meta, next_pc_group, next_align_group ) { clusterID <- as.character(clusterID) if (is.null(cluster <- context$clusters[[clusterID]])) { pcgroup <- as.numeric(curent_var_meta[["pcgroup"]]) adduct_name <- as.character(curent_var_meta[["adduct"]]) annotation <- curent_var_meta[["isotopes"]] cluster <- context$clusters[[clusterID]] <- orm$cluster( pc_group=pcgroup + next_pc_group, # adduct=adduct, align_group=next_align_group, # curent_group=curent_group, clusterID=context$clusterID, annotation=annotation ) if (is.null(adduct <- context$adducts[[adduct_name]])) { context$adducts[[adduct_name]] <- orm$adduct()$load_by(name=adduct_name)$first() if (is.null(adduct <- context$adducts[[adduct_name]])) { adduct <- context$adducts[[adduct_name]] <- orm$adduct(name=adduct_name, charge=0) adduct$save() } } cluster$set_adduct(adduct) ## Crappy hack to assign sample id to cluster without loading the sample. ## Samples are too big (their sample$env) and slows the process, and eat all the menory ## so we dont't want to load them. cluster[["sample_id"]] <- context$samples[sample_no][[1]] cluster$modified__[["sample_id"]] <- cluster[["sample_id"]] } else { if (context$clusterID != 0 && cluster$get_clusterID() == 0) { cluster$set_clusterID(context$clusterID) } } cluster$save() feature$set_cluster(cluster) return (feature) } complete_features <- function(orm, clusters, show_percent) { total <- length(clusters) percent <- -1 i <- 0 for (cluster in clusters) { i <- i+1 if (show_percent && (i / total) * 100 > percent) { percent <- percent + 1 message("\r", sprintf("\r%d %%", percent), appendLF=FALSE) } features <- orm$feature()$load_by(cluster_id=cluster$get_id()) if (features$any()) { if (!is.null(rt <- features$mean("rt"))) { cluster$set_mean_rt(rt)$save() } features_df <- as.data.frame(features) central_feature <- features_df[grepl("^\\[M\\]", features_df[, "iso"]), ] central_feature_into <- central_feature[["int_o"]] if (!identical(central_feature_into, numeric(0)) && central_feature_into != 0) { for (feature in as.vector(features)) { feature$set_abundance( feature$get_int_o() / central_feature_into * 100 )$save() } } } } return (NULL) } load_process_params <- function(orm, sample, params) { for (param_list in params) { if (is.null(param_list[["xfunction"]])) { next } if (param_list[["xfunction"]] == "annotatediff") { load_process_params_peak_picking(orm, sample, param_list) } } return (sample) } load_process_params_peak_picking <- function(orm, sample, peak_picking_params) { return (add_sample_process_parameters( params=peak_picking_params, params_translation=list( ppm="ppm", maxcharge="maxCharge", maxiso="maxIso" ), param_model_generator=orm$peak_picking_parameters, sample_param_setter=sample$set_peak_picking_parameters )) } add_sample_process_parameters <- function( params, params_translation, param_model_generator, sample_param_setter ) { model_params <- list() for (rdata_param_name in names(params_translation)) { database_param_name <- params_translation[[rdata_param_name]] if (is.null(rdata_param <- params[[rdata_param_name]])) { next } model_params[[database_param_name]] <- rdata_param } params_models <- do.call(param_model_generator()$load_by, model_params) if (params_models$any()) { params_model <- params_models$first() } else { params_model <- do.call(param_model_generator, model_params) params_model$save() } return (sample_param_setter(params_model)$save()) } library(optparse) option_list <- list( optparse::make_option( c("-v", "--version"), action="store_true", help="Display this tool's version and exits" ), optparse::make_option( c("-i", "--input"), type="character", help="The rdata path to import in XSeeker" ), optparse::make_option( c("-s", "--samples"), type="character", help="Samples to visualise in XSeeker" ), optparse::make_option( c("-B", "--archetype"), type="character", help="The name of the base database" ), optparse::make_option( c("-b", "--database"), type="character", help="The base database's path" ), optparse::make_option( c("-c", "--compounds-csv"), type="character", help="The csv containing compounds" ), optparse::make_option( c("-m", "--models"), type="character", help="The path or url (must begin with http[s]:// or git@) to the database's models" ), optparse::make_option( c("-o", "--output"), type="character", help="The path where to output sqlite" ), optparse::make_option( c("-P", "--not-show-percent"), action="store_true", help="Flag not to show the percents", default=FALSE ) ) options(error=function(){traceback(3)}) parser <- OptionParser(usage="%prog [options] file", option_list=option_list) args <- parse_args(parser, positional_arguments=0) err_code <- 0 if (!is.null(args$options$version)) { message(sprintf("%s %s", TOOL_NAME, VERSION)) quit() } models <- get_models(args$options$models) orm <- DBModelR::ORM( connection_params=list(dbname=args$options$output), dbms="SQLite" ) invisible(orm$models(models)) invisible(create_database(orm)) message("Database model created") insert_adducts(orm) if (!is.null(args$options$database)) { insert_base_data(orm, args$options$database) } message(sprintf("Base data inserted using %s.", args$options$database)) if (!is.null(args$options$archetype)) { insert_base_data(orm, args$options$archetype, archetype=TRUE) } if (!is.null(args$options$`compounds-csv`)) { insert_compounds(orm, args$options$`compounds-csv`) } # if (!is.null(args$options$rdata)) { # load_rdata_in_base(args$options$rdata, args$options$samples, args$options$`not-show-percent`) # } load(args$options$input, rdata <- new.env()) process_rdata(orm, rdata, args$options) quit(status=err_code)