getNodeRun {DIAlignR} | R Documentation |
Get merged features and merged chromatograms from parent runs. Chromatograms are written on the disk at dataPath/xics. For each precursor aligned parent time-vectors and corresponding child time-vector are also calculated and written as *_av.rda at dataPath.
getNodeRun( runA, runB, mergeName, dataPath, fileInfo, features, mzPntrs, prec2chromIndex, precursors, params, adaptiveRTs, refRuns, multipeptide, peptideScores, ropenms, applyFun = lapply )
runA |
(string) name of a run to be merged with runB. Must be in the rownames of fileInfo. |
runB |
(string) name of a run to be merged with runA. Must be in the rownames of fileInfo. |
mergeName |
(string) name of the node that is generated with merging of runA and runB. |
dataPath |
(string) path to xics and osw directory. |
fileInfo |
(data-frame) output of |
features |
(list of data-frames) contains features and their properties identified in each run. |
mzPntrs |
(list) a list of mzRpwiz. |
prec2chromIndex |
(list) a list of dataframes having following columns: |
precursors |
(data-frame) atleast two columns transition_group_id and transition_ids are required. |
params |
(list) parameters are entered as list. Output of the |
adaptiveRTs |
(environment) an empty environment used to store data for downstream analysis. |
refRuns |
(environment) an empty environment used to store data for downstream analysis. |
multipeptide |
(environment) contains multiple data-frames that are collection of features
associated with analytes. This is an output of |
peptideScores |
(list of data-frames) each dataframe has scores of a peptide across all runs. |
ropenms |
(pyopenms module) get this python module through |
applyFun |
(function) value must be either lapply or BiocParallel::bplapply. |
(None)
Shubham Gupta, shubh.gupta@mail.utoronto.ca
ORCID: 0000-0003-3500-8152
License: (c) Author (2020) + GPL-3 Date: 2020-06-06
childXICs, getChildXICs, traverseUp
library(data.table) dataPath <- system.file("extdata", package = "DIAlignR") params <- paramsDIAlignR() fileInfo <- getRunNames(dataPath = dataPath) mzPntrs <- list2env(getMZMLpointers(fileInfo)) precursors <- getPrecursors(fileInfo, oswMerged = TRUE, runType = params[["runType"]], context = "experiment-wide", maxPeptideFdr = params[["maxPeptideFdr"]]) peptideIDs <- unique(precursors$peptide_id) peptideScores <- getPeptideScores(fileInfo, peptideIDs, oswMerged = TRUE, params[["runType"]], params[["context"]]) masters <- paste("master", 1:(nrow(fileInfo)-1), sep = "") peptideScores <- lapply(peptideIDs, function(pep) {x <- peptideScores[.(pep)][,-c(1L)] x <- rbindlist(list(x, data.table("run" = masters, "score" = NA_real_, "pvalue" = NA_real_, "qvalue" = NA_real_)), use.names=TRUE) setkeyv(x, "run"); x}) names(peptideScores) <- as.character(peptideIDs) features <- getFeatures(fileInfo, maxFdrQuery = 1.00, runType = "DIA_Proteomics") ## Not run: masterFeatures <- dummyFeatures(precursors, nrow(fileInfo)-1, 1L) features <- do.call(c, list(features, masterFeatures)) multipeptide <- getMultipeptide(precursors, features, numMerge = 0L, startIdx = 1L) prec2chromIndex <- getChromatogramIndices(fileInfo, precursors, mzPntrs) masterChromIndex <- dummyChromIndex(precursors, nrow(fileInfo)-1, 1L) prec2chromIndex <- do.call(c, list(prec2chromIndex, masterChromIndex)) mergeName <- "master1" adaptiveRTs <- new.env() refRuns <- new.env() getNodeRun(runA = "run2", runB = "run0", mergeName = mergeName, dataPath = ".", fileInfo, features, mzPntrs, prec2chromIndex, precursors, params, adaptiveRTs, refRuns, multipeptide, peptideScores, ropenms = NULL) file.remove(file.path(".", "xics", paste0(mergeName, ".chrom.sqMass"))) file.remove(list.files(".", pattern = "*_av.rds", full.names = TRUE)) ## End(Not run) for(run in names(mzPntrs)) DBI::dbDisconnect(mzPntrs[[run]])