traverseDown: Traverses down from the root to leaves

In shubham1637/DIAlignR: Dynamic Programming Based Alignment of MS2 Chromatograms

Traverses down from the root to leaves

Description

Features of the root node are propagated to all leaves node. Aligned features are set/added in the multipeptide environment.

Usage

traverseDown(
  tree,
  dataPath,
  fileInfo,
  multipeptide,
  prec2chromIndex,
  mzPntrs,
  precursors,
  adaptiveRTs,
  refRuns,
  params,
  applyFun = lapply
)

Arguments

tree	(phylo) a phylogenetic tree.
dataPath	(string) path to xics and osw directory.
fileInfo	(data-frame) output of getRunNames.
multipeptide	(environment) contains multiple data-frames that are collection of features associated with analytes. This is an output of getMultipeptide.
prec2chromIndex	(list) a list of dataframes having following columns: transition_group_id: it is PRECURSOR.ID from osw file. chromatogramIndex: index of chromatogram in mzML file.
mzPntrs	(list) a list of mzRpwiz.
precursors	(data-frame) atleast two columns transition_group_id and transition_ids are required.
adaptiveRTs	(environment) For each descendant-pair, it contains the window around the aligned retention time, within which features with m-score below aligned FDR are considered for quantification.
refRuns	(environment) For each descendant-pair, the reference run is indicated by 1 or 2 for all the peptides.
params	(list) parameters are entered as list. Output of the paramsDIAlignR function.
applyFun	(function) value must be either lapply or BiocParallel::bplapply.
analytes	(integer) this vector contains transition_group_id from precursors. It must be of the same length as of multipeptide.

Value

(None)

Author(s)

Shubham Gupta, [e-mail]

ORCID: 0000-0003-3500-8152

License: (c) Author (2020) + GPL-3 Date: 2020-07-01

See Also

traverseUp, alignToMaster

Examples

dataPath <- system.file("extdata", package = "DIAlignR")
params <- paramsDIAlignR()
fileInfo <- getRunNames(dataPath = dataPath)
mzPntrs <- list2env(getMZMLpointers(fileInfo))
features <- list2env(getFeatures(fileInfo, maxFdrQuery = params[["maxFdrQuery"]], runType = params[["runType"]]))
precursors <- getPrecursors(fileInfo, oswMerged = TRUE, runType = params[["runType"]],
 context = "experiment-wide", maxPeptideFdr = params[["maxPeptideFdr"]])
precursors <- dplyr::arrange(precursors, .data$peptide_id, .data$transition_group_id)
peptideIDs <- unique(precursors$peptide_id)
peptideScores <- getPeptideScores(fileInfo, peptideIDs, oswMerged = TRUE, params[["runType"]], params[["context"]])
peptideScores <- lapply(peptideIDs, function(pep) dplyr::filter(peptideScores, .data$peptide_id == pep))
names(peptideScores) <- as.character(peptideIDs)
prec2chromIndex <- list2env(getChromatogramIndices(fileInfo, precursors, mzPntrs))
multipeptide <- getMultipeptide(precursors, features)
adaptiveRTs <- new.env()
refRuns <- new.env()
tree <- ape::read.tree(text = "(run1:9,(run2:7,run0:2)master2:5)master1;")
tree <- ape::reorder.phylo(tree, "postorder")
## Not run: 
ropenms <- get_ropenms(condaEnv = "envName", useConda=TRUE)
multipeptide <- traverseUp(tree, dataPath, fileInfo, features, mzPntrs, prec2chromIndex, precursors, params,
 adaptiveRTs, refRuns, multipeptide, peptideScores, ropenms)
multipeptide <- getMultipeptide(precursors, features)
multipeptide <- traverseDown(tree, dataPath, fileInfo, multipeptide, prec2chromIndex, mzPntrs, precursors,
 adaptiveRTs, refRuns, params)
# Cleanup
for(run in names(mzPntrs)) DBI::dbDisconnect(mzPntrs[[run]])
file.remove(list.files(dataPath, pattern = "*_av.rds", full.names = TRUE))
file.remove(list.files(file.path(dataPath, "xics"), pattern = "^master[0-9]+\\.chrom\\.sqMass$", full.names = TRUE))

## End(Not run)

shubham1637/DIAlignR documentation built on March 29, 2023, 8:45 p.m.