Description Usage Arguments Value Author(s) See Also Examples
Features of the root node are propagated to all leaves node. Aligned features are set/added in the multipeptide environment.
1 2 3 4 5 6 7 8 9 10 11 12 13 | traverseDown(
tree,
dataPath,
fileInfo,
multipeptide,
prec2chromIndex,
mzPntrs,
precursors,
adaptiveRTs,
refRuns,
params,
applyFun = lapply
)
|
tree |
(phylo) a phylogenetic tree. |
dataPath |
(string) path to xics and osw directory. |
fileInfo |
(data-frame) output of |
multipeptide |
(environment) contains multiple data-frames that are collection of features
associated with analytes. This is an output of |
prec2chromIndex |
(list) a list of dataframes having following columns: |
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 |
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. |
(None)
Shubham Gupta, shubh.gupta@mail.utoronto.ca
ORCID: 0000-0003-3500-8152
License: (c) Author (2020) + GPL-3 Date: 2020-07-01
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | 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
rm(mzPntrs)
file.remove(list.files(dataPath, pattern = "*_av.rds", full.names = TRUE))
file.remove(list.files(file.path(dataPath, "xics"), pattern = "^master[0-9]+\\.chrom\\.mzML$", full.names = TRUE))
## End(Not run)
|
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