alignToMaster | R Documentation |
During traverse-down, parent runs are aligned to the master/child run. This function performs the alignment by already saved aligned parent-child time vectors. For the aligned peaks, alignment_rank is set to 1 in multipeptide environment.
alignToMaster(
ref,
eXp,
alignedVecs,
refRun,
adaptiveRT,
multipeptide,
prec2chromIndex,
mzPntrs,
fileInfo,
precursors,
params,
applyFun = lapply
)
ref |
(string) name of the descendant run. Must be in the rownames of fileInfo. |
eXp |
(string) name of one of the parent run. Must be in the rownames of fileInfo. |
alignedVecs |
(list of dataframes) Each dataframe contains aligned parents time-vectors and
resulting child/master time vector for a precursor. This is the second element of
|
refRun |
(integer) must be of the same length as of precursors. 1: reference is runA, 2: reference is runB. |
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. |
fileInfo |
(data-frame) output of |
precursors |
(data-frame) atleast two columns transition_group_id and transition_ids are required. |
params |
(list) parameters are entered as list. Output of the |
applyFun |
(function) value must be either lapply or BiocParallel::bplapply. |
refRun is flipped if eXp is runB instead of runA.
(None)
Shubham Gupta, shubh.gupta@mail.utoronto.ca
ORCID: 0000-0003-3500-8152
License: (c) Author (2020) + GPL-3 Date: 2020-07-19
traverseUp, traverseDown, setAlignmentRank
dataPath <- system.file("extdata", package = "DIAlignR")
params <- paramsDIAlignR()
fileInfo <- DIAlignR::getRunNames(dataPath = dataPath)
mzPntrs <- list2env(getMZMLpointers(fileInfo))
features <- list2env(getFeatures(fileInfo, maxFdrQuery = 0.05, runType = "DIA_Proteomics"))
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)
prec2chromIndex <- list2env(getChromatogramIndices(fileInfo, precursors, mzPntrs))
adaptiveRTs <- new.env()
refRuns <- new.env()
tree <- ape::reorder.phylo(ape::read.tree(text = "(run1:7,run2:2)master1;"), "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)
alignedVecs <- readRDS(file = file.path(dataPath, "master1_av.rds"))
adaptiveRT <- (adaptiveRTs[["run1_run2"]] + adaptiveRTs[["run2_run1"]])/2
multipeptide[["14383"]]$alignment_rank[multipeptide[["14383"]]$run == "master1"] <- 1L
multipeptide <- alignToMaster(ref = "master1", eXp = "run1", alignedVecs, refRuns[["master1"]][,1], adaptiveRT,
multipeptide, prec2chromIndex, mzPntrs, fileInfo, precursors, params)
# Cleanup
for(run in names(mzPntrs)) DBI::dbDisconnect(mzPntrs[[run]])
file.remove(file.path(dataPath, "master1_av.rds"))
file.remove(file.path(dataPath, "xics", "master1.chrom.sqMass"))
## End(Not run)
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