Description Usage Arguments Value Author(s) See Also Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | 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
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 32 33 | 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)
rm(mzPntrs)
|
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