getGlobalFits {DIAlignR} | R Documentation |
Calculates all global alignment needed in refRun
getGlobalFits( refRun, features, fileInfo, globalAlignment, globalAlignmentFdr, globalAlignmentSpan, applyFun = lapply )
refRun |
(data-frame) Output of getRefRun function. Must have two columsn : transition_group_id and run. |
features |
(list of data-frames) it is output from getFeatures function. |
fileInfo |
(data-frame) Output of getRunNames function. |
globalAlignment |
(string) Must be from "loess" or "linear". |
globalAlignmentFdr |
(numeric) A numeric value between 0 and 1. Features should have m-score lower than this value for participation in global fit. |
globalAlignmentSpan |
(numeric) Spanvalue for LOESS fit. For targeted proteomics 0.1 could be used. |
(list) Each element is either of class lm or loess.
Shubham Gupta, shubh.gupta@mail.utoronto.ca
ORCID: 0000-0003-3500-8152
License: (c) Author (2020) + GPL-3 Date: 2020-04-19
getRefRun, getFeatures, getGlobalAlignment
dataPath <- system.file("extdata", package = "DIAlignR") fileInfo <- getRunNames(dataPath, oswMerged = TRUE) features <- getFeatures(fileInfo, maxFdrQuery = 0.05) precursors <- getPrecursors(fileInfo, TRUE, "DIA_Proteomics", "experiment-wide", 0.01) precursors <- dplyr::arrange(precursors, .data$peptide_id, .data$transition_group_id) peptideIDs <- unique(precursors$peptide_id) peptideScores <- getPeptideScores(fileInfo, peptideIDs, TRUE, "DIA_Proteomics", "experiment-wide") peptideScores <- lapply(peptideIDs, function(pep) dplyr::filter(peptideScores, .data$peptide_id == pep)) names(peptideScores) <- as.character(peptideIDs) ## Not run: refRun <- getRefRun(peptideScores) fits <- getGlobalFits(refRun, features, fileInfo, "linear", 0.05, 0.1) ## End(Not run)