fetchPrecursorsInfo {DIAlignR} | R Documentation |
Get a data-frame of analytes' transition_group_id, transition_ids, peptide_id and amino-acid sequences.
fetchPrecursorsInfo( filename, runType = "DIA_Proteomics", selectIDs = NULL, context = "global", maxPeptideFdr = 0.05, level = "Peptide", useIdentifying = FALSE )
filename |
(string) Should be from the RUN.FILENAME column from osw files. |
runType |
(string) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics". |
selectIDs |
(integer) a vector of integers. |
context |
(string) Context used in pyprophet peptide. Must be either "run-specific", "experiment-wide", or "global". |
maxPeptideFdr |
(numeric) A numeric value between 0 and 1. It is used to filter peptides from osw file which have SCORE_PEPTIDE.QVALUE less than itself. |
level |
(string) Apply maxPeptideFDR on Protein as well if specified as "Protein". Default: "Peptide". |
useIdentifying |
(logical) Set TRUE to use identifying transitions in alignment. (DEFAULT: FALSE) |
(data-frames) Data-frame has following columns:
transition_group_id |
(integer) a unique id for each precursor. |
transition_id |
(list) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file. |
peptide_id |
(integer) a unique id for each peptide. A peptide can have multiple precursors. |
sequence |
(string) amino-acid sequence of the precursor with possible modifications. |
charge |
(integer) charge on the precursor. |
group_label |
(string) TODO Figure it out. |
Shubham Gupta, shubh.gupta@mail.utoronto.ca
ORCID: 0000-0003-3500-8152
License: (c) Author (2019) + GPL-3 Date: 2019-04-04
getRunNames, getPrecursors, getPrecursorsQuery
dataPath <- system.file("extdata", package = "DIAlignR") filename <- paste0(dataPath,"/osw/merged.osw") ## Not run: precursorsInfo <- fetchPrecursorsInfo(filename, runType = "DIA_Proteomics", context = "experiment-wide") dim(precursorsInfo) # 234 6 ## End(Not run)