getPrecursors | R Documentation |
Get a data-frame of analytes' transition_group_id, transition_ids, peptide_id and amino-acid sequences.
getPrecursors(
fileInfo,
oswMerged = TRUE,
runType = "DIA_Proteomics",
context = "global",
maxPeptideFdr = 0.05,
level = "Peptide",
useIdentifying = FALSE
)
fileInfo |
(data-frame) Output of |
oswMerged |
(logical) TRUE for experiment-wide FDR and FALSE for run-specific FDR by pyprophet. |
runType |
(char) This must be one of the strings "DIA_Proteomics", "DIA_IPF", "DIA_Metabolomics". |
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) A data-frame having following columns:
transition_group_id |
(integer) a unique id for each precursor. |
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. |
transition_ids |
(list) fragment-ion ID associated with transition_group_id. This is matched with chromatogram ID in mzML file. |
Shubham Gupta, shubh.gupta@mail.utoronto.ca
ORCID: 0000-0003-3500-8152
License: (c) Author (2019) + GPL-3 Date: 2019-04-06
getRunNames, fetchPrecursorsInfo
dataPath <- system.file("extdata", package = "DIAlignR")
fileInfo <- getRunNames(dataPath = dataPath)
precursorsInfo <- getPrecursors(fileInfo, oswMerged = TRUE, runType = "DIA_Proteomics",
context = "experiment-wide", maxPeptideFdr = 0.05)
dim(precursorsInfo) # 234 6
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