assign.z: assign.z

Description Usage Arguments Details Value Author(s) References

View source: R/assign.z.R

Description

infer charge state of features in ramclustR object.

Usage

1
2
assign.z(ramclustObj = RC, chargestate = c(1:5), mzError = 0.02,
  nEvents = 2, minPercentSignal = 10, assume1 = TRUE)

Arguments

ramclustObj

ramclustR object to annotate

chargestate

integer vector. vector of integers of charge states to look for. default = c(1:5)

mzError

numeric. the error allowed in charge state m/z filtering. absolute mass units

nEvents

integer. the number of isotopes necessary to assign a charnge state > 1. default = 2.

minPercentSignal

numeric. the ratio of isotope signal (all isotopes) divided by total spectrum signal * 100 much be greated than minPercentSignal to evaluate charge state. Value should be between 0 and 100.

assume1

logical. when TRUE, m/z values for which no isotopes are found are assumed to be at z = 1.

Details

Annotation of ramclustR spectra. looks at isotope spacing for clustered features to infer charge state for each feature and a max charge state for each compound

Value

returns a ramclustR object. new slots holding:

zmax. vector with length equal to number of compounds. max charge state detected for that compound

fm. vector of inferred 'm', m/z value * z value

fz. vector of inferred 'z' values based on analysis of isotopes in spectrum.

Author(s)

Corey Broeckling

References

Broeckling CD, Afsar FA, Neumann S, Ben-Hur A, Prenni JE. RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data. Anal Chem. 2014 Jul 15;86(14):6812-7. doi: 10.1021/ac501530d. Epub 2014 Jun 26. PubMed PMID: 24927477.

Broeckling CD, Ganna A, Layer M, Brown K, Sutton B, Ingelsson E, Peers G, Prenni JE. Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction. Anal Chem. 2016 Sep 20;88(18):9226-34. doi: 10.1021/acs.analchem.6b02479. Epub 2016 Sep 8. PubMed PMID: 7560453.


sneumann/RAMClustR documentation built on June 22, 2018, 4:22 p.m.