metID.optimConsensus: differential evolution build consensus weight optimization

In WMBEdmands/CompMS2miner: an automatable metabolite identification, visualization and data-sharing R package for high-resolution LC-MS datasets

Description

differential evolution build consensus weight optimization

Usage

metID.optimConsensus(object, ...)

Arguments

object	a "compMS2" class object.
...	additional arguments to DEoptim.control.
include	character vector of 7 options to build consensus combinatorial metabolite identification see Details below for a description of each. If specific options are not supplied as a character vector then the default is to consider all 7. i.e. c('massAccuracy', 'spectralDB', 'inSilico', 'rtPred', 'chemSim', 'pubMed', 'substructure').
autoPossId	logical if TRUE the function will automatically add the names of the top annotation based on mean consensus annotation score to the "metID comments" table (default = FALSE). Caution if TRUE this will overwrite any existing possible_identities in the "metID comments" table. This functionality is intended as an automatic metabolite annotation identification tool prior to thorough examination of the data in compMS2Explorer as part of an objective and seamless first-pass annotation workflow. The mean build consensus score can consist of many orthogonal measurements of metabolite identification and a means to rapidly rank metabolite annotations.
minMeanBCscore	numeric minimum mean consensus score (values between 0-1), if argument autoPossId is TRUE any metabolite annotations above this value will be automatically added to the "metID comments" table. (if argument not supplied the default is the upper interquartile range of the mean BC score).
possContam	numeric how many times does a possible annotation have to appear in the automatically generated possible annotations for it to be considered a contaminant and therefore not added to the "metID comment" table (default = 3, i.e. if a database name appears more than 3 times in the automatic annotation table it will be removed).
specDbOnly	logical if TRUE then only spectra matched to a spectral database will be considered. These annotations are identified by the flag "metID.matchSpectralDB" in the comments table. The default FALSE means that all metabolites in the metID comments table will be considered.
popSize	numeric number of population members (see the NP argument in DEoptim.control). The default is 10 * length(include).
itermax	numeric the maximum number of iterations (population generation) allowed. (default = 100). See DEoptim.control for further details.
plotInterval	the number of iterations before plotting the algorithms progress (default=20). Smaller values may slightly slow the process.

Details

uses the package DEoptim to calculate the optimum weighting of all included consensus scores to accurately rank the known annotations (taken from the "metID comments" table in compMS2Explorer). These global parameters can then be used to rank the unknown annotations of other unannotated composite spectra based on the optimum weighted mean consensus score. The annotations above a certain score (minMeanBCscore) can also be automatically added to the "metID comments" table. The ongoing differential evolution process will appear in a plot window with a loess fit line in red highlighting any reduction in the mean rank of the training set annotations (from "metID comments" table) as the genetic process evolves. This metaheuristic global optimization process can help to maximise the parameters for accurate metabolite annotation and ranking.

References

1. David Ardia, Katharine M. Mullen, Brian G. Peterson, Joshua Ulrich (2015). 'DEoptim': Differential Evolution in 'R'. version 2.2-3.

2. Katharine Mullen, David Ardia, David Gil, Donald Windover, James Cline (2011). 'DEoptim': An R Package for Global Optimization by Differential Evolution. Journal of Statistical Software, 40(6), 1-26. URL http://www.jstatsoft.org/v40/i06/.

3. Ardia, D., Boudt, K., Carl, P., Mullen, K.M., Peterson, B.G. (2010). Differential Evolution with 'DEoptim': An Application to Non-Convex Portfolio Optimization. The R Journal, 3(1), 27-34. URL http://journal.r-project.org/archive/2011-1/2011-1_index.html.

4. Ardia, D., Ospina Arango, N., Giraldo Gomez, N. (2010). Jump-Diffusion Calibration using Differential Evolution. Wilmott Magazine, Issue 55 (September), 76-79. URL http://www.wilmott.com/.

5. Kenneth V. Price, Rainer M. Storn and Jouni A. Lampinen (2006). Differential Evolution - A Practical Approach to Global Optimization. Berlin Heidelberg: Springer-Verlag. ISBN 3540209506.

Examples

compMS2Example <- metID(compMS2Example, 'optimConsensus')

WMBEdmands/CompMS2miner documentation built on May 9, 2019, 10:02 p.m.