decBaitMet: Library-driven deconvolution of compounds by BaitMet

In baitmet: Library Driven Compound Profiling in Gas Chromatography - Mass Spectrometry Data

Description

Library-driven deconvolution of GC-MS data by BaitMet

Usage

decBaitMet(Experiment, BaitParameters, 
ms.library=mslib, chrom.method, 
samples.to.process=NULL)

Arguments

Experiment	A 'MetaboSet' S4 object containing the experiment data previously created by newExp.
BaitParameters	The BaitMet deconvolution parameters object previously created by setBaitPar
ms.library	The mass-spectra library to be used for retention time and spectral comparison. By default, the MassBank [2] - Mass Bank of North America (MoNa) database is employed. However, the Golm Metabolome Database [3] is highly recommended.
chrom.method	The chromatographic method previously created by setChrmMethod.
samples.to.process	Vector indicating which samples are to be processed.

Details

See BaitMet vignette for more details. To open the vignette, execute the following code in R: vignette("BaitMetManual", package="baitmet")

Value

The function returns an updated S4 'MetaboSet' class, where the compounds in the library have been searched in the GC-MS samples and deconvolved.

Author(s)

Xavier Domingo-Almenara. [e-mail]

References

[1] Targeting the untargeted: BaitMet, an R package for GC-MS library-driven compound profiling in metabolomics. Xavier Domingo-Almenara, Alexandre Perera-Lluna, Gabriel Vivo-Truyols, Gabriela Venturini, Maria Vinaixa, Jesus Brezmes. (2016) Submitted.

[2] MassBank: A public repository for sharing mass spectral data for life sciences, H. Horai, M. Arita, S. Kanaya, Y. Nihei, T. Ikeda, K. Suwa. Y. Ojima, K. Tanaka, S. Tanaka, K. Aoshima, Y. Oda, Y. Kakazu, M. Kusano, T. Tohge, F. Matsuda, Y. Sawada, M. Yokota Hirai, H. Nakanishi, K. Ikeda, N. Akimoto, T. Maoka, H. Takahashi, T. Ara, N. Sakurai, H. Suzuki, D. Shibata, S. Neumann, T. Iida, K. Tanaka, K. Funatsu, F. Matsuura, T. Soga, R. Taguchi, K. Saito and T. Nishioka, J. Mass Spectrom., 45 (2010) 703-714.

[3] Hummel J, Strehmel N, Selbig J, Walther D, Kopka J. Decision tree supported sub-structure prediction of metabolites from GC-MS profiles. Metabolomics, 6 (2010) 322-333.

See Also

newExp, setChrmMethod, setBaitPar

Examples

# Library-driven deconvolution from an experiment created by \code{\link{newExp}}.

# ex <- newExp(instrumental="path")

# Now, we have to set up a chromatographic method:

Chrm.PLASMA <- setChrmMethod(method="alk.var5", 
rt=c(8.39, 10.759, 13.271, 15.604, 17.751, 19.685, 
21.471, 23.126, 24.645), ri=c(1225.27, 1326.95, 1526.31, 
1729.57, 1928.52, 2131.7, 2342.06, 2548.56, 2739.86), 
name="Test Chrm Method")

# The following will set BaitMet for analyzing the chromatograms
# without taking into account the masses 1:69,73:75,147:149, 
# (since is the mass range of the Golm Metabolome Database)
# with a minimum peak width of 2 seconds.

ext.par <- setBaitPar(ri.error=0.05, min.peak.width=2,  
min.peak.height=1000, noise.threshold=100, 
avoid.processing.mz=c(1:69,73:75,147:149))

# An now deconvolve the compounds in the samples:
# ex <- decBaitMet(ex, ext.par, chrom.method=Chrm.PLASMA)

baitmet documentation built on May 2, 2019, 11 a.m.