findIsotopesWithValidation-methods: Deconvolute/Annotate LC/ESI-MS data

Description Usage Arguments Details Author(s) References See Also Examples

Description

Annotate validated isotope clusters for a xsAnnotate object. Returns a xsAnnotate object with annotated isotopes. Validation of isotope clusters is based on statistics of the KEGG database implemented in S4 class object compoundQuantiles.

Usage

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  findIsotopesWithValidation(object, maxcharge=3, ppm=5, mzabs=0.01, intval=c("maxo","into","intb"), validateIsotopePatterns = TRUE, database="kegg")

Arguments

object

the xsAnnotate object

maxcharge

max. number of the isotope charge

ppm

ppm error for the search

mzabs

allowed variance for the search

intval

choose intensity values for C12/C13 check. Allowed values are into, maxo, intb

validateIsotopePatterns

logical, if TRUE putative isotope clusters are validated based on KEGG database statistics.

database

the database which is the basis for isotope cluster validation. One of compoundLibraries().

Details

Isotope peaks are annotated for a xsAnnotate object according to given rules (maxcharge, maxiso). The algorithm benefits from a earlier grouping of the data, with groupFWHM. Generates a list of all possible isotopes, which is stored in object@isotopes. Those isotope information will be used in the groupCorr funtion. The ratios between isotope peaks are checked against the mass–specific $99%$ confidence interval based on statistics of the KEGG database.

Author(s)

Hendrik Treutler <hendrik.treutler@ipb-halle.de>

References

Hendrik Treutler and Steffen Neumann. "Prediction, detection, and validation of isotope clusters in mass spectrometry data". Submitted to Metabolites 2016, Special Issue "Bioinformatics and Data Analysis".

See Also

findIsotopes

Examples

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 library(CAMERA)
 file <- system.file('mzdata/MM14.mzdata', package = "CAMERA")
 xs   <- xcmsSet(file, method="centWave", ppm=30, peakwidth=c(5,10))
 an   <- xsAnnotate(xs)
 an   <- groupFWHM(an)
 an   <- findIsotopesWithValidation(an)

CAMERA documentation built on Nov. 8, 2020, 4:59 p.m.