Adaptive processing of LC-MS data

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Documentation for package ‘recetox.aplcms’ version 0.9.4

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recetox.aplcms-package Adaptive processing of LC/MS data
adaptive.bin Adaptive binning
adaptive.bin.2 Adaptive binning specifically for the machine learning approach.
adduct.table A table of potential adducts.
adjust.time Adjust retention time across spectra.
aligned sample data after alignment
cdf.to.ftr Convert a number of cdf files in the same directory to a feature table
combine.seq.3 An internal function.
cont.index Continuity index
draw.cfd.2d Plot the data in the m/z and retention time plane.
draw.txt.2d Plot the data in the m/z and retention time plane.
eic.disect Internal function: Extract data feature from EIC.
EIC.plot Plot extracted ion chromatograms
EIC.plot.learn Plot extracted ion chromatograms based on the machine learning method output
eic.pred Internal function: calculate the score for each EIC based on prediction of match status.
eic.qual Internal function: Calculate the single predictor quality.
feature.align Align peaks from spectra into a feature table.
features Sample feature tables from 4 profiles
features.learn Sample feature tables from 4 profiles. The original feature detection is done by machine learning approach.
features2 Feature tables after elution time correction.
features2.learn Feature tables after elution time correction. The original feature detection is done by machine learning approach.
find.match Internal function: finding the best match between a set of detected features and a set of known features.
find.tol An internal function that is not supposed to be directly accessed by the user. Find m/z tolerance level.
find.tol.time An internal function that is not supposed to be directly accessed by the user. Find elution time tolerance level.
find.turn.point Find peaks and valleys of a curve.
hybrid Runs features extraction in hybrid mode.
interpol.area Interpolate missing intensities and calculate the area for a single EIC.
known.table.common.pos A known feature table based on HMDB.
known.table.hplus A known feature table based on HMDB.
learn.cdf Peak detection using the machine learning approach.
load.lcms Loading LC/MS data.
make.known.table Producing a table of known features based on a table of metabolites and a table of allowable adducts.
metabolite.table A known metabolite table based on HMDB.
new.aligned Feature data after alignment and weak signal recovery
new.aligned.learn Feature data after alignment and weak signal recovery. The initial peak detection is done by machine learning approach.
peak.characterize Internal function: Updates the information of a feature for the known feature table.
present.cdf.3d Generates 3 dimensional plots for LCMS data.
proc.cdf Filter noise and detect peaks from LC/MS data in CDF format
proc.txt Filter noise and detect peaks from LC/MS data in text format
prof Sample profile data after noise filtration by the run filter
prof.learn Sample profile data after noise filtration by the machine learning approach.
prof.to.features Generate feature table from noise-removed LC/MS profile
recetox.aplcms Adaptive processing of LC/MS data
recover.weaker Recover weak signals in some profiles that is not identified as a peak, but corresponds to identified peaks in other spectra.
recovered Sample data after weak signal recovery
recovered.learn Sample data after weak signal recovery. The original peak detection was conducted using machine learning approach
rm.ridge Removing long ridges at the same m/z.
semi.sup Semi-supervised feature detection
semi.sup.learn Semi-supervised feature detection using machine learning approach.
target.search Targeted search of metabolites with given m/z and (optional) retention time
two.step.hybrid Two step hybrid feature detection.
unsupervised Runs features extraction in unsupervised mode.