Adaptive Processing of LC-MS Data

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Documentation for package ‘apLCMS’ version 6.8.3

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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.
apLCMS Adaptive processing of LC/MS data
cdf.to.ftr Convert a number of cdf files in the same directory to a feature table
cont.index Continuity index
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
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.
interpol.area Interpolate missing intensities and calculate the area for a single EIC.
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.
mass.match An internal function: finding matches between two vectors of m/z values.
merge_seq_3 An internal function.
metabolite.table A known metabolite table based on HMDB.
peak.characterize Internal function: Updates the information of a feature for the known feature table.
plot_cdf_2d Plot the data in the m/z and retention time plane.
plot_txt_2d Plot the data in the m/z and retention time plane.
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.cdf.2d Compute a 2D Binned Kernel Density Estimate 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.to.features Generate feature table from noise-removed LC/MS profile
recover.weaker Recover weak signals in some profiles that is not identified as a peak, but corresponds to identified peaks in other spectra.
rm.ridge Removing long ridges at the same m/z.
semi.sup Semi-supervised feature detection
semi.sup.2d Semi-supervised feature detection using 2D peak 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.
two.step.hybrid.2d Two step hybrid feature detection using 2D peak detection.