# Adaptive processing of LC-MS data

## Documentation for package ‘recetox.aplcms’ version 0.9.0

- DESCRIPTION file.

## Help Pages

apLCMS-package | Adaptive processing of LC/MS data
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
apLCMS | Adaptive processing of LC/MS data
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
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.
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.
merge.seq.3 | An internal function.
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.
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.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
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.