# Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics

## Documentation for package ‘msPurity’ version 1.37.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

assessPuritySingle | Assess the purity of a single LC-MS/MS or DI-MS/MS file
averageAllFragSpectra | Using a purityA object, average and filter MS/MS spectra for each XCMS feature within and across MS data files (ignoring intra and inter relationships)
averageAllFragSpectra-method | Using a purityA object, average and filter MS/MS spectra for each XCMS feature within and across MS data files (ignoring intra and inter relationships)
averageInterFragSpectra | Using a purityA object, average and filter fragmentation spectra for each XCMS feature across multiple MS data files
averageInterFragSpectra-method | Using a purityA object, average and filter fragmentation spectra for each XCMS feature across multiple MS data files
averageIntraFragSpectra | Using a purityA object, average and filter fragmentation spectra for each XCMS feature within a MS data file
averageIntraFragSpectra-method | Using a purityA object, average and filter fragmentation spectra for each XCMS feature within a MS data file
averageSpectra | Using purityD object, calculates to average mz, intensity and signal-to-noise of multiple scans from multiple MS datafiles (mzML or .csv)
averageSpectra-method | Using purityD object, calculates to average mz, intensity and signal-to-noise of multiple scans from multiple MS datafiles (mzML or .csv)
averageSpectraSingle | Calculates to average mz, intensity and signal-to-noise of multiple scans from 1 MS datafile (mzML or .csv)
combineAnnotations | Combine Annotations
createDatabase | Create database
createMSP | Using a purityA object, create an MSP file of fragmentation spectra
createMSP-method | Using a purityA object, create an MSP file of fragmentation spectra
create_database | Create database deprecated
dimsPredictPurity | Using purityD object, assess anticipated purity from a DI-MS run
dimsPredictPurity-method | Using purityD object, assess anticipated purity from a DI-MS run
dimsPredictPuritySingle | Predict the precursor purity from a DI-MS dataset
filterFragSpectra | Filter fragmentation spectra associated with an XCMS feature
filterFragSpectra-method | Filter fragmentation spectra associated with an XCMS feature
filterp | Filter out peaks based on intensity and RSD criteria
filterp-method | Filter out peaks based on intensity and RSD criteria
flag_remove | Flag and remove unwanted peaks
frag4feature | Using a purityA object, link MS/MS data to XCMS features
frag4feature-method | Using a purityA object, link MS/MS data to XCMS features
Getfiles | Get files for DI-MS processing
getP | Get peaklist for a purityD object
getP-method | Get peaklist for a purityD object
get_additional_mzml_meta | Get additional mzML meta
groupPeaks | Using purityD object, group multiple peaklists by similar mz values (mzML or .csv)
groupPeaks-method | Using purityD object, group multiple peaklists by similar mz values (mzML or .csv)
groupPeaksEx | Group peaklists from a list of dataframes
initialize-method | Constructor for S4 class to represent a DI-MS purityD
iwNormGauss | Gaussian normalisation for isolation window efficiency
iwNormQE.5 | Q-Exactive +/- 0.5 range, normalisation for isolation window efficiency
iwNormRcosine | Raised cosine normalisation for isolation window efficiency
pcalc | Perform purity calculation on a peak matrix
purityA | Assess the acquired precursor ion purity of MS/MS spectra (constructor)
purityD | An S4 class to represent a DI-MS purityD
purityD-class | An S4 class to represent a DI-MS purityD
purityX | Assessing anticipated purity of XCMS features from an LC-MS run
show-method | Show method for purityA class
show-method | Show method for purityD
show-method | Show method for purityX
spectralMatching | Spectral matching for LC-MS/MS datasets
spectral_matching | Spectral matching deprecated
subtract | Using Subtract MZ values based on ppm tolerance and noise ratio
subtract-method | Using Subtract MZ values based on ppm tolerance and noise ratio
subtractMZ | Subtract MZ values based on ppm tolerance and noise ratio
validate | Validate precursor purity predictions using LC-MS and LC-MS/MS dataset
validate-method | Validate precursor purity predictions using LC-MS and LC-MS/MS dataset
writeOut | Using purityD object, save peaks as text files
writeOut-method | Using purityD object, save peaks as text files