trans.ASCII
(Package: MSeasy) :
Transform GC-MS data in ASCII format to suitable data for MS.DataCreation
This function transform each ASCII file (i.e. each GC-MS analysis in ASCII format) into a new file compatible with MS.DataCreation.
● Data Source:
CranContrib
● Keywords:
● Alias: trans.ASCII
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SearchNIST
(Package: MSeasy) :
Identification of putative molecules using the NIST mass spectral library search tool
SearchNIST performs identification of putative molecules using the NIST mass spectral library search tool. The input file is a MSP file. It can be obtained directly from MSeasy output files by using the conversion function MSeasyToMSP. Tcltk package is needed. Warning: this function only works on Windows plateforms !
● Data Source:
CranContrib
● Keywords:
● Alias: SearchNIST
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MSeasyToMSP
(Package: MSeasy) :
Convert output files from MS.clust into MSP format for NIST mass spectral library search
MSeasyToMSP export mass spectra from the output files generated by MS.clust into a MSP file compatible with NIST mass spectral library search tool. It is possible to consider only mass spectra from a selected subset of clusters. tcltk package is needed.
● Data Source:
CranContrib
● Keywords:
● Alias: MSeasyToMSP
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MSeasyToARISTO
(Package: MSeasy) :
Convert output files from the function MS.clust into compatible format for ARISTO websearch url{http://www.ionspectra.org/aristo/batchmode/
MSeasyToARISTO convert the output files output_peak or output_cluster generated by MS.clust to the ARISTO webtool. ARISTO is a webtool that provides ontology of submitted compounds http://www.ionspectra.org/aristo/batchmode/. It is possible to consider only a subset of selected clusters.
● Data Source:
CranContrib
● Keywords:
● Alias: MSeasyToARISTO
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MSeasy-package
(Package: MSeasy) :
Unsupervised and untargeted processing of Gas Chromatography-Mass Spectrometry (GC-MS) data
Package for the detection of molecules in complex mixtures of compounds. It creates an initial_DATA matrix from several GC-MS analyses by collecting and assembling the information from chromatograms and mass spectra (MS.DataCreation). It tests for the best unsupervised clustering method to group similar mass spectra into molecules (MS.test.clust). It runs the optimal unsupervised clustering method on the initial_DATA matrix, identifies the optimal number of clusters, produces different files for facilitating the quality control and identification of putative molecules, and returns fingerprinting or profiling matrices (MS.clust). It converts output files from MS.clust for NIST mass spectral library search and ARISTO webtool search
● Data Source:
CranContrib
● Keywords:
● Alias: MSeasy-package
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MS.test.clust
(Package: MSeasy) :
Test for the best clustering method
This function tests the efficiency of several unsupervised clustering methods to group similar mass spectra from mass spectrometry (MS) data. Using a dataset where molecules are already well-identified and represented by several samples/individuals mass spectra, the clustering algorithms are tested for their ability to find the correct structure of the dataset (correctly assign the different mass spectra to the pre-defined molecules).
● Data Source:
CranContrib
● Keywords:
● Alias: MS.test.clust
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This function constructs a global matrix called initial_DATA.txt by collecting and assembling the information from chromatograms and mass spectra from several GC-MS analyses. It performs basic peak detection if the input file is in ASCII format. For other input files, peak retention times (or retention indices) are retrieved from the chromatograms (peaklist.txt or rteres.txt files) and associated to their respective mass spectrum (AIA/ANDI NetCDF, mzXML, mzData and mzML files). Each row of the output matrix represents one peak in one analysis and reports the sample name in first column, the peak retention time (or retention index) in second column and the mass spectrum of the peak in the following columns. If the input file is in Agilent format, two quantification measures of peak size can be extracted directly from rteres.txt: corrected area is then inserted in column 3 and percent of the total corrected area is placed in column 4 of initial_DATA.txt. If the input file is CDF, one or two quantification measures of peak size can be extracted from column 6 (quantification1) and 7 (quantification2) of peaklist.txt; values are then reported respectively in column 3 and 4 of initial_DATA.txt. Except for ASCII, xcms package is needed. Copy paste the following code to download xcms: source("http://bioconductor.org/biocLite.R");biocLite("xcms")
● Data Source:
CranContrib
● Keywords:
● Alias: MS.DataCreation
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MS.clust
(Package: MSeasy) :
Mass spectra clustering and creation of a fingerprinting or profiling matrix
MS.clust runs unsupervised clustering methods on mass spectra. It can identify the optimal number of clusters using a cluster validity index (silhouette width), produces different files for facilitating the quality control and identification of putative molecules within a complex dataset of numerous mass spectra, and returns a fingerprinting or profiling matrix for homogeneous clusters (see details below for the definition of homogeneous clusters).
● Data Source:
CranContrib
● Keywords:
● Alias: MS.clust
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