ASCII_MSclust

This demonstration dataset includes 2 GC-MS analyses of Petrel, representing a total of 67 chromatogram's peaks. It can be used with the function MS.clust: (i) to identify the optimal number of clusters.(ii) to obtain the fingerprinting matrix (absence or presence of peaks for all samples)

datasets

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 can
read several format (ASCII, CDF, mzML, mzXML or mzData).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

Yann Guitton

MSeasy

Preprocessing of Gas Chromatography-Mass Spectrometry (GC-MS)
data

ASCII_MSclust function

A data frame with 67 chromatogram's peaks from 2 GC-MS analyses.
 <ul>
<li><code>header line </code> the first row contains columns' names
 </li>
<li><code>first column </code> name of the sample/analysis
 </li>
<li><code>second column </code> retention time of the peak 
 </li>
<li><code>following columns </code> mean relative mass spectrum of the peak (the intensity of one mass fragment (m/z) per column; Mean mass spectrum calculated by averaging 5 percent of the mass spectra surrounding the apex; The intensity of each mass fragment is transformed to a relative percentage of the highest mass fragment per spectrum)
 </li>
</ul>

ASCII_MSclust: Demonstration dataset for MS.clust

Description

Usage

Arguments

Format

Examples