Package: MSeasy
Type: Package
Title: Preprocessing of Gas Chromatography-Mass Spectrometry (GC-MS)
data
Version: 5.3
Date: 2012-04-20
Depends: amap, clValid, cluster, fpc
Suggests: xcms, tcltk
Author: Elodie Courtois, Yann Guitton, Florence Nicole
Maintainer: <yann.guitton@gmail.com>
Description: 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 (\emph{MS.DataCreation}).It
tests for the best unsupervised clustering method to group
similar mass spectra into molecules (\emph{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 (\emph{MS.clust}).It
converts output files from \emph{MS.clust} for NIST mass
spectral library search and ARISTO webtool search
License: GPL-2
URL: http://sites.google.com/site/rpackagemseasy/
LazyLoad: yes
Packaged: 2012-05-04 07:46:36 UTC; Yann
Repository: CRAN
Date/Publication: 2012-05-04 09:47:32

Title: MSeasy Tcl/Tk Graphical User Interface
Description: A Tcl/Tk GUI for some basic functions in the MSeasy

Author: Yann Guitton <yann.guitton@gmail.com> Cyrille Conord

person("Matineh", "Rahmatbakhsh", , "matinerb.94@gmail.com", c("aut", "cre")))
Description: The 'MSiP' is a computational approach to predict protein-protein interactions from large-scale affinity purification mass 'spectrometry' (AP-MS) data. This approach includes both spoke and matrix models for interpreting AP-MS data in a network context. The "spoke" model considers only bait-prey interactions, whereas the "matrix" model assumes that each of the identified proteins (baits and prey) in a given AP-MS experiment interacts with each of the others. The spoke model has a high false-negative rate, whereas the matrix model has a high false-positive rate. Although, both statistical models have merits, a combination of both models has shown to increase the performance of machine learning classifiers in terms of their capabilities in discrimination between true and false positive interactions.