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MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data (online first)
by
Laptenok, S
, Vos, C.H. de
, Hall, R.D
, Bovy, A.G
, Tikunov, Y.M
in
Biochemistry
/ Biomedical and Life Sciences
/ Biomedicine
/ Cell Biology
/ Developmental Biology
/ Life Sciences
/ metabolism
/ metabolomics approach
/ Molecular Medicine
/ ms
/ peak alignment
/ plant metabolomics
/ Short Communication
/ tomato
/ volatiles
2012
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MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data (online first)
by
Laptenok, S
, Vos, C.H. de
, Hall, R.D
, Bovy, A.G
, Tikunov, Y.M
in
Biochemistry
/ Biomedical and Life Sciences
/ Biomedicine
/ Cell Biology
/ Developmental Biology
/ Life Sciences
/ metabolism
/ metabolomics approach
/ Molecular Medicine
/ ms
/ peak alignment
/ plant metabolomics
/ Short Communication
/ tomato
/ volatiles
2012
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Do you wish to request the book?
MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data (online first)
by
Laptenok, S
, Vos, C.H. de
, Hall, R.D
, Bovy, A.G
, Tikunov, Y.M
in
Biochemistry
/ Biomedical and Life Sciences
/ Biomedicine
/ Cell Biology
/ Developmental Biology
/ Life Sciences
/ metabolism
/ metabolomics approach
/ Molecular Medicine
/ ms
/ peak alignment
/ plant metabolomics
/ Short Communication
/ tomato
/ volatiles
2012
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MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data (online first)
Journal Article
MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data (online first)
2012
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Overview
Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust—a software tool for analysis GC–MS and LC–MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC–MS and LC–MS alignment data sets.
Publisher
Springer US,Springer Nature B.V
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