An approach for feature selection with data modelling in LC-MS metabolomics
The data processing workflow for LC-MS based metabolomics study is suggested with signal drift correction, univariate analysis, supervised learning, feature selection and unsupervised modelling. The proposed approach requires only an annotation-free peak table and produces an extremely reduced set o...
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| Veröffentlicht in: | Analytical methods 2020-07, Vol.12 (28), p.3582-3591 |
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| container_title | Analytical methods |
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| creator | Plyushchenko, Ivan Shakhmatov, Dmitry Bolotnik, Timofey Baygildiev, Timur Nesterenko, Pavel N Rodin, Igor |
| description | The data processing workflow for LC-MS based metabolomics study is suggested with signal drift correction, univariate analysis, supervised learning, feature selection and unsupervised modelling. The proposed approach requires only an annotation-free peak table and produces an extremely reduced set of the most relevant features together with validation
via
Receiver Operating Characteristic analysis for selected predictors, cross-validation and unsupervised projection. The presented study was initially optimised by its own experimental set and then was successfully tested by using 36 datasets from 21 publicly available metabolomics projects. The suggested workflow can be used for classification purposes in high dimensional metabolomics studies and as a first step in exploratory analysis, data projection, biomarker selection, data integration and fusion.
The data processing workflow for LC-MS based metabolomics study is suggested with signal drift correction, univariate analysis, supervised learning, feature selection and unsupervised modelling. |
| doi_str_mv | 10.1039/d0ay00204f |
| format | Article |
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via
Receiver Operating Characteristic analysis for selected predictors, cross-validation and unsupervised projection. The presented study was initially optimised by its own experimental set and then was successfully tested by using 36 datasets from 21 publicly available metabolomics projects. The suggested workflow can be used for classification purposes in high dimensional metabolomics studies and as a first step in exploratory analysis, data projection, biomarker selection, data integration and fusion.
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via
Receiver Operating Characteristic analysis for selected predictors, cross-validation and unsupervised projection. The presented study was initially optimised by its own experimental set and then was successfully tested by using 36 datasets from 21 publicly available metabolomics projects. The suggested workflow can be used for classification purposes in high dimensional metabolomics studies and as a first step in exploratory analysis, data projection, biomarker selection, data integration and fusion.
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Shakhmatov, Dmitry ; Bolotnik, Timofey ; Baygildiev, Timur ; Nesterenko, Pavel N ; Rodin, Igor</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-12532b8e1b64f81eef1930b16fe3ac4204883961a417c9a83532776f3d311c953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Annotations</topic><topic>Biomarkers</topic><topic>Chromatography, Liquid</topic><topic>Data integration</topic><topic>Data processing</topic><topic>Feature selection</topic><topic>Metabolomics</topic><topic>Metabolomics - methods</topic><topic>Modelling</topic><topic>Models, Biological</topic><topic>Reproducibility of Results</topic><topic>Signal processing</topic><topic>Software</topic><topic>Tandem Mass Spectrometry</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Plyushchenko, Ivan</creatorcontrib><creatorcontrib>Shakhmatov, Dmitry</creatorcontrib><creatorcontrib>Bolotnik, Timofey</creatorcontrib><creatorcontrib>Baygildiev, Timur</creatorcontrib><creatorcontrib>Nesterenko, Pavel N</creatorcontrib><creatorcontrib>Rodin, Igor</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Analytical methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Plyushchenko, Ivan</au><au>Shakhmatov, Dmitry</au><au>Bolotnik, Timofey</au><au>Baygildiev, Timur</au><au>Nesterenko, Pavel N</au><au>Rodin, Igor</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An approach for feature selection with data modelling in LC-MS metabolomics</atitle><jtitle>Analytical methods</jtitle><addtitle>Anal Methods</addtitle><date>2020-07-28</date><risdate>2020</risdate><volume>12</volume><issue>28</issue><spage>3582</spage><epage>3591</epage><pages>3582-3591</pages><issn>1759-9660</issn><eissn>1759-9679</eissn><abstract>The data processing workflow for LC-MS based metabolomics study is suggested with signal drift correction, univariate analysis, supervised learning, feature selection and unsupervised modelling. The proposed approach requires only an annotation-free peak table and produces an extremely reduced set of the most relevant features together with validation
via
Receiver Operating Characteristic analysis for selected predictors, cross-validation and unsupervised projection. The presented study was initially optimised by its own experimental set and then was successfully tested by using 36 datasets from 21 publicly available metabolomics projects. The suggested workflow can be used for classification purposes in high dimensional metabolomics studies and as a first step in exploratory analysis, data projection, biomarker selection, data integration and fusion.
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| ispartof | Analytical methods, 2020-07, Vol.12 (28), p.3582-3591 |
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| source | MEDLINE; Royal Society Of Chemistry Journals 2008- |
| subjects | Annotations Biomarkers Chromatography, Liquid Data integration Data processing Feature selection Metabolomics Metabolomics - methods Modelling Models, Biological Reproducibility of Results Signal processing Software Tandem Mass Spectrometry Workflow |
| title | An approach for feature selection with data modelling in LC-MS metabolomics |
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