Simple integrative preprocessing preserves what is shared in data sources
<p>Abstract</p> <p>Background</p> <p>Bioinformatics data analysis toolbox needs general-purpose, fast and easily interpretable preprocessing tools that perform data integration during exploratory data analysis. Our focus is on vector-valued data sources, each consisting...
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| Format: | Article |
| Language: | English |
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BMC
2008-02-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/9/111 |
| _version_ | 1818163406486634496 |
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| author | Klami Arto Tripathi Abhishek Kaski Samuel |
| author_facet | Klami Arto Tripathi Abhishek Kaski Samuel |
| author_sort | Klami Arto |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Bioinformatics data analysis toolbox needs general-purpose, fast and easily interpretable preprocessing tools that perform data integration during exploratory data analysis. Our focus is on vector-valued data sources, each consisting of measurements of the same entity but on different variables, and on tasks where source-specific variation is considered noisy or not interesting. Principal components analysis of all sources combined together is an obvious choice if it is not important to distinguish between data source-specific and shared variation. Canonical Correlation Analysis (CCA) focuses on mutual dependencies and discards source-specific "noise" but it produces a separate set of components for each source.</p> <p>Results</p> <p>It turns out that components given by CCA can be combined easily to produce a linear and hence fast and easily interpretable feature extraction method. The method fuses together several sources, such that the properties they share are preserved. Source-specific variation is discarded as uninteresting. We give the details and implement them in a software tool. The method is demonstrated on gene expression measurements in three case studies: classification of cell cycle regulated genes in yeast, identification of differentially expressed genes in leukemia, and defining stress response in yeast. The software package is available at <url>http://www.cis.hut.fi/projects/mi/software/drCCA/</url>.</p> <p>Conclusion</p> <p>We introduced a method for the task of data fusion for exploratory data analysis, when statistical dependencies between the sources and not within a source are interesting. The method uses canonical correlation analysis in a new way for dimensionality reduction, and inherits its good properties of being simple, fast, and easily interpretable as a linear projection.</p> |
| first_indexed | 2024-12-11T16:49:03Z |
| format | Article |
| id | doaj.art-96332db2105341cdae85316e7831f8c5 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T16:49:03Z |
| publishDate | 2008-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-96332db2105341cdae85316e7831f8c52022-12-22T00:58:08ZengBMCBMC Bioinformatics1471-21052008-02-019111110.1186/1471-2105-9-111Simple integrative preprocessing preserves what is shared in data sourcesKlami ArtoTripathi AbhishekKaski Samuel<p>Abstract</p> <p>Background</p> <p>Bioinformatics data analysis toolbox needs general-purpose, fast and easily interpretable preprocessing tools that perform data integration during exploratory data analysis. Our focus is on vector-valued data sources, each consisting of measurements of the same entity but on different variables, and on tasks where source-specific variation is considered noisy or not interesting. Principal components analysis of all sources combined together is an obvious choice if it is not important to distinguish between data source-specific and shared variation. Canonical Correlation Analysis (CCA) focuses on mutual dependencies and discards source-specific "noise" but it produces a separate set of components for each source.</p> <p>Results</p> <p>It turns out that components given by CCA can be combined easily to produce a linear and hence fast and easily interpretable feature extraction method. The method fuses together several sources, such that the properties they share are preserved. Source-specific variation is discarded as uninteresting. We give the details and implement them in a software tool. The method is demonstrated on gene expression measurements in three case studies: classification of cell cycle regulated genes in yeast, identification of differentially expressed genes in leukemia, and defining stress response in yeast. The software package is available at <url>http://www.cis.hut.fi/projects/mi/software/drCCA/</url>.</p> <p>Conclusion</p> <p>We introduced a method for the task of data fusion for exploratory data analysis, when statistical dependencies between the sources and not within a source are interesting. The method uses canonical correlation analysis in a new way for dimensionality reduction, and inherits its good properties of being simple, fast, and easily interpretable as a linear projection.</p>http://www.biomedcentral.com/1471-2105/9/111 |
| spellingShingle | Klami Arto Tripathi Abhishek Kaski Samuel Simple integrative preprocessing preserves what is shared in data sources BMC Bioinformatics |
| title | Simple integrative preprocessing preserves what is shared in data sources |
| title_full | Simple integrative preprocessing preserves what is shared in data sources |
| title_fullStr | Simple integrative preprocessing preserves what is shared in data sources |
| title_full_unstemmed | Simple integrative preprocessing preserves what is shared in data sources |
| title_short | Simple integrative preprocessing preserves what is shared in data sources |
| title_sort | simple integrative preprocessing preserves what is shared in data sources |
| url | http://www.biomedcentral.com/1471-2105/9/111 |
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