Limits...
Simple integrative preprocessing preserves what is shared in data sources.

Tripathi A, Klami A, Kaski S - BMC Bioinformatics (2008)

Bottom Line: 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.We give the details and implement them in a software tool.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, P,O, Box 68, FI-00014, University of Helsinki, Finland. abhishek@cs.helsinki.fi

ABSTRACT

Background: 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.

Results: 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 http://www.cis.hut.fi/projects/mi/software/drCCA/.

Conclusion: 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.

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Mutual Information. Mutual information for two data sets as a function of the reduced dimensionality. Each subgraph represents mutual information curve for two data sets corresponding to each data collection. The curves for other pairs in each data collection show a similar pattern.
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Figure 1: Mutual Information. Mutual information for two data sets as a function of the reduced dimensionality. Each subgraph represents mutual information curve for two data sets corresponding to each data collection. The curves for other pairs in each data collection show a similar pattern.

Mentions: based on the assumption of normally distributed data. Consequently we started by confining to pairs of data sources. Figure 1 shows the results for one of the pairs in each collection; the rest are analogous. It is evident that the method retains the shared variation between data sets and the shared variation increases with increasing number of dimensions in the combined data.


Simple integrative preprocessing preserves what is shared in data sources.

Tripathi A, Klami A, Kaski S - BMC Bioinformatics (2008)

Mutual Information. Mutual information for two data sets as a function of the reduced dimensionality. Each subgraph represents mutual information curve for two data sets corresponding to each data collection. The curves for other pairs in each data collection show a similar pattern.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2278131&req=5

Figure 1: Mutual Information. Mutual information for two data sets as a function of the reduced dimensionality. Each subgraph represents mutual information curve for two data sets corresponding to each data collection. The curves for other pairs in each data collection show a similar pattern.
Mentions: based on the assumption of normally distributed data. Consequently we started by confining to pairs of data sources. Figure 1 shows the results for one of the pairs in each collection; the rest are analogous. It is evident that the method retains the shared variation between data sets and the shared variation increases with increasing number of dimensions in the combined data.

Bottom Line: 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.We give the details and implement them in a software tool.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, P,O, Box 68, FI-00014, University of Helsinki, Finland. abhishek@cs.helsinki.fi

ABSTRACT

Background: 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.

Results: 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 http://www.cis.hut.fi/projects/mi/software/drCCA/.

Conclusion: 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.

Show MeSH
Related in: MedlinePlus