Bioinformatics Advance Access originally published online on July 20, 2009
Bioinformatics 2009 25(20):2677-2684; doi:10.1093/bioinformatics/btp442
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Multi-dimensional correlations for gene coexpression and application to the large-scale data of Arabidopsis
1 Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639 and 2 Bioinformatics Research and Development, Japan Science and Technology Corporation, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
* To whom correspondence should be addressed.
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Abstract |
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Background: Recent improvements in DNA microarray techniques have made a large variety of gene expression data available in public databases. This data can be used to evaluate the strength of gene coexpression by calculating the correlation of expression patterns among different genes between many experiments. However, gene expression levels differ significantly across various tissues in higher organisms, as well as in different cellular location in eukaryotes in different cell state. Thus the usual correlation measure can only evaluate the difference of tissues or cellular localizations, and cannot adequately elucidate the functional relationship from the coexpression of genes.
Method: We propose a new measure of coexpression by expanding the generally used correlation into a multidimensional one. We used principal component analyses to identify the major factors of gene expression correlation, and then re-calculate the correlation by subtracting the major components in order to remove biases cased by a few experiments. The repeated subtractions of the major components yielded a set of correlation values for each pair of genes. We observed the correlation changes when the first ten principal components were subtracted step-by-step in large-scale Arabidopsis expression data.
Results: We found two extreme patterns of correlation changes, corresponding to stable and fragile coexpression. Our new indexes provided a good means to determine the functional relationships of the genes, by examining a few examples, and higher performance of Gene Ontology term prediction by using the support vector machine and the multidimensional correlation.
Availability: The results are available from the expression detail pages in ATTED-II (http://atted.jp).
Contact: kinosita{at}hgc.jp
Supplementary information: Supplementary data are available at Bioinformatics online.
Received on February 24, 2009; revised on June 22, 2009; accepted on July 13, 2009
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