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Bioinformatics Advance Access published online on September 27, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl492
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received July 31, 2006
Revised September 5, 2006
Accepted September 18, 2006

Article

A scalable method for integration and functional analysis of multiple microarray data sets

Curtis Huttenhower 1, Matt Hibbs 1, Chad Myers 1, and Olga G. Troyanskaya 1

1 Department of Computer Science, Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ


  Abstract

Motivation: The diverse microarray data sets that have become available over the past several years represent a rich opportunity and challenge for biological data mining. Many supervised and unsupervised methods have been developed for the analysis of individual microarray datasets. However, integrated analysis of multiple datasets can provide a broader insight into genetic regulation of specific biological pathways under a variety of conditions.

Results: To aid in the analysis of such large compendia of microarray experiments, we present MEFIT, a scalable Bayesian framework for predicting functional relationships from integrated microarray data sets. Furthermore, MEFIT predicts these functional relationships within the context of specific biological processes. All results are provided in the context of one or more specific biological functions, which can be provided by a biologist or drawn automatically from catalogues such as the Gene Ontology. Using MEFIT, we integrated 40 S. cerevisiae microarray data sets spanning 712 unique conditions. In tests based on 110 biological functions drawn from the GO biological process ontology, MEFIT provided a 5% or greater performance increase for 54 functions, with a 5% or more decrease in performance in only 2 functions.

Availability: Supplemental data, a collection of predictions made by MEFIT, and software implementing MEFIT are available online at http://function.princeton.edu/mefit/.

Associate Editor: John Quackenbush
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