Title: Genome-wide functional annotation by integrating multiple microarray datasets using meta-analysis

Authors: Gyan Prakash Srivastava, Jing Qiu, Dong Xu

Addresses: Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, 1201 E, Rollins Rd. Columbia, MO 65201, USA. ' Department of Statistics, University of Missouri-Columbia, 134 I, Middlebush Hall, Columbia, MO 65201, USA. ' Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri-Columbia, 1201 E. Rollins Rd. Columbia, MO 65201, USA

Abstract: Tremendous amounts of microarray data for various organisms have provided a rich opportunity for computational analyses of gene products. Integrating these data can help inferring biological knowledge effectively. We present a new statistical method of integrating multiple microarray datasets for gene function prediction. We tested the performance of our model using yeast and human datasets. Our results show that combining multiple datasets improves the accuracy over the best function prediction of any single dataset significantly. We also compared performance of the meta p-value and meta correlation methods for function prediction. Supplementary results and code are available at http://digbio.missouri.edu/meta_analyses.

Keywords: gene function prediction; microarray data analysis; Pearson correlation coefficient; meta-analysis; meta correlation; p-value; bioinformatics; yeast datasets; human datasets; multiple datasets.

DOI: 10.1504/IJDMB.2010.034194

International Journal of Data Mining and Bioinformatics, 2010 Vol.4 No.4, pp.357 - 376

Published online: 17 Jul 2010 *

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