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Bioinformatics Advance Access published online on August 14, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl439
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 15, 2006
Revised August 2, 2006
Accepted August 9, 2006

Article

Meta-analysis based on control of false discovery rate: combining yeast ChIP-chip data sets

Saumyadipta Pyne 1 *, Bruce Futcher 2, and Steve Skiena 1

1 Department of Computer Science, Stony Brook University, NY 11794, USA
2 Department of Molecular Genetics and Microbiology, Stony Brook University, NY 11794, USA

* To whom correspondence should be addressed.
Saumyadipta Pyne, E-mail: spyne{at}cs.sunysb.edu


  Abstract

Motivation: High-throughput microarray technology can be used to examine thousands of features, such as all the genes of an organism, and measure their expression. Two important issues of microarray bioinformatics are first, how to combine the significance values for each feature across experiments with high statistical power, and second, how to control the proportion of false positives. Existing methods address these issues separately, in spite of their linked usage.

Results: We present a novel method (ESP) to address the two requirements in an interdependent way. It generalizes the Truncated Product Method of Zaykin et al. (2002) to combine only those significance values which clear their respective experiment-specific false discovery restrictive thresholds, thus allowing us to control the False Discovery Rate (FDR) for the final combined result. Further, we introduce several concepts that together offer FDR control, high power, quality control and speed-up in meta-analysis as done by our algorithm. Computational and statistical methods of research synthesis like the one described here will be increasingly important as additional genome-wide data sets accumulate in databases.

We apply our method to combine three well-known ChIP-chip transcription factor binding data sets for budding yeast to identify significant intergenic regulatory sequences for nine cell cycle regulating transcription factors, both with high power and controlled FDR.

Supplementary Materials and Appendices: http://www.cs.sunysb.edu/~compbio/Meta.

Associate Editor: Martin Bishop
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