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Bioinformatics Advance Access published online on November 27, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn611
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Sparse combinatorial inference with an application in cancer biology

Sach Mukherjee 1,2,*, Steven Pelech 3, Richard M. Neve 4, Wen-Lin Kuo 5, Safiyyah Ziyad 5, Paul T. Spellman 5, Joe W. Gray 5,6 and Terence P. Speed 7

1Department of Statistics and 2Centre for Complexity Science, University of Warwick, Coventry CV4 7AL, U.K., 3Kinexus Bioinformatics Corporation, Vancouver, Canada V6P 6T3, 4Genentech Inc., San Francisco, CA 94080, 5 Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, 6Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, 7Department of Statistics, University of California, Berkeley, Berkeley, CA 94720.

*To whom correspondence should be addressed. Dr. Sach Mukherjee, E-mail: S.N.Mukherjee{at}warwick.ac.uk


  Abstract

Motivation: Combinatorial effects, in which several variables jointly influence an output or reponse, play an important role in biological systems. In many settings, Boolean functions provide a natural way to describe such influences. However, biochemical data using which we may wish to characterize such influences are usually subject to much variability. Furthermore, in high-throughput biological settings Boolean relationships of interest are very often sparse, in the sense of being embedded in an overall dataset of higher dimensionality. This motivates a need for statistical methods capable of making inferences regarding Boolean functions under conditions of noise and sparsity.

Results: We put forward a statistical model for sparse, noisy Boolean functions and methods for inference under the model. We focus on the case in which the form of the underlying Boolean function, as well as the number and identity of its inputs are all unknown. We present results on synthetic data and on a study of signalling proteins in cancer biology.

Availability: go.warwick.ac.uk/sachmukherjee/sci.

Contact: S.N.Mukherjee{at}warwick.ac.uk

Associate Editor: Dr. Jonathan Wren

Received on June 19, 2008; revised on October 15, 2008; accepted on November 20, 2008

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