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Bioinformatics Advance Access published online on December 8, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl598
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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 23, 2006
Revised November 21, 2006
Accepted November 21, 2006

Article

Causality and pathway search in microarray time series experiment

Nitai D. Mukhopadhyay 1 * and Snigdhansu Chatterjee 2

1 Eli Lilly and Co.,
2 University of Minnesota

* To whom correspondence should be addressed.
Nitai D. Mukhopadhyay, E-mail: nitai{at}lilly.com


  Abstract

Motivation: Interaction among time series can be explored in many ways. All the approach has the usual problem of low power and high dimensional model. Here we attempted to build a causality network among a set of time series. The causality has been established by Granger causality, and then constructing the pathway has been implemented by finding the Minimal Spanning Tree within each connected component of the inferred network. False discovery rate measurement has been used to identify the most significant causalities.

Results: Simulation shows good convergence and accuracy of the algorithm. Robustness of the procedure has been demonstrated by applying the algorithm in a non-stationary time series setup. Application of the algorithm in a real dataset identified many causalities, with some overlap with previously known ones. Assembled network of the genes reveals features of the network that are common wisdom about naturally occurring networks.

Associate Editor: David Rocke
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