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Genomic instability, the propensity of aberrations in chromosomes, plays acritical role in the development of many diseases. High throughput genotypingexperiments have been performed to study genomic instability in diseases. Theoutput of such experiments can be summarized as high dimensional binaryvectors, where each binary variable records aberration status at one markerlocus. It is of keen interest to understand how these aberrations interact witheach other. In this paper, we propose a novel method, \texttt{LogitNet}, toinfer the interactions among aberration events. The method is based onpenalized logistic regression with an extension to account for spatialcorrelation in the genomic instability data. We conduct extensive simulationstudies and show that the proposed method performs well in the situationsconsidered. Finally, we illustrate the method using genomic instability datafrom breast cancer samples.

Learning networks from high dimensional binary data: An application to genomic instability data