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Open Access Methodology article

Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries

Corinne Dahinden12*, Giovanni Parmigiani3, Mark C Emerick4 and Peter Bühlmann12

Author Affiliations

1 Seminar für Statistik, ETH Zürich, CH-8092 Zürich, Switzerland

2 Competence Center for Systems Physiology and Metabolic Diseases, ETH Zürich, CH-8093 Zürich, Switzerland

3 Departments of Oncology and Biostatistics, Johns Hopkins Schools of Medicine and Public Health, Baltimore, MD, USA

4 Department of Physiology, Johns Hopkins School of Medicine, Baltimore, MD, USA

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BMC Bioinformatics 2007, 8:476  doi:10.1186/1471-2105-8-476

Published: 11 December 2007



The joint analysis of several categorical variables is a common task in many areas of biology, and is becoming central to systems biology investigations whose goal is to identify potentially complex interaction among variables belonging to a network. Interactions of arbitrary complexity are traditionally modeled in statistics by log-linear models. It is challenging to extend these to the high dimensional and potentially sparse data arising in computational biology. An important example, which provides the motivation for this article, is the analysis of so-called full-length cDNA libraries of alternatively spliced genes, where we investigate relationships among the presence of various exons in transcript species.


We develop methods to perform model selection and parameter estimation in log-linear models for the analysis of sparse contingency tables, to study the interaction of two or more factors. Maximum Likelihood estimation of log-linear model coefficients might not be appropriate because of the presence of zeros in the table's cells, and new methods are required. We propose a computationally efficient ℓ1-penalization approach extending the Lasso algorithm to this context, and compare it to other procedures in a simulation study. We then illustrate these algorithms on contingency tables arising from full-length cDNA libraries.


We propose regularization methods that can be used successfully to detect complex interaction patterns among categorical variables in a broad range of biological problems involving categorical variables.