Biclustering models for structured microarray data.

Heather L Turner; Trevor C Bailey; Wojtek J Krzanowski; Cheryl A Hemingway

doi:10.1109/tcbb.2005.49

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Biclustering models for structured microarray data.

Heather L Turner·Trevor C Bailey·Wojtek J Krzanowski·Cheryl A Hemingway

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Abstract

Microarrays have become a standard tool for investigating gene function and more complex microarray experiments are increasingly being conducted. For example, an experiment may involve samples from several groups or may investigate changes in gene expression over time for several subjects, leading to large three-way data sets. In response to this increase in data complexity, we propose some extensions to the plaid model, a biclustering method developed for the analysis of gene expression data. This model-based method lends itself to the incorporation of any additional structure such as external grouping or repeated measures. We describe how the extended models may be fitted and illustrate their use on real data.

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Algorithms Cluster Analysis Computer Simulation Databases, Genetic Gene Expression Profiling Humans Models, Genetic Oligonucleotide Array Sequence Analysis Tuberculosis, Meningeal Tuberculosis, Pulmonary Journal Article Research Support, Non-U.S. Gov't

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Heather L Turner, Trevor C Bailey, Wojtek J Krzanowski, Cheryl A Hemingway. Biclustering models for structured microarray data.. https://doi.org/10.1109/tcbb.2005.49

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