Biclustering models for structured microarray data

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 t...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2005-10, Vol.2 (4), p.316-329
Main Authors: Turner, H.L., Bailey, T.C., Krzanowski, W.J., Hemingway, C.A.
Format: Article
Language:English
Subjects:
Algorithms
Bayesian methods
Biclustering
Biological processes
Cluster Analysis
Clustering methods
Computer Simulation
Context modeling
Data analysis
Databases, Genetic
Diseases
Filters
Gene expression
Gene Expression Profiling
Humans
Models, Genetic
Oligonucleotide Array Sequence Analysis - methods
overlapping clustering
partial supervision
repeated measures
three-way data
Time measurement
Tuberculosis, Meningeal - genetics
Tuberculosis, Pulmonary - complications
Tuberculosis, Pulmonary - genetics
two-way clustering
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Summary: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.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2005.49