Bayesian classification of tumours by using gene expression data

• Published in: Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2005-04, Vol.67 (2), p.219-234
• Main Authors: Mallick, Bani K., Ghosh, Debashis, Ghosh, Malay
• Format: Article
• Language: English
• Subjects: Applications; Bayesian method; Biology, psychology, social sciences; Cancer; Classification; Data analysis; Datasets; Diagnostics; Exact sciences and technology; Gene expression; Genes; Gibbs sampling; Hilbert spaces; Information classification; Logistics; Markov chain Monte Carlo methods; Mathematical functions; Mathematical vectors; Mathematics; Medical sciences; Metropolis-Hastings algorithm; Microarrays; Modeling; Morphology; Multivariate analysis; Neoplasia; Parametric models; Probability and statistics; Reproducing kernel Hilbert space; Sciences and techniques of general use; Shrinkage parameters; Statistical mechanics; Statistical methods; Statistics; Studies; Support vector machines; Tumors
• ISSN: 1369-7412; 1467-9868
• DOI: 10.1111/j.1467-9868.2005.00498.x

Precise classification of tumours is critical for the diagnosis and treatment of cancer. Diagnostic pathology has traditionally relied on macroscopic and microscopic histology and tumour morphology as the basis for the classification of tumours. Current classification frame-works, however, cannot discriminate between tumours with similar histopathologic features, which vary in clinical course and in response to treatment. In recent years, there has been a move towards the use of complementary deoxyribonucleic acid microarrays for the classification of tumours. These high throughput assays provide relative messenger ribonucleic acid expression measurements simultaneously for thousands of genes. A key statistical task is to perform classification via different expression patterns. Gene expression profiles may offer more information than classical morphology and may provide an alternative to classical tumour diagnosis schemes. The paper considers several Bayesian classification methods based on reproducing kernel Hubert spaces for the analysis of microarray data. We consider the logistic likelihood as well as likelihoods related to support vector machine models. It is shown through simulation and examples that support vector machine models with multiple shrinkage parameters produce fewer misclassification errors than several existing classical methods as well as Bayesian methods based on the logistic likelihood or those involving only one shrinkage parameter.