A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data.

Chekouo, Thierry; Mukherjee, Himadri

Chekouo, Thierry; Mukherjee, Himadri.

Afiliación

Chekouo T; Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minnesota, USA.
Mukherjee H; Department of Mathematics and Statistics, University of Minnesota Duluth, Minnesota, USA.

Biom J ; 66(4): e2300173, 2024 Jun.

Article en En | MEDLINE | ID: mdl-38817110

ABSTRACT

ABSTRACT

We introduce a Bayesian approach for biclustering that accounts for the prior functional dependence between genes using hidden Markov models (HMMs). We utilize biological knowledge gathered from gene ontologies and the hidden Markov structure to capture the potential coexpression of neighboring genes. Our interpretable model-based clustering characterized each cluster of samples by three groups of features overexpressed, underexpressed, and irrelevant features. The proposed methods have been implemented in an R package and are used to analyze both the simulated data and The Cancer Genome Atlas kidney cancer data.

Asunto(s)

Teorema de Bayes; Neoplasias Renales; Cadenas de Markov; Neoplasias Renales/genética; Humanos; Análisis por Conglomerados; Perfilación de la Expresión Génica; Regulación Neoplásica de la Expresión Génica; Biometría/métodos

Palabras clave

Bayesian hidden Markov model; biclustering; biological prior knowledge; kidney cancer

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Cadenas de Markov / Teorema de Bayes / Neoplasias Renales Límite: Humans Idioma: En Revista: Biom J Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Alemania

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