Bayesian Variable Selection for Probit Mixed Models Applied to Gene Selection
| Autor: | Meili Baragatti |
|---|---|
| Přispěvatelé: | Institut de mathématiques de Luminy (IML), Centre National de la Recherche Scientifique (CNRS)-Université de la Méditerranée - Aix-Marseille 2, Université de la Méditerranée - Aix-Marseille 2-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2011 |
| Předmět: |
Statistics and Probability
Mixed model FOS: Computer and information sciences Individual gene Computer science Probit grouping technique (or blocking technique) Latent variable computer.software_genre Bayesian inference 01 natural sciences Quantitative Biology - Quantitative Methods Statistics - Applications Methodology (stat.ME) 010104 statistics & probability 03 medical and health sciences 62J07 probit mixed regression model random effects Applications (stat.AP) 0101 mathematics Statistics - Methodology Quantitative Methods (q-bio.QM) 030304 developmental biology Bayesian variable selection 0303 health sciences [STAT.AP]Statistics [stat]/Applications [stat.AP] Metropolis-within-Gibbs algorithm Applied Mathematics 92D10 Random effects model Gene selection 62-04 62P10 FOS: Biological sciences 62J12 Data mining 62F15 computer [STAT.ME]Statistics [stat]/Methodology [stat.ME] |
| Zdroj: | Bayesian Analysis Bayesian Analysis, International Society for Bayesian Analysis, 2011, 6 (2), pp.209-230. ⟨10.1214/11-BA607⟩ Bayesian Anal. 6, no. 2 (2011), 209-229 Bayesian Analysis, 2011, 6 (2), pp.209-230. ⟨10.1214/11-BA607⟩ |
| ISSN: | 1936-0975 1931-6690 |
| DOI: | 10.48550/arxiv.1101.4577 |
| Popis: | International audience; In computational biology, gene expression datasets are characterized by very few individual samples compared to a large number of measurements per sample. Thus, it is appealing to merge these datasets in order to increase the number of observations and diversify the data, allowing a more reliable selection of genes relevant to the biological problem. Besides, the increased size of a merged dataset facilitates its re-splitting into training and validation sets. This necessitates the introduction of the dataset as a random effect. In this context, extending a work of Lee et al. (2003), a method is proposed to select relevant variables among tens of thousands in a probit mixed regression model, considered as part of a larger hierarchical Bayesian model. Latent variables are used to identify subsets of selected variables and the grouping (or blocking) technique of Liu (1994) is combined with a Metropolis-within-Gibbs algorithm (Robert and Casella 2004). The method is applied to a merged dataset made of three individual gene expression datasets, in which tens of thousands of measurements are available for each of several hundred human breast cancer samples. Even for this large dataset comprised of around 20000 predictors, the method is shown to be efficient and feasible. As an illustration, it is used to select the most important genes that characterize the estrogen receptor status of patients with breast cancer. |
| Databáze: | OpenAIRE |
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