Finite mixture biclustering of discrete type multivariate data

Fernández, Daniel; Arnold, Richard; Pledger, Shirley; Liu, Ivy; Costilla, Roy

doi:10.1007/s11634-018-0324-3

Cited by 3 publications

(1 citation statement)

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“…The Factor analysis for bicluster acquisition (FABIA), developed by Kasim et al (2010) considers instead a class of multiplicative models that exploits a sparse factorization of the data matrix that allows for heavy-tailed data, paired with a model selection approach to detect biclusters under a Laplacian prior to enforce sparsity. For a recent review on these and other bicluster methods for gene expression data, see Pontes et al (2015), and for the case of biclustering discrete multivariate data, see Fernández et al (2019).…”

Section: Introductionmentioning

confidence: 99%

Biclustering via Semiparametric Bayesian Inference

Murua

Quintana

2022

Bayesian Anal.

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Motivated by classes of problems frequently found in the analysis of gene expression data, we propose a semiparametric Bayesian model to detect biclusters, that is, subsets of individuals sharing similar patterns over a set of conditions. Our approach is based on the well-known plaid model by Lazzeroni and Owen (2002). By assuming a truncated stick-breaking prior we also find the number of biclusters present in the data as part of the inference. Evidence from a simulation study shows that the model is capable of correctly detecting biclusters and performs well compared to some competing approaches. The flexibility of the proposed prior is demonstrated with applications to the analysis of gene expression data (continuous responses) and histone modifications data (count responses).

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Section: Introductionmentioning

confidence: 99%

Biclustering via Semiparametric Bayesian Inference

Murua

Quintana

2022

Bayesian Anal.

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Row mixture-based clustering with covariates for ordinal responses

Preedalikit¹,

Fernández²,

Liu³

et al. 2023

Comput Stat

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Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal data, using finite mixtures to cluster the rows (observations) of the matrix. These models can incorporate the main effects of individual rows and columns, as well as cluster effects, to model the matrix of responses. However, many real-world applications also include available covariates, which provide insights into the main characteristics of the clusters and determine clustering structures based on both the individuals’ similar patterns of responses and the effects of the covariates on the individuals' responses. In our research we have extended the mixture-based models to include covariates and test what effect this has on the resulting clustering structures. We focus on clustering the rows of the data matrix, using the proportional odds cumulative logit model for ordinal data. We fit the models using the Expectation-Maximization algorithm and assess performance using a simulation study. We also illustrate an application of the models to the well-known arthritis clinical trial data set.

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Biclustering multivariate discrete longitudinal data

Alfó,

Marino,

Martella

2023

Stat Comput

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A model-based biclustering method for multivariate discrete longitudinal data is proposed. We consider a finite mixture of generalized linear models to cluster units and, within each mixture component, we adopt a flexible and parsimonious parameterization of the component-specific canonical parameter to define subsets of variables (segments) sharing common dynamics over time. We develop an Expectation-Maximization-type algorithm for maximum likelihood estimation of model parameters. The performance of the proposed model is evaluated on a large scale simulation study, where we consider different choices for the sample the size, the number of measurement occasions, the number of components and segments. The proposal is applied to Italian crime data (font ISTAT) with the aim to detect areas sharing common longitudinal trajectories for specific subsets of crime types. The identification of such biclusters may potentially be helpful for policymakers to make decisions on safety.

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Finite mixture biclustering of discrete type multivariate data

Abstract: Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints This is a post-peer-review, pre-copy edit version of an article

Cited by 3 publications

References 82 publications

Biclustering via Semiparametric Bayesian Inference

Biclustering via Semiparametric Bayesian Inference

Row mixture-based clustering with covariates for ordinal responses

Biclustering multivariate discrete longitudinal data

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