Generalized finite mixture of multivariate regressions with applications to therapeutic biomarker identification

Finite mixtures of regressions have been used to analyze data that come from a heterogeneous population. When more than one response is observed, accommodating a multivariate response can be useful. In this article, we go a step further and introduce a multivariate extension that includes a latent o...

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Bibliographic Details
Published inStatistics in medicine Vol. 39; no. 28; pp. 4301 - 4324
Main Authors Liu, Hongmei, Sunil Rao, J.
Format Journal Article
LanguageEnglish
Published England Wiley Subscription Services, Inc 10.12.2020
Subjects
Biomarkers
cancer biomarkers
EM algorithm
Lasso
mixture of multivariate regression models
overlapping clustering
mixture of multivariate regression models
overlapping clustering
cancer biomarkers
EM algorithm
Lasso
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Summary:Finite mixtures of regressions have been used to analyze data that come from a heterogeneous population. When more than one response is observed, accommodating a multivariate response can be useful. In this article, we go a step further and introduce a multivariate extension that includes a latent overlapping cluster indicator variable that allows for potential overdispersion. A generalized mixture of multivariate regressions in connection with the proposed model and a new EM algorithm for fitting are provided. In addition, we allow for high‐dimensional predictors via shrinkage estimation. This model proves particularly useful in the analysis of complex data like the search for cancer therapeutic biomarkers. We demonstrate this using the genomics of drug sensitivity in cancer resource.
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ISSN:0277-6715
1097-0258
DOI:10.1002/sim.8726