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• W4386496250 abstract "Abstract Discrete data such as counts of microbiome taxa resulting from next-generation sequencing are routinely encountered in bioinformatics. Taxa count data in microbiome studies are typically high-dimensional, over-dispersed, and can only reveal relative abundance therefore being treated as compositional. Analyzing compositional data presents many challenges because they are restricted to a simplex. In a logistic normal multinomial model, the relative abundance is mapped from a simplex to a latent variable that exists on the real Euclidean space using the additive log-ratio transformation. While a logistic normal multinomial approach brings flexibility for modeling the data, it comes with a heavy computational cost as the parameter estimation typically relies on Bayesian techniques. In this paper, we develop a novel mixture of logistic normal multinomial models for clustering microbiome data. Additionally, we utilize an efficient framework for parameter estimation using variational Gaussian approximations (VGA). Adopting a variational Gaussian approximation for the posterior of the latent variable reduces the computational overhead substantially. The proposed method is illustrated on simulated and real datasets." @default.
• W4386496250 created "2023-09-07" @default.
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• W4386496250 date "2023-09-07" @default.
• W4386496250 modified "2023-09-29" @default.
• W4386496250 title "Clustering microbiome data using mixtures of logistic normal multinomial models" @default.
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• W4386496250 doi "https://doi.org/10.1038/s41598-023-41318-8" @default.
• W4386496250 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37679485" @default.
• W4386496250 hasPublicationYear "2023" @default.
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