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<p>A mixture of multivariate Poisson-log normal factor analyzers is introduced by imposing constraints on the covariance matrix, which results in flexible models for clustering purposes. In particular, a class of eight parsimonious mixture models based on the mixtures of factor analyzers is introduced. The variational Gaussian approximation is used for parameter estimation, and information criteria are used for model selection. The proposed models are explored in the context of clustering discrete data arising from RNA sequencing studies. Using real and simulated data, the models are shown to give favourable clustering performance. The GitHub <Emphasis FontCategory="SansSerif">R</Emphasis> package for this work is available at <a href="https://github.com/anjalisilva/mixMPLNFA">https://github.com/anjalisilva/mixMPLNFA</a> and is released under the open-source MIT license.</p>

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Finite mixtures of multivariate Poisson-log normal factor analyzers for clustering count data

  • 期刊论文

  • 发表日期：

    2025年9月15日

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  • Andrea Payne,
  • Anjali Silva,
  • Steven J Rothstein,
  • Paul D. McNicholas,
  • Sanjeena Subedi

摘要

A mixture of multivariate Poisson-log normal factor analyzers is introduced by imposing constraints on the covariance matrix, which results in flexible models for clustering purposes. In particular, a class of eight parsimonious mixture models based on the mixtures of factor analyzers is introduced. The variational Gaussian approximation is used for parameter estimation, and information criteria are used for model selection. The proposed models are explored in the context of clustering discrete data arising from RNA sequencing studies. Using real and simulated data, the models are shown to give favourable clustering performance. The GitHub R package for this work is available at https://github.com/anjalisilva/mixMPLNFA and is released under the open-source MIT license.