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A mixture of multivariate Poisson-log normal factor analyzers is introducedby imposing constraints on the covariance matrix, which resulted in flexiblemodels for clustering purposes. In particular, a class of eight parsimoniousmixture models based on the mixtures of factor analyzers model are introduced.Variational Gaussian approximation is used for parameter estimation, andinformation criteria are used for model selection. The proposed models areexplored in the context of clustering discrete data arising from RNA sequencingstudies. Using real and simulated data, the models are shown to give favourableclustering performance. The GitHub R package for this work is available athttps://github.com/anjalisilva/mixMPLNFA and is released under the open-sourceMIT license.

Finite Mixtures of Multivariate Poisson-Log Normal Factor Analyzers for Clustering Count Data