  A mixture of multivariate Poisson-log normal factor analyzers is introduced
by imposing constraints on the covariance matrix, which resulted in flexible
models for clustering purposes. In particular, a class of eight parsimonious
mixture models based on the mixtures of factor analyzers model are introduced.
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.
