Sparse inverse covariance estimation for high-throughput microRNA sequencing data in the Poisson log-normal graphical model

We introduce a one-step EM algorithm to estimate the graphical structure in a Poisson-Log-Normal graphical model. This procedure is equivalent to a normality transformation that makes the problem of identifying relationships in high-throughput microRNA (miRNA) sequence data feasible. The Poisson-log-normal model moreover allows us to directly account for known overdispersion relationships present in this data set. We show that our EM algorithm provides a provable increase in performance in determining the network structure. The model is shown to provide an increase in performance in simulation settings over a range of network structures. The model is applied to high-throughput miRNA sequencing data from patients with breast cancer from The Cancer Genome Atlas (TCGA). By selecting the most highly connected miRNA molecules in the fitted network we find that nearly all of them are known to be involved in the regulation of breast cancer.