  The problem of estimating the structure of a graph from observed data is of
growing interest in the context of high-throughput genomic data, and
single-cell RNA sequencing in particular. These, however, are challenging
applications, since the data consist of high-dimensional counts with high
variance and over-abundance of zeros. Here, we present a general framework for
learning the structure of a graph from single-cell RNA-seq data, based on the
zero-inflated negative binomial distribution. We demonstrate with simulations
that our approach is able to retrieve the structure of a graph in a variety of
settings and we show the utility of the approach on real data.
