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The problem of estimating the structure of a graph from observed data is ofgrowing interest in the context of high-throughput genomic data, andsingle-cell RNA sequencing in particular. These, however, are challengingapplications, since the data consist of high-dimensional counts with highvariance and over-abundance of zeros. Here, we present a general framework forlearning the structure of a graph from single-cell RNA-seq data, based on thezero-inflated negative binomial distribution. We demonstrate with simulationsthat our approach is able to retrieve the structure of a graph in a variety ofsettings and we show the utility of the approach on real data.

Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data