STRUCTURE LEARNING FOR ZERO-INFLATED COUNTS WITH AN APPLICATION TO SINGLE-CELL RNA SEQUENCING DATA

Abstract

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 overabundance 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.