Structure learning for zero-inflated counts with an application to single-cell RNA sequencing data
Thi Kim Hue Nguyen, Koen van den Berge, Monica Chiogna, Davide Risso
The Annals of Applied Statistics · 2023 · Volume 17 · Issue 3
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.
Keywords
graphical models, single-cell RNA-seq, structure learning, zero-inflated counts
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