Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data
Genomics
2023-02-15 v3 Applications
Methodology
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 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.
Keywords
Cite
@article{arxiv.2011.12044,
title = {Structure learning for zero-inflated counts, with an application to single-cell RNA sequencing data},
author = {Thi Kim Hue Nguyen and Koen Van den Berge and Monica Chiogna and Davide Risso},
journal= {arXiv preprint arXiv:2011.12044},
year = {2023}
}