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

2020/11/24 by Thi Kim Hue Nguyen, Nguyen, Thi Kim Hue, Koen Van Den Berge +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genomics (q-bio.GN) #Machine Learning and Algorithms #Methodology (stat.ME) #Single-cell and spatial transcriptomics

paper · pdf · doi:10.48550/arxiv.2011.12044

openalex publication_date 2020/11/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• A Statistical Framework for Co-Mediators of Zero-Inflated Single-Cell RNA-Seq Data

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