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

• 成果类型: Article
• 署名作者: Nguyen, Thi Kim Hue; van den Berge, Koen; Chiogna, Monica; Risso, Davide
• 署名单位: University of Padua; Ghent University; University of Bologna
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1732
• 发表日期: 2023
• 页码: 2555-2573
• 关键词: Graphical models; stem-cells; maintenance; networks

摘要

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.