Directed Graphical Models and Causal Discovery for Zero-Inflated Data.

ABSTRACT: With advances in technology, gene expression measurements from single cells can be used to gain refined insights into regulatory relationships among genes. Directed graphical models are well-suited to explore such (cause-effect) relationships. However, statistical analyses of single cell data are complicated by the fact that the data often show zero-inflated expression patterns. To address this challenge, we propose directed graphical models that are based on Hurdle conditional distributions parametrized in terms of polynomials in parent variables and their 0/1 indicators of being zero or nonzero. While directed graphs for Gaussian models are only identifiable up to an equivalence class in general, we show that, under a natural and weak assumption, the exact directed acyclic graph of our z

SUBMITTER: Yu S 

PROVIDER: S-EPMC11257027 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature