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

TL;DR

This paper introduces a new statistical framework for analyzing causal mediation in zero-inflated single-cell RNA-seq data, effectively handling complex mediator structures and zero inflation to uncover disease-related pathways.

Contribution

It develops a novel mediation model using zero-inflated negative binomial and beta regression, improving power and accuracy in identifying mediators in single-cell data.

Findings

Enhanced power and controlled false discovery rates in simulations

Successful application to ROSMAP data revealing meaningful mediators

Improved understanding of disease mechanisms through identified mediators

Abstract

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity, enabling detailed molecular profiling at the individual cell level. However, integrating high-dimensional single-cell data into causal mediation analysis remains challenging due to zero inflation and complex mediator structures. We propose a novel mediation framework leveraging zero-inflated negative binomial models to characterize cell-level mediator distributions and beta regression for zero-inflation proportions. The model can identify expression level as well as expressed proportion that could mediate disease-leading causal pathway. Extensive simulation studies demonstrate improved power and controlled false discovery rates. We further illustrate the utility of this approach through application to ROSMAP single-cell transcriptomic data, uncovering biologically meaningful mediation effects that enhance understanding of disease mechanisms.