From local to global gene co-expression estimation using single-cell RNA-seq data

TL;DR

This paper introduces a novel dependence measure for gene relationships in single-cell RNA-seq data that captures local, non-linear, and non-monotone dependencies more effectively than existing methods, with proven robustness and superior performance.

Contribution

It proposes a new univariate dependence measure derived from cell-specific gene networks, capable of detecting complex local dependencies in heterogeneous single-cell data, and establishes its theoretical robustness.

Findings

Outperforms existing dependence measures in simulations

Effectively detects non-linear, non-monotone relationships

Robust in both population and empirical analyses

Abstract

In genomics studies, the investigation of the gene relationship often brings important biological insights. Currently, the large heterogeneous datasets impose new challenges for statisticians because gene relationships are often local. They change from one sample point to another, may only exist in a subset of the sample, and can be non-linear or even non-monotone. Most previous dependence measures do not specifically target local dependence relationships, and the ones that do are computationally costly. In this paper, we explore a state-of-the-art network estimation technique that characterizes gene relationships at the single-cell level, under the name of cell-specific gene networks. We first show that averaging the cell-specific gene relationship over a population gives a novel univariate dependence measure that can detect any non-linear, non-monotone relationship. Together with a consistent nonparametric estimator, we establish its robustness on both the population and empirical levels. Simulations and real data analysis show that this measure outperforms existing independence measures like Pearson, Kendall's $\tau$, $\tau^\star$, distance correlation, HSIC, Hoeffding's D, HHG, and MIC, on various tasks.