Constructing local cell-specific networks from single-cell data

Xuran Wang,David Choi,Kathryn Roeder

doi:10.1073/pnas.2113178118

Abstract

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

Highlights

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level
Application of our algorithm to two brain cell samples furthers our understanding of autism spectrum disorder by examining the evolution of gene networks in fetal brain cells and comparing the networks of cells sampled from case and control subjects
We propose an algorithm for cell-specific networks (CSNs) construction that allows for the window size of the local independence test to vary cell by cell

Summary

Introduction

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. Relying on the CSN principle, a full gene–gene network is potentially available for each cell ( with high noise), which can be envisioned as a graph, with genes as nodes and edges depicting gene–gene dependencies Building on this concept, we develop an alternative analysis scheme that utilizes a more coexpression network | differential network genes | differential expression | single-cell RNA-seq | brain cells. Even if the aim is to estimate a cell-type-specific network, traditional methods estimate a single coexpression relationship across the entire sample of cells of that type With such an approach, heterogeneity of coexpression across individual cells is erased, Understanding gene regulatory networks is a topic of great interest because it can provide insights into cellular development, and identify factors that differ between normal and abnormal cells and phenotypes. Application of our algorithm to two brain cell samples furthers our understanding of autism spectrum disorder by examining the evolution of gene networks in fetal brain cells and comparing the networks of cells sampled from case and control subjects

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