Constructing local Cell Sepcific Networks from Single Cell Data

Abstract

Gene co-expression 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 that estimates cell-specific networks (CSN) for each cell using a method inspired by Dai et al. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of network structure, which provide better estimates of gene block structures than traditional measures. The method, called locCSN, is based on a non-parametric investigation of the joint distribution of gene expression, hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. The individual networks preserve information about the heterogeneity of the cells and having repeated estimates of network structure facilitates testing for difference in network structure between groups of cells. The original CSN algorithm showed promise; however, it had shortcomings which locCSN overcomes. Additionally, we propose new downstream analysis methods using CSNs, to utilize more fully the information contained within them. Finally, to further our understanding of autism spectrum disorder we examined the evolution of gene networks in fetal brain cells and compared the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene co-expression changes.

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

“…In this paper, we formalize the idea of averaging the cell-specific gene association [4,5] under a general statistical framework. We show that this approach produces a novel univariate dependence measure, called aLDG, that can detect nonlinear, non-monotone relationships between a pair of variables.…”

“…Specifically, as a one-sided test, I XY (j) assesses whether or not f XY (x, y) > f X (x)f Y (y), at position (x, y) marked by cell j. To assess global independence, aggregation, as proposed by Wang et al [5], is needed. Their empirical measure can be formally written as:…”

“…This method is particularly designed to detect differential signals attributable to a small fraction of the genes. To emphasize the contrast with differentially expressed genes, [5] call these differential network genes.…”

“…In a follow-up paper, [5] take the first steps to capitalize on the CSN approach by redirecting the concept to obtain an estimator of co-expression. Specifically, they propose averaging the "cell specific" gene association indicators over cells in a class to recover a global measure of gene association (avgCSN).…”

“…By contrast, measures like Pearson's correlation can have both negative and positive summands, and therefore the final value can be small even if the dependence structure is clear for a subset of the cells. To make the method more stable, [5] proposed some heuristic and practical techniques to compute avgCSN, for which we would like to have more principled insights. Examples are the choice of window size in defining neighborhoods in the local contingency table test, the choice of thresholding in constructing an edge, and the range of cells to aggregate over.…”