Journal Article

Evaluating measures of association for single-cell transcriptomics

Squair Jordan W, Skinnider, Michael A, Foster, Leonard J

2019

Overview

Single-cell transcriptomics provides an opportunity to characterize cell-type-specific transcriptional networks, intercellular signaling pathways and cellular diversity with unprecedented resolution by profiling thousands of cells in a single experiment. However, owing to the unique statistical properties of scRNA-seq data, the optimal measures of association for identifying gene–gene and cell–cell relationships from single-cell transcriptomics remain unclear. Here, we conducted a large-scale evaluation of 17 measures of association for their ability to reconstruct cellular networks, cluster cells of the same type and link cell-type-specific transcriptional programs to disease. Measures of proportionality were consistently among the best-performing methods across datasets and tasks. Our analysis provides data-driven guidance for gene and cell network analysis in single-cell transcriptomics.Among 17 measures of association tested, measures of proportionality consistently performed well for inference of gene and cellular networks, cell clusters and links to disease from scRNA-seq data. In contrast, several widely used measures of association performed well on only a subset of tasks.

• Publisher: Nature Publishing Group
• Subject: Cellular communication / Datasets / Gene expression / Network analysis / Reproducibility / Transcription