Evaluating measures of association for single-cell transcriptomics

Nat Methods. 2019 May;16(5):381-386. doi: 10.1038/s41592-019-0372-4. Epub 2019 Apr 8.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cardiovascular Diseases / genetics
  • Cell Line
  • Central Nervous System Diseases / genetics
  • Cluster Analysis
  • Computational Biology / methods*
  • Gene Expression Profiling / methods*
  • Gene Regulatory Networks / genetics*
  • Humans
  • Mice
  • Reproducibility of Results
  • Sequence Analysis, RNA / methods
  • Single-Cell Analysis / methods*
  • Software