Evaluating measures of association for single-cell transcriptomics

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

Research output: Contribution to journalArticlepeer-review

74 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)381-386
Number of pages6
JournalNature Methods
Volume16
Issue number5
DOIs
StatePublished - May 1 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Biochemistry
  • Biotechnology
  • Cell Biology

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