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

Evaluating measures of association for single-cell transcriptomics.

Author information

1
Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada. michael.skinnider@msl.ubc.ca.
2
International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Bristish Columbia, Canada.
3
Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada. foster@msl.ubc.ca.
4
Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, British Columbia, Canada. foster@msl.ubc.ca.

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.

PMID:
30962620
DOI:
10.1038/s41592-019-0372-4

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