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Updated: Jan 26, 2026

Transcriptome Analysis of Single Cells
Published on: April 25, 2011
Evaluating measures of association for single-cell transcriptomics
Michael A Skinnider1, Jordan W Squair2, Leonard J Foster3,4
1Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada. michael.skinnider@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.
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