Evaluating measures of association for single-cell transcriptomics
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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hdWGCNA co-expression networks have a similar level of functional coherence to a previous study that evaluated co-expression networks derived from scRNA-seq data with different measures of gene-gene association.
“We report a similar level of functional coherence in these co-expression networks to a previous study that evaluated co-expression networks derived from scRNA-seq data with different measures of gene-gene association.24”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [24]
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hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
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Self-citation
- Authors
- Skinnider, M. A., Squair, J. W., & Foster, L. J.
- Affiliations
- University of British Columbia (CA)
- Corresponding author
- Skinnider, Michael A.
- Foster, Leonard J.
- Venue
- Nature Methods
- Published
- 2019-04-08
- Volume
- 16
- Issue
- 5
- Pages
- 381-386
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- OpenAlex
- W2929810236
- Cited by
- 145
- References
- 16
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 18 · 2023: 31 · 2022: 18 · 2021: 18 · 2020: 25 · 2019: 10
- FWCI
- 5.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Genome Canada
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- association measures, single-cell transcriptomics, single-cell RNA sequencing, gene-gene association, gene regulatory networks, cell clustering, disease association, network inference
- MeSH terms
- Animals, Cardiovascular Diseases, Cell Line, Central Nervous System Diseases, Humans, Software, Reproducibility of Results, Cluster Analysis, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Mice, Gene Regulatory Networks, Single-Cell Analysis
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