ArticleNature methods2019
Evaluating measures of association for single-cell transcriptomics.
Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 4 of them syntheses that pooled it.
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Who cites it
78 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Meta-analysis of single-cell RNA sequencing co-expression in human neural organoids reveals their high variability in recapitulating primary tissue.PLoS biology · 2024Pooled it
- Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data.Genome biology · 2023Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments.Nature methods · 2021Pooled it
- Nivolumab plus ipilimumab induce hyper-progression in renal medullary carcinoma: results of a phase II trial and preclinical evidence.Nature communications · 2025Trial
- Proportionality-based association metrics in count compositional data.NAR genomics and bioinformatics · 2026Article
- Single-Cell Transcriptomic Integration Identifies LTA4H as an Immune-Associated Therapeutic Target in Hepatocellular Carcinoma.Cell biochemistry and biophysics · 2026Article
- A unified framework for selecting and evaluating cell-type-specific gene co-expressions in single-cell data.Briefings in bioinformatics · 2026Article
- Translation efficiency covariation identifies conserved coordination patterns across cell types.Nature biotechnology · 2026Article
- Host Prior Exposure Augments Gene Expression Heterogeneity of Both Host and Pathogen During In Vivo Infection.Molecular ecology · 2026Article
- DeepCE: a deep learning framework for correlation-enhanced gene regulatory network inference in single-cell RNA sequencing data.Bioinformatics advances · 2026Article
- High-resolution single-cell analyses reveal evolutionary constraints and evolvability of sexual circuits inProceedings of the National Academy of Sciences of the United States of America · 2025Article
- scSpecies: enhancement of network architecture alignment in comparative single-cell studies.Genome biology · 2025Article
- Library size-stabilized metacells construction enhances co-expression network analysis in single-cell data.PLoS computational biology · 2025Article
- Scalable transcription factor mapping uncovers the regulatory dynamics of natural and synthetic transcription factors in human T cell states.bioRxiv : the preprint server for biology · 2025Article
- ST-deconv: an accurate deconvolution approach for spatial transcriptome data utilizing self-encoding and contrastive learning.NAR genomics and bioinformatics · 2025Article
- Host prior exposure augments heterogeneity in gene expression in both host and pathogen duringbioRxiv : the preprint server for biology · 2025Article
- High-resolution comparative single-cell transcriptomics ofbioRxiv : the preprint server for biology · 2025Article
- Identifying reproducible transcription regulator coexpression patterns with single cell transcriptomics.PLoS computational biology · 2025Article
- Single-cell network biology enabling cell-type-resolved disease genetics.Genomics & informatics · 2025Review
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.