ArticleNature communications2023
Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Single-cell differential abundance detection: A new angle on dissecting tumor heterogeneity.World journal of clinical oncology · 2026Review
- Assessing differential cell composition in single-cell studies using voomCLR.Bioinformatics (Oxford, England) · 2026Article
- Deep generative modeling of sample-level heterogeneity in single-cell genomics.Nature methods · 2025Article
- Divide-and-conquer strategy with engineered ossification center organoids for rapid bone healing through developmental cell recruitment.Nature communications · 2025Article
- Epigenetic ageing clocks: statistical methods and emerging computational challenges.Nature reviews. Genetics · 2025Review
- Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging.Scientific reports · 2025Article
- Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution.Aging · 2024Article
- Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data.Nature communications · 2023Article
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR improves the sensitivity to detect cell-type composition shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent differential abundance testing. ELVAR is available as an open-source R-package.
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