ArticleNature biotechnology2026
Shortcomings of silhouette in single-cell integration benchmarking.
Article in Nature biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed.
- Unsupervised clustering of electroclinical features in temporal lobe epilepsy: A data-driven approach.Epileptic disorders : international epilepsy journal with videotape · 2026Article
- IntegrateRigor: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches.bioRxiv : the preprint server for biology · 2026Article
- ProtoCloud: A prototypical self-explaining model for single-cell analysis.Cell genomics · 2026Article
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- Spatiotemporal transcriptomic mapping reveals region-specific glial activation and astrocyte shifts in epileptogenesis beyond the hippocampus.Acta neuropathologica communications · 2026Article
- Stack: In-Context Learning of Single-Cell Biology.bioRxiv : the preprint server for biology · 2026Article
- A pan-cancer single cell landscape reveals heterogeneity and functional diversity of double-negative T cells.Molecular cancer · 2026Article
- Single-cell transcriptomic analysis reveals the immunosuppressive status of NK cell subpopulations in TNBC.PloS one · 2026Article
- A latent factor framework to organize regulatory and metabolic programs inferred from scRNA-seq.Bioinformatics advances · 2026Article
- Development and comparison of evaluation metrics for batch correction reveals performance differences.Bioinformatics advances · 2026Article
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
- Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- Revealing a coherent cell state landscape across single cell datasets with CONCORD.bioRxiv : the preprint server for biology · 2025Article
- PRTS: Predicting Single-Cell Spatial Transcriptomic Maps from Histological Images.Research (Washington, D.C.) · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Single-cell studies rely on advanced integration methods for complex datasets affected by batch effects from technical factors alongside meaningful biological variation. Silhouette is an established metric for assessing unsupervised clustering results, comparing within-cluster cohesion to between-cluster separation. However, silhouette's assumptions are typically violated in single-cell data integration scenarios. We demonstrate that silhouette-based metrics cannot reliably assess batch effect removal or biological signal conservation and propose more robust evaluation strategies.
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Registered trials
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.