ArticleNature communications2025
scICE: enhancing clustering reliability and efficiency of scRNA-seq data with multi-cluster label consistency evaluation.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- How AI Can Advance Mathematical Biology: Opportunities, Challenges, and Future Directions.Bulletin of mathematical biology · 2026Review
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.Research square · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.bioRxiv : the preprint server for biology · 2026Article
- scMagnifier: Resolving fine-grained cell subtypes via GRN-informed perturbations and consensus clustering.PLoS computational biology · 2026Article
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- Tracing cell communication programs across conditions at single cell resolution with CCC-RISE.bioRxiv : the preprint server for biology · 2026Article
- scICE: enhancing clustering reliability and efficiency of scRNA-seq data with multi-cluster label consistency evaluation.Nature communications · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Clustering analysis is a fundamental step in scRNA-seq data analysis. However, its reliability is compromised by clustering inconsistency among trials due to stochastic processes in clustering algorithms. Despite efforts to obtain reliable and consensus clustering, existing methods cannot be applied to large scRNA-seq datasets due to high computational costs. Here, we develop the single-cell Inconsistency Clustering Estimator (scICE) to evaluate clustering consistency and provide consistent clustering results, achieving up to a 30-fold improvement in speed compared to conventional consensus clustering-based methods, such as multiK and chooseR. Application of scICE to 48 real and simulated scRNA-seq datasets, some with over 10,000 cells, successfully identifies all consistent clustering results, substantially narrowing the number of clusters to explore. By enabling the focus on a narrower set of more reliable candidate clusters, users can greatly reduce computational burden while generating more robust results.
Indexed as
Identifiers
What OpenQuestion holds
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.