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Nat. Commun., Published: 02 July 2025 | https://doi.org/10.1038/s41467-025-60702-8
scICE: enhancing clustering reliability and efficiency of scRNA-seq data with multi-cluster label consistency evaluation
Hyun Kim1, Issac Park2, Jong-Eun Park3, Jong Kyoung Kim4, Minseok Seo5 & Jae Kyoung Kim1,6,7
1Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Republic of Korea.
2Department of Mathematics, Pusan National University, Busan, Republic of Korea.
3Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea.
4Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
5Department of Computer and Information Science, Korea University, Sejong, Republic of Korea.
6Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea.
7Department of Medicine, College of Medicine, Korea University, Seoul, Republic of Korea.
Corresponding author
Correspondence to Jae Kyoung Kim.
1Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, Republic of Korea.
2Department of Mathematics, Pusan National University, Busan, Republic of Korea.
3Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea.
4Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
5Department of Computer and Information Science, Korea University, Sejong, Republic of Korea.
6Department of Mathematical Sciences, KAIST, Daejeon, Republic of Korea.
7Department of Medicine, College of Medicine, Korea University, Seoul, Republic of Korea.
Corresponding author
Correspondence to Jae Kyoung Kim.
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.
논문정보
- Research article
- 2025년 07월 (BRIC 등록일 2025-07-04)
- 국내(교신)+국외 연구진
- 바이오·의료융합 > 다학제 융합/복합
관련 보도자료
정확도·효율성 높인 생명과학 데이터 분석 도구 ‘scICE’ 개발
기존보다 최대 30배 빠른 속도로 안정적인 결과만을 자동으로 선별하여 대규모 생명과학 데이터 분석의 정확도와 효율성을 획기적으로 향상하는 방법이 나왔다. 기초과학연구원(IBS, 원장 노도영) 수리 및 계산 과학 연구단 의생명 수학 그룹 김재경 CI(KAIST 수리과학과 교수) 연구팀은 세포 분류(클러스터링) 결과의 안정성을 수학적으로 평가해 불안정한 결과를 걸러내는 새로운 분석 도구인 ‘scICE(single-cell Inconsistency Clustering Estimator)’를 개발했다.
- Bio통신원
- 2025. 07. 03
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