scICE: enhancing clustering reliability and efficiency of scRNA-seq data with multi-cluster label consistency evaluation
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 b...
Saved in:
| Main Authors: | Hyun Kim, Issac Park, Jong-Eun Park, Jong Kyoung Kim, Minseok Seo, Jae Kyoung Kim |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60702-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data.
by: Xiaopeng Wei, et al.
Published: (2025-04-01) -
scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data
by: Lin Yuan, et al.
Published: (2025-04-01) -
scCompass: An Integrated Multi‐Species scRNA‐seq Database for AI‐Ready
by: Pengfei Wang, et al.
Published: (2025-07-01) -
A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering
by: Binhua Tang, et al.
Published: (2025-08-01) -
CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data
by: Wanlin Juan, et al.
Published: (2025-01-01)