Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
A Novel Low-Dimensional Sparse and Low-Rank Representation Method for Single-Cell RNA Sequencing Data Clustering. |
| Συγγραφείς: |
Zhang Z, Shang J, Dai L, Wang J |
| Πηγή: |
IEEE transactions on computational biology and bioinformatics [IEEE Trans Comput Biol Bioinform] 2026 May-Jun; Vol. 23 (3), pp. 1253-1264. |
| Τύπος έκδοσης: |
Journal Article |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: IEEE Country of Publication: United States NLM ID: 9919068173606676 Publication Model: Print Cited Medium: Internet ISSN: 2998-4165 (Electronic) Linking ISSN: 29984165 NLM ISO Abbreviation: IEEE Trans Comput Biol Bioinform Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: [New York, New York] : IEEE, [2025]- |
| Ιατρικοί όροι (MeSH): |
Computational Biology*/methods , Sequence Analysis, RNA*/methods , Clustering Algorithms* , Single-Cell Gene Expression Analysis*, Animals ; Humans ; Dimensionality Reduction |
| Περίληψη: |
The advancement of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to capture cellular heterogeneity at the individual cell level, driving progress in diverse fields such as developmental biology, immunology, and cancer research. Accurate cell clustering is a crucial step for researchers utilizing scRNA-seq data; however, inherent characteristics like high dimensionality and sparsity pose significant challenges to obtaining precise clustering results. To achieve accurate clustering, this paper proposes a novel approach that integrates dimensionality reduction, self-representation matrix construction, and the clustering process into an end-to-end model termed LDSLRR (Low-Dimensional Sparse and Low-Rank Representation). Specifically, the original gene expression matrix first undergoes dimensionality reduction via projection. Subsequently, low-rank representation combined with a sparsity constraint facilitates the learning of the self-representation matrix. Finally, the cluster assignment matrix is acquired using graph-regularized non-negative matrix factorization (NMF). These three modules are simultaneously optimized, enhancing the accuracy of the clustering results. Comparative experiments against multiple state-of-the-art clustering methods on various scRNA-seq datasets demonstrate the superiority of the proposed LDSLRR method. |
| Entry Date(s): |
Date Created: 20260317 Date Completed: 20260611 Latest Revision: 20260615 |
| Update Code: |
20260616 |
| DOI: |
10.1109/TCBBIO.2026.3674992 |
| PMID: |
41843533 |
| Βάση Δεδομένων: |
MEDLINE |