Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Graph-Embedded Deep Generative Clustering for Single-Cell Multi-Omics Data Integration. |
| Συγγραφείς: |
Liang C, Chen W, Gao L, Wang CD, Zhang S, Guo F |
| Πηγή: |
IEEE transactions on pattern analysis and machine intelligence [IEEE Trans Pattern Anal Mach Intell] 2026 Jul; Vol. 48 (7), pp. 7890-7901. |
| Τύπος έκδοσης: |
Journal Article; Research Support, Non-U.S. Gov't |
| Γλώσσα: |
English |
| Στοιχεία περιοδικού: |
Publisher: IEEE Computer Society Country of Publication: United States NLM ID: 9885960 Publication Model: Print Cited Medium: Internet ISSN: 1939-3539 (Electronic) Linking ISSN: 00985589 NLM ISO Abbreviation: IEEE Trans Pattern Anal Mach Intell Subsets: MEDLINE |
| Imprint Name(s): |
Original Publication: [New York] IEEE Computer Society. |
| Ιατρικοί όροι (MeSH): |
Computational Biology*/methods , Multiomics*/methods , Single-Cell Analysis*/methods , Clustering Algorithms* , Deep Learning* , Generative Artificial Intelligence*, Animals ; Humans ; Cluster Analysis |
| Περίληψη: |
The advancement of sequencing technologies has generated an unprecedented volume of single-cell multi-omics data, providing new opportunities for biological discovery and medical research. However, due to the high heterogeneity across different omics types, effective integration of single-cell multi-omics data remains a critical challenge. Existing methods generally ignore the graph structure information among cells or resort to additional knowledge to construct the cell graphs, leading to suboptimal performance and potentially limited practical utility. In this study, we propose a novel Graph-embedded Deep Generative Clustering model (GeDGC) for single-cell multi-omics data integration. Specifically, GeDGC simultaneously learns the shared latent representations and cluster factors across multiple omics by leveraging Gaussian mixture models. Moreover, we impose the graph embedding constraint on both the latent representations and the cluster assignments to ensure the preservation of intrinsic local data structure among cells. As a result, our model captures complex correlations across omics and obtains informative shared latent embeddings for downstream tasks. Extensive experimental results with seventeen competing methods on ten datasets confirm the superiority of GeDGC in single-cell multi-omics data integration. |
| Entry Date(s): |
Date Created: 20260224 Date Completed: 20260611 Latest Revision: 20260615 |
| Update Code: |
20260615 |
| DOI: |
10.1109/TPAMI.2026.3667525 |
| PMID: |
41734125 |
| Βάση Δεδομένων: |
MEDLINE |