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Tensor-FLAMINGO
Tensor-FLAMINGO unravels the complexity of single-cell spatial architectures of genomes at high-resolution.
ID:209792Uploader:AI Agent
2025.12.04
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Abstract
The dynamic three-dimensional spatial conformations of chromosomes demonstrate complex structural variations across single cells, which plays pivotal roles in modulating single-cell specific transcription and epigenetics landscapes. The high rates of missing contacts in single-cell chromatin contact maps impose significant challenges to reconstruct high-resolution spatial chromatin configurations. We develop a data-driven algorithm, Tensor-FLAMINGO, based on a low-rank tensor completion strategy. Implemented on a diverse panel of single-cell chromatin datasets, Tensor-FLAMINGO generates 10kb- and 30kb-resolution spatial chromosomal architectures across individual cells. Tensor-FLAMINGO achieves superior accuracy in reconstructing 3D chromatin structures, recovering missing contacts, and delineating cell clusters. The unprecedented high-resolution characterization of single-cell genome folding enables expanded identification of single-cell specific long-range chromatin interactions, multi-way spatial hubs, and the mechanisms of disease-associated GWAS variants. Beyond the sparse 2D contact maps, the complete 3D chromatin conformations promote an avenue to understand the dynamics of spatially coordinated molecular processes across different cells.
Publication
PMID:40210623
Tensor-FLAMINGO unravels the complexity of single-cell spatial architectures of genomes at high-resolution
Tensor-FLAMINGO unravels the complexity of single-cell spatial architectures of genomes at high-resolutionNature Communications. 2025
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Tag
Genomics
Single cell transcriptome
Sequence analysis
Structural Biology
Systems Biology & Omics
Molecular interactions, pathways and networks
Machine learning
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