MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data

Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for ident...

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Bibliographic Details
Published in:Genome Biology 2024-05, Vol.25 (1), p.133-23, Article 133
Main Authors: Wang, Yu, Liu, Zaiyi, Ma, Xiaoke
Format: Article
Language:English
Subjects:
Algorithms
Cells
Clustering
Gene expression
Gene Expression Profiling - methods
Gene Regulatory Networks
Humans
Identification
Integrative analysis
Joint learning
Learning
Method
Morphology
Network model
Spatial discrimination learning
Spatial domain
Spatial transcriptomics
Structure-function relationships
Topological structure
Transcriptome
Transcriptomics
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Summary:Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for identifying spatial domains in spatial transcriptomics data with joint learning. We demonstrate that spatial domains can be precisely characterized and discriminated by the topological structure of cell networks, facilitating identification and interpretability of spatial domains, which outperforms state-of-the-art baselines. Furthermore, we prove that network model offers an effective and efficient strategy for integrative analysis of spatial transcriptomics data from various platforms.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-024-03272-0