GraphCVAE: Uncovering cell heterogeneity and therapeutic target discovery through residual and contrastive learning

Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial contexts. However, accurately identifying spatial domains remains challenging due to data sparsity and noise. Traditional clustering methods often f...

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Veröffentlicht in:	Life sciences (1973) Jg. 359; S. 123208
Hauptverfasser:	Zhang, Zhiwei, Wang, Mengqiu, Dai, Ruoyan, Wang, Zhenghui, Lei, Lixin, Zhao, Xudong, Han, Kaitai, Shi, Chaojing, Guo, Qianjin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier Inc 15.12.2024
Schlagworte:	colorectal neoplasms Colorectal Neoplasms - genetics Colorectal Neoplasms - pathology Contrastive learning data collection Deep learning domain extracellular matrix Gene expression Gene Expression Profiling - methods glioblastoma Glioblastoma - genetics Glioblastoma - pathology Humans Machine Learning Neural Networks, Computer Prefrontal Cortex Spatial clustering spatial data Spatial transcriptomics therapeutics Transcriptome transcriptomics Variational graph autoencoder Deep learning Spatial clustering Gene expression Spatial transcriptomics Variational graph autoencoder Contrastive learning
ISSN:	0024-3205, 1879-0631, 1879-0631
Online-Zugang:	Volltext
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