STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering.

Journal: Computers in biology and medicine

Published Date: Sep 9, 2023

Abstract

BACKGROUND: Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background.

Authors

Lihong Peng

School of Computer Science, Hunan University of Technology, Zhuzhou, China.
Xianzhi He

China Communications Construction Company Second Highway Consultants Co., Ltd., Wuhan 430056, China.
Xinhuai Peng

School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China.
Zejun Li
Li Zhang

Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.

Keywords

Algorithms Breast Neoplasms Cluster Analysis Gene Expression Profiling Humans Neural Networks, Computer Transcriptome

External Resources

View on PubMed Access via DOI PubMed (37738898)

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering.

Abstract

Authors

Keywords

External Resources

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