Model Architecture Analysis and Implementation of TENET for Cell–Cell Interaction Network Reconstruction Using Spatial Transcriptomics Data
Cellular communication relies on the intricate interplay of signaling molecules, which come together to form the cell–cell interaction (CCI) network that orchestrates tissue behavior. Researchers have shown that shallow neural networks can effectively reconstruct the CCI from the abundant molecular...
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| Format: | Article |
| Language: | English |
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Bio-protocol LLC
2025-02-01
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| Series: | Bio-Protocol |
| Online Access: | https://bio-protocol.org/en/bpdetail?id=5205&type=0 |
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| _version_ | 1825206639216558080 |
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| author | Ziyang Wang Yujian Lee Yongqi Xu Peng Gao Chuckel Yu Jiaxing Chen |
| author_facet | Ziyang Wang Yujian Lee Yongqi Xu Peng Gao Chuckel Yu Jiaxing Chen |
| author_sort | Ziyang Wang |
| collection | DOAJ |
| description | Cellular communication relies on the intricate interplay of signaling molecules, which come together to form the cell–cell interaction (CCI) network that orchestrates tissue behavior. Researchers have shown that shallow neural networks can effectively reconstruct the CCI from the abundant molecular data captured in spatial transcriptomics (ST). However, in scenarios characterized by sparse connections and excessive noise within the CCI, shallow networks are often susceptible to inaccuracies, leading to suboptimal reconstruction outcomes. To achieve a more comprehensive and precise CCI reconstruction, we propose a novel method called triple-enhancement-based graph neural network (TENET). The TENET framework has been implemented and evaluated on both real and synthetic ST datasets. This protocol primarily introduces our network architecture and its implementation. |
| format | Article |
| id | doaj-art-a816db313c074976b4a3e6dd888407fb |
| institution | Kabale University |
| issn | 2331-8325 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Bio-protocol LLC |
| record_format | Article |
| series | Bio-Protocol |
| spelling | doaj-art-a816db313c074976b4a3e6dd888407fb2025-02-07T08:16:46ZengBio-protocol LLCBio-Protocol2331-83252025-02-0115310.21769/BioProtoc.5205Model Architecture Analysis and Implementation of TENET for Cell–Cell Interaction Network Reconstruction Using Spatial Transcriptomics DataZiyang Wang0Yujian Lee1Yongqi Xu2Peng Gao3Chuckel Yu4Jiaxing Chen5Dept/Center, Guangdong Medical University, Dongguan, ChinaGuangdong Provincial Key Laboratory IRADS, BNU-HKBU UIC, Zhuhai, ChinaDepartment of Computer Science and Technology, Guangdong University of Technology, Guangzhou, ChinaGuangdong Provincial Key Laboratory IRADS, BNU-HKBU UIC, Zhuhai, ChinaIndependent researcher, Guangzhou, ChinaGuangdong Provincial Key Laboratory IRADS, BNU-HKBU UIC, Zhuhai, ChinaCellular communication relies on the intricate interplay of signaling molecules, which come together to form the cell–cell interaction (CCI) network that orchestrates tissue behavior. Researchers have shown that shallow neural networks can effectively reconstruct the CCI from the abundant molecular data captured in spatial transcriptomics (ST). However, in scenarios characterized by sparse connections and excessive noise within the CCI, shallow networks are often susceptible to inaccuracies, leading to suboptimal reconstruction outcomes. To achieve a more comprehensive and precise CCI reconstruction, we propose a novel method called triple-enhancement-based graph neural network (TENET). The TENET framework has been implemented and evaluated on both real and synthetic ST datasets. This protocol primarily introduces our network architecture and its implementation.https://bio-protocol.org/en/bpdetail?id=5205&type=0 |
| spellingShingle | Ziyang Wang Yujian Lee Yongqi Xu Peng Gao Chuckel Yu Jiaxing Chen Model Architecture Analysis and Implementation of TENET for Cell–Cell Interaction Network Reconstruction Using Spatial Transcriptomics Data Bio-Protocol |
| title | Model Architecture Analysis and Implementation of TENET for Cell–Cell Interaction Network Reconstruction Using Spatial Transcriptomics Data |
| title_full | Model Architecture Analysis and Implementation of TENET for Cell–Cell Interaction Network Reconstruction Using Spatial Transcriptomics Data |
| title_fullStr | Model Architecture Analysis and Implementation of TENET for Cell–Cell Interaction Network Reconstruction Using Spatial Transcriptomics Data |
| title_full_unstemmed | Model Architecture Analysis and Implementation of TENET for Cell–Cell Interaction Network Reconstruction Using Spatial Transcriptomics Data |
| title_short | Model Architecture Analysis and Implementation of TENET for Cell–Cell Interaction Network Reconstruction Using Spatial Transcriptomics Data |
| title_sort | model architecture analysis and implementation of tenet for cell cell interaction network reconstruction using spatial transcriptomics data |
| url | https://bio-protocol.org/en/bpdetail?id=5205&type=0 |
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