Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data
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arXiv
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| Format: | Preprint |
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2025
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| author | Huang, Yu-An Li, Yue-Chao You, Hai-Ru Pan, Jie Cao, Xiyue Li, Xinyuan Huang, Zhi-An You, Zhu-Hong |
| author_facet | Huang, Yu-An Li, Yue-Chao You, Hai-Ru Pan, Jie Cao, Xiyue Li, Xinyuan Huang, Zhi-An You, Zhu-Hong |
| contents | The exploration of cellular heterogeneity within the tumor microenvironment (TME) via single-cell RNA sequencing (scRNA-seq) is essential for understanding cancer progression and response to therapy. Current scRNA-seq approaches, however, lack spatial context and rely on incomplete datasets of ligand-receptor interactions (LRIs), limiting accurate cell type annotation and cell-cell communication (CCC) inference. This study addresses these challenges using a novel graph neural network (GNN) model that enhances cell type prediction and cell interaction analysis. Our study utilized a dataset consisting of 49,020 cells from 19 patients across three cancer types: Leukemia, Breast Invasive Carcinoma, and Colorectal Cancer. The proposed scGSL model demonstrated robust performance, achieving an average accuracy of 84.83%, precision of 86.23%, recall of 81.51%, and an F1 score of 80.92% across all datasets. These metrics represent a significant enhancement over existing methods, which typically exhibit lower performance metrics. Additionally, by reviewing existing literature on gene interactions within the TME, the scGSL model proves to robustly identify biologically meaningful gene interactions in an unsupervised manner, validated by significant expression differences in key gene pairs across various cancers. The source code and data used in this paper can be found in https://github.com/LiYuechao1998/scGSL. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02629 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data Huang, Yu-An Li, Yue-Chao You, Hai-Ru Pan, Jie Cao, Xiyue Li, Xinyuan Huang, Zhi-An You, Zhu-Hong Genomics Artificial Intelligence Machine Learning The exploration of cellular heterogeneity within the tumor microenvironment (TME) via single-cell RNA sequencing (scRNA-seq) is essential for understanding cancer progression and response to therapy. Current scRNA-seq approaches, however, lack spatial context and rely on incomplete datasets of ligand-receptor interactions (LRIs), limiting accurate cell type annotation and cell-cell communication (CCC) inference. This study addresses these challenges using a novel graph neural network (GNN) model that enhances cell type prediction and cell interaction analysis. Our study utilized a dataset consisting of 49,020 cells from 19 patients across three cancer types: Leukemia, Breast Invasive Carcinoma, and Colorectal Cancer. The proposed scGSL model demonstrated robust performance, achieving an average accuracy of 84.83%, precision of 86.23%, recall of 81.51%, and an F1 score of 80.92% across all datasets. These metrics represent a significant enhancement over existing methods, which typically exhibit lower performance metrics. Additionally, by reviewing existing literature on gene interactions within the TME, the scGSL model proves to robustly identify biologically meaningful gene interactions in an unsupervised manner, validated by significant expression differences in key gene pairs across various cancers. The source code and data used in this paper can be found in https://github.com/LiYuechao1998/scGSL. |
| title | Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data |
| topic | Genomics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2502.02629 |