用图神经网络从非空间单细胞数据中推断肿瘤微环境细胞互作关系。
Graph Structure Learning for Tumor Microenvironment with Cell Type Annotation from non-spatial scRNA-seq data
- 构建GNN模型scGSL，结合基因表达与潜在细胞互作关系建模。
- 在4.9万细胞上实现84.83%准确率，优于现有方法。
- 无需标注即可发现关键基因对的生物学意义互作，适合癌症研究者。
通过单细胞RNA测序（scRNA-seq）探索肿瘤微环境（TME）中的细胞异质性对理解癌症进展和治疗反应至关重要。然而，现有scRNA-seq方法缺乏空间信息，且依赖不完整的配体-受体互作（LRIs）数据，限制了细胞类型注释与细胞间通信（CCC）推断的准确性。本研究提出一种新型图神经网络（GNN）模型scGSL，显著提升细胞类型预测与细胞互作分析能力。研究基于19名患者、三种癌种（白血病、乳腺浸润性癌、结直肠癌）的49,020个细胞数据集，scGSL模型在所有数据集上平均准确率达84.83%，精确率为86.23%，召回率为81.51%，F1得分为80.92%，显著优于现有方法。通过整合文献中的基因互作知识，scGSL在无监督条件下仍能稳健识别具有生物学意义的基因互作，其关键基因对在不同癌症中表现出显著表达差异。代码与数据见：https://github.com/LiYuechao1998/scGSL。
原文摘要 · Abstract (English)
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.
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