scTECTA: Asymmetric Deep Transfer Learning for Cross-Patient Tumor Microenvironment Single-Cell Annotation

Cellular heterogeneity and dynamic interactions within the tumor microenvironment are critical drivers of cancer initiation and progression. Single-cell RNA sequencing, with its high-resolution capabilities, has significantly advanced the study of cellular heterogeneity in the tumor microenvironment. However, existing single-cell annotation methods are limited by data sparsity, biological heterogeneity, and batch effects, which hinder their broader application in this context. To address this, we propose scTECTA, an innovative graph neural network-based method that employs transfer learning to seamlessly transfer cell-type annotation knowledge from a well-annotated source domain to an unannotated target domain. This approach leverages graph domain adaptation, integrating novel asymmetric neural network architecture and domain-adversarial learning framework. By harnessing the generalization capabilities of graph convolutional network to correct distribution shifts and employing adversarial training to further align expression profiles across batches, scTECTA substantially enhances predictive precision and robustness. We performed a systematic evaluation across multiple datasets from diverse sources, encompassing six cancer types from 34 patients, to compare the cell-type classification performance of scTECTA against 10 benchmark methods. The results demonstrate that scTECTA markedly outperforms benchmark methods in cell-type classification and exhibits robust batch-effect correction, establishing it as an efficient and powerful tool for tumor microenvironment cell-type annotation.