The breakthrough development of single-cell RNA sequencing technology enables tissue heterogeneity analysis at single-cell resolution, where accurate cell type annotation is crucial for unlocking its full potential. To address three key challenges in current annotation methods-scarce labeled data, suboptimal graph topology, and missing cell state information-we propose scALGSL, an innovative framework integrating dynamic graph optimization with active learning. Our core contributions are threefold: (1) A graph-guided active learning mechanism adaptively selects high-value training samples, significantly alleviating label scarcity; (2) A learnable graph structure optimization module dynamically refines adjacency matrices to eliminate spurious connections caused by data sparsity; (3) A novel cell state auxiliary pathway extracts critical functional features via pre-trained models to enhance type discrimination. The systematic review showed that the average accuracy and f1 of scALGSL on the cancer dataset were 0.896 and 0.771, respectively, and it showed good robustness in cross-platform tasks. Integration of cell state information substantially boosts performance, while ablation studies validate the necessity of node selection and edge optimization modules. This framework provides a scalable solution for precise cell annotation, facilitating tumor microenvironment analysis and precision medicine applications.

The tumor microenvironment is a dynamic eco system where cellular interactions drive cancer progression. However, inferring cell-cell communication from non-spatial scRNA-seq data remains challenging due to incomplete li gand-receptor databases and noisy cell type annotations. H ere, we propose scGraphDap, a graph neural network frame work that integrates functional state pseudo-labels and graph structure learning to improve both cell type annotation an d CCC inference. By leveraging pathway activity scores (e. g., angiogenesis, apoptosis) as pseudo-labels, scGraphDap optimizes cell-cell graphs to capture functional proximity be yond geometric similarity. Furthermore, a graph domain adaptation module aligns cell embeddings across patients, enhancing cross-individual generalization. Evaluated on 38,667 cells from 15 patients across three cancers, scGraphDap ac hieved an average accuracy of 82.82%. Statistical validation confirmed its ability to recover disease-specific gene interactions (e.g., STAT3-CD274 in breast invasive carcinoma) without prior knowledge. The source code and data used in this paper can be found in https://github.com/LiYuechao1998/sc GraphDap. Our work provides a unified framework for TME analysis, offering insights into therapeutic target discovery.

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.