HL-BscPF: Hybrid learning facilitates brain cell auto-identification in multiple pathologies.

Aims: The rapidly growing scale and complexity of single-cell transcriptomic data in brain research make it increasingly difficult for traditional methods to extract meaningful insights efficiently, highlighting the need for artificial intelligence.

Materials And Methods: We presented the Hybrid Learning-based Brain single-cell Prediction Framework (HL-BscPF), designed to automate cell type classification and reveal disease-related pathways in the brain. HL-BscPF integrates ItClust and TOSICA models, combining autoencoder-based dimensionality reduction with transformer architecture to enhance predictive accuracy. HL-BscPF was evaluated using brain scRNA-seq datasets representing various neuropathological states, and its predictive performance was benchmarked against ground-truth annotations.

Key Findings: Applied to four brain-specific single-cell datasets, including aging, Alzheimer's disease, postoperative cognitive dysfunction, and stroke, HL-BscPF accurately classified cell types and uncovered key functional alterations in neuronal and glial populations. TOSICA showed higher accuracy in large-scale datasets due to its multi-head self-attention capabilities, whereas ItClust performed optimally in cases with lower cell diversity, demonstrating their complementary strengths. By providing precise cell identification and novel insights into brain-specific pathway dysregulation, HL-BscPF offers a powerful tool for extracting meaningful insights from vast single-cell datasets, enabling a deeper understanding of the complex neuropathologies.

Significance: HL-BscPF demonstrates exceptional accuracy and interpretability in cell type annotation and functional analysis, uncovering critical disease-related mechanisms. This framework offers a powerful tool for advancing single-cell research in brain pathologies.