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

Abstract

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.