Efficient algorithm design and implementation for heterogeneity detection in glioma stem cells

The analysis of single-cell RNA sequencing data from glioblastoma multiforme (GBM) poses significant challenges due to its high dimensionality and complex cellular heterogeneity. Understanding this heterogeneity is crucial for developing effective personalized treatment strategies. To address these challenges, our research team developed an efficient computational framework that integrates multiple advanced algorithms. The framework includes improved Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction for handling high-dimensional data and Graph Convolutional Network (GCN) semi-supervised clustering for accurate cell type identification. Using this approach, we successfully analyzed 157,387 cells and identified 12 distinct cell subpopulations with high accuracy (94.2%). Differential expression analysis revealed 2,387 significantly differentially expressed genes, including key oncogenic drivers such as EGFR and PDGFRA. We further developed a novel heterogeneity scoring system based on information entropy, which quantified intercellular differences and demonstrated that tumor cells exhibit significantly higher heterogeneity compared to non-tumor cells (3.78±0.42 vs 2.13±0.35, p<0.001). Notably, this heterogeneity score showed significant correlation with patient prognosis (Pearson r=-0.63, p=0.002). These findings provide new perspectives for the molecular classification of GBM and establish a foundation for developing more effective personalized treatment strategies. Our computational framework also offers a valuable tool for analyzing complex single-cell datasets in cancer research.