Machine Learning–Driven Single-Cell RNA-Seq Analysis for Cell Classification, Feature Discovery, and Functional Annotation in Glioblastoma

Abstract—In computational biology, machine learning is now
essential to the analysis of massive transcriptome datasets. Singlecell RNA sequencing (scRNA-Seq) can be used to study glioblastoma multiforme (GBM), the most prevalent and deadly adult
brain tumour, cell by cell. We developed a repeatable, leakagecontrolled machine learning workflow using the GSE84465
dataset (3,589 cells) to categorise each cell according to its surgical origin (tumour core vs. infiltrating periphery) and determine
the genes that differentiate the two compartments.XGBoost, Logistic Regression, Random Forest, AdaBoost, SVM, LightGBM,
Extra Trees, a Neural Network, and a Stacking Ensemble were
the nine classifiers that were assessed. XGBoost was the most
stable under stratified five-fold cross-validation (0.949 ± 0.007),
with held-out accuracy ranging from 0.92 to 0.95 (ROC-AUC
up to 0.993). Immune and microenvironmental factors (e.g.,
CCL3, MIF, ANXA1/ANXA2, HLA-B, VIM) were enriched in
the top 100 XGBoost-ranked genes. Twenty enriched pathways,
twenty hub genes, and fifteen potential medications were obtained
through downstream pathway/GO enrichment, PPI and hub-gene
analysis, TF/miRNA inference, and drug-protein mining. Overall,
the study demonstrates how interpretable machine learning can
transform GBM scRNA-Seq data into prioritised therapeutic
targets and biologically grounded biomarkers.