Accelerating Marker Gene Selection in Scrna-Seq

Single-cell RNA sequencing (scRNA-seq) generates vast amounts of data, enabling gene expression profiling at the resolution of individual cells. Accurate classification of cells by their tissue of origin relies on selecting informative marker genes that distinguish cell types while minimizing redundancy. However, identifying an optimal subset of genes presents a challenging combinatorial optimization problem in scRNA-seq analysis. In this paper, we focus on enhancing scGeneFit, a state-of-the-art marker gene selection method. We introduce optimized solvers and nearest-neighbor search techniques to improve both accuracy and computational efficiency. Through systematic benchmarking on real-world datasets, we demonstrate that our modifications yield superior performance compared to the original implementation.