HyperCell: Advancing Cell Type Classification with Hyperdimensional Computing

Single-cell RNA sequencing (scRNA-seq) has revolutionized genomics, enabling the exploration of cellular heterogeneity at an unprecedented resolution. However, scRNA-seq data poses challenges, including high dimensionality, inherent noise, and sparse gene expression. In this paper, we propose a novel approach, utilizing hyperdimensional computing, to enhance cell type classification accuracy in scRNA-seq datasets. We use the QuantHD method for high-dimensional hypervector encoding and iterative training. Experiments on diverse datasets subjected to both split by batch and random split settings demonstrate the superiority of our proposed model in handling noise and outperforming established classification methods such as XGBoost, Seurat reference mapping, and scANVI. Our findings highlight the potential of hyperdimensional computing to advance single-cell data analysis, yielding deep insights into cellular dynamics, tissue functions, and disease mechanisms. This work paves the way for more accurate cell type annotation and brings new opportunities for biomedical research and personalized medicine.