Hyperdimensional Computing Approaches in Single Cell RNA Sequencing Classification

Petros Barmpas, Sotiris K. Tasoulis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos

Abstract: Single-cell RNA sequencing (scRNA-seq) represents a paradigm shift in understanding the complexities of cellular functions and states at an individual level. Despite its transformative potential in revealing cellular heterogeneity and mechanisms, scRNA-seq data poses significant analytical challenges due to its high dimensionality, noise, and sparsity. This paper introduces Hyperdimensional Computing (HDC) as an alternative classification approach, suited for addressing these challenges. HDC, characterized by its robustness to noise and efficiency in high-dimensional spaces, accommodates the sparsity and variability inherent in scRNA-seq for the classification and analysis of scRNA-seq data offerring a promising pathway. This study aims to explore the application of HDC in scRNA-seq data classification, benchmarking its performance against traditional methods, and discussing its potential to enhance single-cell transcriptomic analysis. This work not only contributes to advancing the computational methodologies available for scRNA-seq analysis but also establishes a foundation for future research into scalable and robust data-driven approaches in genomics.