HyperCell: Advancing Cell Type Classification with Hyperdimensional Computing
Document Type
Article
Publication Date
12-17-2024
Identifier/URL
42198986 (Pure)
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Abstract
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.
Repository Citation
Mohammadi, H.,
Baranpouyan, M.,
Thirunarayan, K.,
& Chen, L.
(2024). HyperCell: Advancing Cell Type Classification with Hyperdimensional Computing. 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings.
https://corescholar.libraries.wright.edu/cse/661
DOI
10.1109/EMBC53108.2024.10782122
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