| Issue |
BIO Web Conf.
Volume 174, 2025
2025 7th International Conference on Biotechnology and Biomedicine (ICBB 2025)
|
|
|---|---|---|
| Article Number | 03016 | |
| Number of page(s) | 8 | |
| Section | Technologies and Methodologies in Biomedical Research | |
| DOI | https://doi.org/10.1051/bioconf/202517403016 | |
| Published online | 12 May 2025 | |
ScBlkCom: An Integrated Compression Algorithm for Single-Cell RNA Sequencing Data
1 School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China
2 Xinge Yuan Biotechnology Co., Ltd., Nanjing 211189, China
* Corresponding author: Fan Jue, fanjue@singleronbio.com; Xiao Sun, xsun@seu.edu.cn
High-throughput sequencing advancements have shifted genomic project bottlenecks from data generation to computational storage and analysis. Single-cell RNA-seq (scRNA-seq) data exhibits unique structural features, including extensive labeled sequence identifiers, which conventional compression tools fail to optimize. This study proposes ScBlkCom, a specialized compression scheme for scRNA-seq data. The method partitions sequencing data into distinct blocks and applies tailored compression strategies: differential encoding for numerical attributes, Huffman coding for categorical labels, and context-adaptive encoding for sequence identifiers. Experiments demonstrate ScBlkCom achieves 84.29% higher compression gain compared to single-module approaches and outperforms generic tools (e.g., GZIP, BZIP2) by 6.44% in compression ratio, while maintaining stable processing speeds. This block-wise adaptive framework effectively addresses scRNA-seq data redundancy, offering enhanced storage efficiency for large-scale single-cell studies.
© The Authors, published by EDP Sciences, 2025
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