ScBlkCom: An Integrated Compression Algorithm for Single-Cell RNA Sequencing Data

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.