With the number of cells measured in single-cell RNA sequencing (scRNA-seq) datasets increasing exponentially and concurrent with increased sparsity due to more zero counts being measured for many genes, Bouland et al. (2023) demonstrated that downstream analyses on binary-based gene expression give similar results as count-based analyses. Moreover, a binary representation scales up to ~50-fold more cells that can be analyzed using the same computational resources. They also highlighed the possibility that binarized scRNA-seq data provided by binarized scRNA-seq data. Development of specialized tools for bit-aware implementations of downstream analytical tasks will enable a more fine-grained resolution of biological heterogeneity.
All codes, processed data, and analysis results in the publication by Bouland et al. (2023) are publicly available at GitHub and Zenodo.