ScValue: value-based subsampling of large-scale single-cell transcriptomic data for machine and deep learning tasks.

ABSTRACT: Large single-cell ribonucleic acid-sequencing (scRNA-seq) datasets offer unprecedented biological insights but present substantial computational challenges for visualization and analysis. While existing subsampling methods can enhance efficiency, they may not ensure optimal performance in downstream machine learning and deep learning (ML/DL) tasks. Here, we introduce scValue, a novel approach that ranks individual cells by 'data value' using out-of-bag estimates from a random forest model. scValue prioritizes high-value cells and allocates greater representation to cell types with higher variability in data value, effectively preserving key biological signals within subsamples. We benchmarked scValue on automatic cell-type annotation tasks across four large datasets, paired with distinct M

SUBMITTER: Huang L 

PROVIDER: S-EPMC12165832 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature