Scalable, Fast and Accurate Differential Gene Expression Testing from Millions of Cells of Multiple Patients

Key points

Core problem: cells in single-cell RNA-seq are statistically dependent, which breaks the assumptions of traditional differential expression tests designed for independent bulk samples.
Bayesian framework: it restores the independence structure at the patient level, cleanly separating inter-individual variation from true transcriptional differences.
Multi-GPU variational inference enables computational scalability to more than 10 million cells across thousands of samples from clinical cohorts, atlas projects, and drug-response screens.
Practical impact: it makes single-cell differential expression analysis possible at scale for the first time, without the computational bottlenecks or mathematical limitations of existing methods.

External research summaries. These are not HDATF publications or measured product results.