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April 15th, 2025
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University of Toronto
bioinformatics
biorxiv

FLASH-MM: fast and scalable single-cell differential expression analysis using linear mixed-effects models

Xu, C.Open in Google Scholar•Pouyabahar, D.Open in Google Scholar•Voisin, V.Open in Google Scholar•Heydari, H.Open in Google Scholar•Bader, G.Open in Google Scholar

Single-cell RNA sequencing (scRNA-seq) enables detailed comparisons of gene expression across cells and conditions. Single-cell differential expression analysis faces challenges like sample correlation, individual variation, and scalability. We developed a fast and scalable linear mixed-effects model (LMM) estimation algorithm, FLASH-MM, to address these issues. We reformulate aspects of the model estimation procedure to make it faster, by reducing computational complexity and memory use in the case of working with a gene by cell matrix. Simulation studies with scRNA-seq data show that FLASH-MM is accurate, computationally efficient, effectively controls false positive rates, and maintains high statistical power in differential expression analysis. Tests on tuberculosis immune and kidney single-cell data demonstrate FLASH-MM\'s utility in accelerating single-cell differential expression analysis across diverse biological contexts.

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