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

Xu, C.; Pouyabahar, D.; Voisin, V.; Heydari, H.; Bader, G.

doi:10.1101/2025.04.08.647860

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bioRxiv · 10.1101/2025.04.08.647860

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

Xu, C.·Pouyabahar, D.·Voisin, V.·Heydari, H.·Bader, G.

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Abstract

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 linear mixed 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-MMs utility in accelerating single-cell differential expression analysis across diverse biological contexts.

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Xu, C., Pouyabahar, D., Voisin, V., Heydari, H., Bader, G.. 2025-04-15. FLASH-MM: fast and scalable single-cell differential expression analysis using linear mixed-effects models. https://doi.org/10.1101/2025.04.08.647860

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