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

TL;DR

FLASH-MM is a fast and efficient method for analyzing gene expression differences in single-cell data while accounting for sample structure and variation.

Contribution

FLASH-MM introduces a reformulated linear mixed-effects model algorithm for scalable and accurate single-cell differential expression analysis.

Findings

FLASH-MM reduces computational complexity and memory usage while maintaining accuracy.

Simulation studies show FLASH-MM effectively controls false positives and maintains high statistical power.

FLASH-MM demonstrates utility in diverse biological contexts like tuberculosis and kidney data.

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 develop 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 usage. 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.

Detecting gene expression changes in single-cell data while accounting for sample structure is vital but computationally demanding. FLASH-MM is a scalable, memory efficient, and statistically robust method that can quickly compute cell-level differential expression across diverse biological contexts.

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