Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet.

Dreamlet enables fast, memory-efficient differential expression analysis of large-scale single-cell transcriptomics data, accurately identifying gene-trait associations across 150 Alzheimer's cases.

• Why it matters: As single-cell datasets grow to include millions of cells from hundreds of subjects, scalable and precise analysis methods are needed to uncover disease biology without excessive computational costs.
• What they did: The open-source R package dreamlet employs a pseudobulk approach with precision-weighted linear mixed models to analyze complex datasets, supporting large cohorts and controlling false positives.
• The result: Applying dreamlet to a dataset of 1.4 million nuclei from postmortem brains demonstrated its superior speed and memory efficiency, enabling detailed insights into cell type-specific gene expression in Alzheimer's disease.