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

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet ( DiseaseNeurogenomics.github.io/dreamlet ) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4 M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

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Publication Details

Journal
Nature Communications
Published
2026-09-23
DOI
https://doi.org/10.1038/s41467-026-75680-8
Citations
6
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
15.02

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article

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

N. M. Prashant, Sanan Venkatesh, John F. Fullard, Zhiping Shao et al.
6 citations
Nature Communications
Single-cell and spatial transcriptomics
15.02
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Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet

N. M. Prashant, Sanan Venkatesh, John F. Fullard, Zhiping Shao, Marcela Alvia, Georgios Voloudakis, Gabriel E. Hoffman, Karen Therrien, Jaroslav M. Bendl, Vahram H. Haroutunian, Clara Casey, Panos Roussos, Aram Hong, Stathis Argyriou, Donghoon Lee
article en
6 citations

Abstract

Advances in single-cell and -nucleus transcriptomics have enabled generation of increasingly large-scale datasets from hundreds of subjects and millions of cells. These studies promise to give unprecedented insight into the cell type specific biology of human disease. Yet performing differential expression analyses across subjects remains difficult due to challenges in statistical modeling of these complex studies and scaling analyses to large datasets. Our open-source R package dreamlet ( DiseaseNeurogenomics.github.io/dreamlet ) uses a pseudobulk approach based on precision-weighted linear mixed models to identify genes differentially expressed with traits across subjects for each cell cluster. Designed for data from large cohorts, dreamlet is substantially faster and uses less memory than existing workflows, while supporting complex statistical models and controlling the false positive rate. We demonstrate computational and statistical performance on published datasets, and a novel dataset of 1.4 M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

Nature CommunicationsVol. 17(1)
Allen Institute for Brain Science (US), James J. Peters VA Medical Center (US), Icahn School of Medicine at Mount Sinai (US)
National Institute on Aging, National Institute of Mental Health, National Institute on Drug Abuse, National Institute of Neurological Disorders and Stroke
Openalex Percentile: Top 1%
Single-cell and spatial transcriptomics
15.02
DOIOpen Access PDFOpenAlex
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