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Peer-reviewedCC BYNature Communications

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

  • Gabriel E. Hoffman1,2,3,4,5
  • Donghoon Lee6,7,8,9
  • Jaroslav M. Bendl6,7,8,9
  • N. M. Prashant6,7,8,9
  • Aram Hong6,7,8,9
  • Clara Casey6,7,8,9
  • Marcela Alvia6,7,8,9
  • Zhiping Shao6,7,8,9
  • Stathis Argyriou6,7,8,9
  • Karen Therrien6,7,8,9
  • Sanan Venkatesh6,7,8,9
  • Georgios Voloudakis6,10,7,8,9
3 more authors
  • Vahram H. Haroutunian10,11,8
  • John F. Fullard6,7,8,9
  • Panos Roussos12,13,14,15,16,17
17 affiliations
  1. 1Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  2. 2Center for Precision Medicine and Translational Therapeutics, Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York, NY, USA. [email protected]
  3. 3Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  4. 4Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  5. 5Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  6. 6Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  7. 7Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  8. 8Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  9. 9Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  10. 10Center for Precision Medicine and Translational Therapeutics, Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York, NY, USA
  11. 11Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
  12. 12Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  13. 13Center for Precision Medicine and Translational Therapeutics, Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York, NY, USA. [email protected]
  14. 14Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  15. 15Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  16. 16Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
  17. 17Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected]
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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 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.

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Cite

BibTeXRIS
BibTeX
@article{hoffman2026efficient,
  title = {{Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet}},
  author = {Gabriel E. Hoffman and Donghoon Lee and Jaroslav M. Bendl and N. M. Prashant and Aram Hong and Clara Casey and Marcela Alvia and Zhiping Shao and Stathis Argyriou and Karen Therrien and Sanan Venkatesh and Georgios Voloudakis and Vahram H. Haroutunian and John F. Fullard and Panos Roussos},
  journal = {Nature Communications},
  year = {2026},
  month = sep,
  doi = {10.1038/s41467-026-75680-8},
  url = {https://doi.org/10.1038/s41467-026-75680-8},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Efficient differential expression analysis of large-scale single-cell transcriptomics data using Dreamlet
AU  - Gabriel E. Hoffman
AU  - Donghoon Lee
AU  - Jaroslav M. Bendl
AU  - N. M. Prashant
AU  - Aram Hong
AU  - Clara Casey
AU  - Marcela Alvia
AU  - Zhiping Shao
AU  - Stathis Argyriou
AU  - Karen Therrien
AU  - Sanan Venkatesh
AU  - Georgios Voloudakis
AU  - Vahram H. Haroutunian
AU  - John F. Fullard
AU  - Panos Roussos
PY  - 2026
DA  - 2026/09/23
JO  - Nature Communications
DO  - 10.1038/s41467-026-75680-8
UR  - https://doi.org/10.1038/s41467-026-75680-8
AB  - 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 1.4M single nuclei from postmortem brains of 150 Alzheimer's disease cases and 149 controls.
SN  - 2041-1723
ER  -