Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamlet Article Swipe
Gabriel E. Hoffman
,
Donghoon Lee
,
Jaroslav Bendl
,
N. M. Prashant
,
Aram Hong
,
Clara Casey
,
Marcela Alvia
,
Zhiping Shao
,
Stathis Argyriou
,
Karen Therrien
,
Sanan Venkatesh
,
Georgios Voloudakis
,
Vahram Haroutunian
,
John F. Fullard
,
Panos Roussos
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.17.533005
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.17.533005
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.
Related Topics
Concepts
Workflow
Computer science
Scale (ratio)
Computational biology
Differential (mechanical device)
Transcriptome
Scaling
False discovery rate
R package
Expression (computer science)
Data mining
Biology
Gene expression
Gene
Mathematics
Genetics
Computational science
Cartography
Geography
Geometry
Aerospace engineering
Engineering
Programming language
Database
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.03.17.533005
- https://www.biorxiv.org/content/biorxiv/early/2023/03/20/2023.03.17.533005.full.pdf
- OA Status
- green
- Cited By
- 35
- References
- 76
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4327905864
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4327905864Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.03.17.533005Digital Object Identifier
- Title
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Efficient differential expression analysis of large-scale single cell transcriptomics data using dreamletWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-20Full publication date if available
- Authors
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Gabriel E. Hoffman, Donghoon Lee, Jaroslav Bendl, N. M. Prashant, Aram Hong, Clara Casey, Marcela Alvia, Zhiping Shao, Stathis Argyriou, Karen Therrien, Sanan Venkatesh, Georgios Voloudakis, Vahram Haroutunian, John F. Fullard, Panos RoussosList of authors in order
- Landing page
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https://doi.org/10.1101/2023.03.17.533005Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2023/03/20/2023.03.17.533005.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2023/03/20/2023.03.17.533005.full.pdfDirect OA link when available
- Concepts
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Workflow, Computer science, Scale (ratio), Computational biology, Differential (mechanical device), Transcriptome, Scaling, False discovery rate, R package, Expression (computer science), Data mining, Biology, Gene expression, Gene, Mathematics, Genetics, Computational science, Cartography, Geography, Geometry, Aerospace engineering, Engineering, Programming language, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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35Total citation count in OpenAlex
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2025: 20, 2024: 15Per-year citation counts (last 5 years)
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76Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| countries_distinct_count | 1 |
| institutions_distinct_count | 15 |
| corresponding_institution_ids | https://openalex.org/I1314596251, https://openalex.org/I4210116369, https://openalex.org/I4210119030, https://openalex.org/I98704320 |
| citation_normalized_percentile.value | 0.97178501 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |