Dream: powerful differential expression analysis for repeated measures designs
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Abstract
Summary: Large-scale transcriptome studies with multiple samples per individual are widely used to study disease biology. Yet, current methods for differential expression are inadequate for cross-individual testing for these repeated measures designs. Most problematic, we observe across multiple datasets that current methods can give reproducible false-positive findings that are driven by genetic regulation of gene expression, yet are unrelated to the trait of interest. Here, we introduce a statistical software package, dream, that increases power, controls the false positive rate, enables multiple types of hypothesis tests, and integrates with standard workflows. In 12 analyses in 6 independent datasets, dream yields biological insight not found with existing software while addressing the issue of reproducible false-positive findings. Availability and implementation: Dream is available within the variancePartition Bioconductor package at http://bioconductor.org/packages/variancePartition. Contact: gabriel.hoffman@mssm.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Differential gene expression between MAPT mutation organoids and isogenic controls was predicted by linear mixed model analysis accounting for sample correlations within isogenic lines and differentiation batches.
“Differential gene expression between MAPT mutation organoids and isogenic controls was predicted by linear mixed model analysis using the variancePartition package (v1.14.1) in the R programming environment (Hoffman and Roussos, 2021) to account for sample correlations within isogenic lines and differentiation batches.”
From ELAVL4, splicing, and glutamatergic dysfunction precede neuron loss in MAPT mutation cerebral organoids (2021) · cites this paper as Hoffman and Roussos, 2021
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Impact of genome build on RNA-seq interpretation and diagnostics
Ungar, R. A., Goddard, P. C., Jensen, T. D., Degalez, F., Smith, K. S., Jin, C. A., Undiagnosed Diseases Network, Bonner, D. E., Bernstein, J. A., Wheeler, M. T., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2024.05.005 · Cited by 12 · see citing works
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Fast, flexible analysis of differences in cellular composition with crumblr
Hoffman, G. E., & Roussos, P. (2026) · Nature Communications · doi:10.1038/s41467-026-75681-7 · Cited by 15 · see citing works
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- Authors
- Hoffman, G. E., & Roussos, P.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- James J. Peters VA Medical Center (US)
- Corresponding author
- Hoffman, Gabriel E
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-07-30
- Volume
- 37
- Issue
- 2
- Pages
- 192-201
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC8055218
- OpenAlex
- W3046454618
- Cited by
- 319
- References
- 74
- Citations by year
- 2026: 79 · 2025: 62 · 2024: 69 · 2023: 44 · 2022: 34 · 2021: 27 · 2020: 3 · 2019: 1
- FWCI
- 9.17
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NIMH (U01MH116442, R01MH109677, R01MH109897, R01MH110921)
- NIA (R01AG050986)
- Veterans Affairs merit (BX002395)
- NARSAD Young Investigator Award (26313)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- differential gene expression, repeated measures designs, gene expression profiling, false positive rate control, genetic regulation of gene expression, statistical software, reproducibility
- MeSH terms
- Gene Expression Regulation, Software, Gene Expression Profiling, Transcriptome
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