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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 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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“Combined isogenic analysis with DESeq2 identified more DEGs than individual paired analysis using DREAM (Differential expression for REpeAted Measures) (Hoffman and Roussos, 2020) (Figure 3H).”
From Cholesterol and matrisome pathways dysregulated in astrocytes and microglia (2022) · cites this paper as Hoffman and Roussos, 2020
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Metadata
- 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
- 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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