Cited by 330Gene expression and cancer classificationBioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

Dream: powerful differential expression analysis for repeated measures designs

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Hoffman, Gabriel E, Roussos, Panos·2021·Bioinformatics

Published version of a preprint

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.”

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Research works printed but not linked: 5

  1. XXX, 2020, Vol. XXX, No. XXX Communications, 8(1), 2225.
  2. Blainey, P., Krzywinski, M., and Altman, N. (2014) Points of Significance: Replication. Nature Methods, 11(9), 879-880.
  3. Wickham, H. (2009) ggplot2, Springer New York, New York, NY.
  4. XXX, 2020, Vol. XXX, No. XXX "NAR˙v2" -2020/1/6 -16:29 -page 20 -#20 · DOI
  5. XXX, 2020, Vol. XXX, No. XXX "NAR˙v2" -2020/1/6 -16:29 -page 22 -#22 · DOI

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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
Published
2020-07-30
Volume
37
Issue
2
Pages
192-201
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
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
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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