A guide to creating design matrices for gene expression experiments
Open accessAbstract
Differential expression analysis of genomic data types, such as RNA-sequencing experiments, use linear models to determine the size and direction of the changes in gene expression. For RNA-sequencing, there are several established software packages for this purpose accompanied with analysis pipelines that are well described. However, there are two crucial steps in the analysis process that can be a stumbling block for many -- the set up an appropriate model via design matrices and the set up of comparisons of interest via contrast matrices. These steps are particularly troublesome because an extensive catalogue for design and contrast matrices does not currently exist. One would usually search for example case studies across different platforms and mix and match the advice from those sources to suit the dataset they have at hand. This article guides the reader through the basics of how to set up design and contrast matrices. We take a practical approach by providing code and graphical representation of each case study, starting with simpler examples (e.g. models with a single explanatory variable) and move onto more complex ones (e.g. interaction models, mixed effects models, higher order time series and cyclical models). Although our work has been written specifically with a limma -style pipeline in mind, most of it is also applicable to other software packages for differential expression analysis, and the ideas covered can be adapted to data analysis of other high-throughput technologies. Where appropriate, we explain the interpretation and differences between models to aid readers in their own model choices. Unnecessary jargon and theory is omitted where possible so that our work is accessible to a wide audience of readers, from beginners to those with experience in genomics data analysis.
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The validity of differential gene expression results strongly depends on capturing the major axis of variation in the statistical model.
“The validity of DGE results strongly depends on the capture of the major axis of variation in the statistical model.”
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All regression analyses were set up as mixed effects models including age, sex, and cell type (microglia or CD45−ve bulk) as fixed effects and mouse ID as a random effect.
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Self-citationlimma powers differential expression analyses for RNA-sequencing and microarray studies
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Self-citationRNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
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Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
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A multiomics approach reveals RNA dynamics promote cellular sensitivity to DNA hypomethylation
Ge, A. Y., Arab, A., Dai, R., Navickas, A., Fish, L., Garcia, K., Asgharian, H., Goudreau, J., Lee, S., Keenan, K., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-77314-9 · Cited by 4 · see citing works
- Authors
- Law, C. W., Zeglinski, K., Dong, X., Alhamdoosh, M., Smyth, G. K., & Ritchie, M. E.
- Affiliations
- The University of Melbourne (AU)
- Walter and Eliza Hall Institute of Medical Research (AU)
- CSL (Australia) (AU)
- Corresponding author
- Law, Charity W.
- Smyth, Gordon K.
- Ritchie, Matthew E.
- ORCID
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2020-12-10
- Volume
- 9
- Pages
- 1444
- ISSN
- 2046-1402 (ISSN-L)
- PMC
- PMC7873980
- OpenAlex
- W3111088326
- Cited by
- 69
- References
- 9
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 23 · 2023: 11 · 2022: 5
- FWCI
- 1.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- F1000Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- Chan Zuckerberg Initiative
- National Health and Medical Research Council (1154970)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- design matrix, differential expression analysis, RNA sequencing, linear models, limma, mixed models, interaction model, time series models, cyclical model, model selection
- MeSH terms
- Gene Expression, Linear Models, Sequence Analysis, RNA, Genomics
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