Sparse Reduced-Rank Regression for Exploratory Visualisation of Paired Multivariate Data
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Abstract
In genomics, transcriptomics, and related biological fields (collectively known as omics), combinations of experimental techniques can yield multiple sets of features for the same set of biological replicates. One example is Patch-seq, a method combining single-cell RNA sequencing with electrophysiological recordings from the same cells. Here we present a framework based on sparse reduced-rank regression (RRR) for obtaining an interpretable visualisation of the relationship between the transcriptomic and the electrophysiological data. We use elastic net regularisation that yields sparse solutions and allows for an efficient computational implementation. Using several Patch-seq datasets, we show that sparse RRR outperforms both sparse full-rank regression and non-sparse RRR, as well as previous sparse RRR approaches, in terms of predictive performance. We introduce a bibiplot visualisation in order to display the dominant factors determining the relationship between transcriptomic and electrophysiological properties of neurons. We believe that sparse RRR can provide a valuable tool for the exploration and visualisation of paired multivariate datasets.
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The selected sparse reduced-rank regression model used 25 genes with a 5-dimensional latent space and achieved a cross-validated R2 of 0.38.
“The selected model used 25 genes with a 5-dimensional latent space and achieved a cross-validated R2 of 0.38.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [32]
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Phenotypic variation of transcriptomic cell types in mouse motor cortex
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., et al. (2021) · Nature · doi:10.1038/s41586-020-2907-3 · Cited by 398 · see citing works
Self-citation
- Authors
- Kobak, D., Bernaerts, Y., Weis, M. A., Scala, F., Tolias, A. S., & Berens, P.
- Affiliations
- University of Tübingen (DE)
- Max Planck Institute for Intelligent Systems (DE)
- Baylor College of Medicine (US)
- Corresponding author
- Kobak, Dmitry
- Berens, Philipp
- Venue
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-08-01
- Volume
- 70
- Issue
- 4
- Pages
- 980-1000
- ISSN
- 0035-9254 (ISSN-L)
- 1467-9876
- OpenAlex
- W4234232768
- Cited by
- 24
- References
- 62
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 6 · 2023: 4 · 2022: 5 · 2020: 1
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of the Royal Statistical Society Series C (Applied Statistics) (publishedVersion, cc-by)
- Funders
- German Ministry of Education and Research (FKZ 01GQ1601)
- German Research Foundation (390727645, BE5601/4-1)
- National Institutes of Health (U19MH114830)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- Patch-seq, single-cell RNA sequencing, electrophysiology, multi-omics integration, elastic net, neurons
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