scCODA is a Bayesian model for compositional single-cell data analysis
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Abstract
Compositional changes of cell types are main drivers of biological processes. Their detection through single-cell experiments is difficult due to the compositionality of the data and low sample sizes. We introduce scCODA ( https://github.com/theislab/scCODA ), a Bayesian model addressing these issues enabling the study of complex cell type effects in disease, and other stimuli. scCODA demonstrated excellent detection performance, while reliably controlling for false discoveries, and identified experimentally verified cell type changes that were missed in original analyses.
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scCODA analysis identified a credible compositional change (FDR < 0.05) in the rCap endothelial cell cluster between mouse lungs treated with PBS and those treated with B16 melanoma cells at 6 h.
“scCODA identified a credible change (FDR < 0.05) in rCap cluster between mice treated with either PBS or B16 melanoma cells.”
From Endothelial Pim3 kinase protects the vascular barrier during lung metastasis (2024) · cites this paper as [58]
The Results passages report scCODA findings in other datasets, including COVID-19 samples at an FDR level of 0.2, but do not address rCap endothelial cells or a PBS-versus-B16 melanoma comparison. Searches for “rCap” and “B16 melanoma PBS 6 h” found no passage reporting the claimed change at FDR < 0.05. (LLM, conf=0.76)
Experimental — accuracy not yet measuredUnivariate statistical models may perceive some cell-type population shifts as statistically sound effects although they are purely a statistical artefact caused by the compositionality of the data, leading to an elevated false discovery rate.
“Univariate statistical models, which analyse change in abundance for each cell type individually, such as Poisson regression or Wilcoxon rank-sum tests, may perceive some cell-type population shifts as statistically sound effects, although they are purely a statistical artefact caused by the compositionality of the data108, leading to an elevated FDR.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [108]
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References 50 · Cited by 388
Research works printed but not linked: 2
- [41]Büttner, M., Ostner, J., Müller, C. L., Theis, F. J. & Schubert, B. scCODA is a Bayesian model for compositional single-cell data analysis. https://github.com/theislab/scCODA, 10.5281/zenodo.5578025 (2021). PMC8616929 34824236 · DOI
- [42]Büttner, M., Ostner, J., Müller, C. L., Theis, F. J. & Schubert, B. Reproducibility repository—scCODA is a Bayesian model for compositional single-cell data analysis. https://github.com/theislab/scCODA_reproducibility, 10.5281/zenodo.5578002 (2021). PMC8616929 34824236 · DOI
- Authors
- Büttner, M., Ostner, J., Müller, C. L., Theis, F. J., & Schubert, B.
- Affiliations
- Helmholtz Munich (DE)
- Corresponding author
- Müller, C. L.
- Schubert, B.
- Venue
- Nature Communications
- Published
- 2021-11-25
- Volume
- 12
- Issue
- 1
- Pages
- 6876-6876
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC8616929
- OpenAlex
- W3217151720
- Cited by
- 388
- References
- 50
- Citations by year
- 2026: 117 · 2025: 89 · 2024: 93 · 2023: 49 · 2022: 32 · 2021: 8
- FWCI
- 16.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
- mediaTUM (Technical University of Munich) (submittedVersion, other-oa)
- Site cant be reached (submittedVersion, cc-by)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- Funders
- Helmholtz Zentrum München
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- compositional data, single-cell RNA sequencing, cell type composition, Bayesian model, false discovery rate control, small sample size
- MeSH terms
- Bayes Theorem, Humans, Models, Statistical, Sample Size, Benchmarking, Gene Expression Profiling, Single-Cell Analysis
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