Nature Communications· 2026Q1
Fast, flexible analysis of differences in cellular composition with crumblr
- 2citations
- Q1SCImago
- 2026year
Short summary
Crumblr, a new statistical method, analyzes cell type composition changes in single-cell RNA-seq data using precision-weighted linear mixed models, outperforming existing methods in simulations by increasing statistical power and controlling false positive rates.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces crumblr, a scalable statistical method for analyzing cell type composition in single-cell RNA-seq data.
- Utilizes precision-weighted linear mixed models with random effects for complex study designs.
- Performs multivariate statistical testing across multiple cell lineage hierarchy levels to boost power.
- Demonstrated to increase power and control false positive rates compared to existing methods in simulations.
- Applied to diverse datasets including aging, tuberculosis, prostate cancer, and SARS-CoV-2 infection.
AI-generated from the title and abstract; the full text is not read.
Abstract
Changes in cell type composition play an important role in human health and disease. Recent advances in single-cell technology have enabled the measurement of cell type composition at increasing cell lineage resolution across large cohorts of individuals. Yet this raises new challenges for statistical analysis of these compositional data to identify changes in cell type frequency. We introduce crumblr ( DiseaseNeurogenomics.github.io/crumblr ), a scalable statistical method for analyzing count ratio data using precision-weighted linear mixed models incorporating random effects for complex study designs. Uniquely, crumblr performs statistical testing at multiple levels of the cell lineage hierarchy using a multivariate approach to increase power over tests of one cell type. In simulations, crumblr increases power compared to existing methods while controlling the false positive rate. We demonstrate the application of crumblr to published single-cell RNA-seq datasets for aging, tuberculosis infection in T cells, bone metastases from prostate cancer, and SARS-CoV-2 infection.
The authors' abstract, as published at the source. Nature Communications, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Molecular Biology
Molecular BiologyBiochemistry, Genetics and Molecular Biology