01587nas a2200193   4500000000100000000000100001008004100002260001500043653001300058653002400071100002300095700001800118245008000136856005500216300000900271490000700280520109200287022001401379       2026                            d  c2026-09-2310aSoftware10aStatistical methods1 aGabriel E. Hoffman1 aPanos Roussos00aFast, flexible analysis of differences in cellular composition with crumblr  uhttps://www.nature.com/articles/s41467-026-75681-7  a95980 v173 aChanges 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.  a2041-1723