Sample-level modeling of single-cell data at scale with tinydenseR

Abstract

Single-cell studies now routinely encompass hundreds of samples and millions of cells, offering unprecedented opportunities to link sample-level phenotypes with cellular and molecular states. However, current workflows often depend on cell-level inference and rigid clustering, which can distort significance and obscure subtle, continuous variation, in particular for complex experimental designs. Here, we present tinydenseR, a clustering-independent framework that enables robust, scalable, and statistically sensitive detection of differential cell state density, outperforming existing workflows in speed and memory usage. Technology-agnostic at its core, tinydenseR works seamlessly on scRNA-seq, flow, mass and spectral cytometry. Across synthetic benchmarks, a preclinical xenograft model, a publicly available COVID-19 study, two immuno-oncology trials and a multi-study atlas, tinydenseR uncovers disease and treatment history-associated effects, including subtle cell state heterogeneity, while embedding samples in a quantitative manner. Designed to accelerate discovery in clinical, preclinical, and translational research, the open-source package is available at GitHub.com/Novartis/tinydenseR.