Sample-level modeling of single-cell data at scale with tinydenseR
Milanez-Almeida, P.; Schildknecht, D.; Linder, M.; Brachmann, S. M.; Weiss, A.; Adler, F.; Lenticchia, S. C.; Meistertzheim, M.; Wild, S.; Cuttat, R.; Jayaraman, P.; Lee, L. H.; Mulvey, T.; Hassounah, N.; Crafts, G.; Quinn, D. S.; Orlando, E. J.
Show 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 states, outperforming existing workflows in speed, memory usage, and biological resolution. Technology-agnostic at its core, tinydenseR works seamlessly on scRNA-seq, flow, mass and spectral cytometry. Across synthetic benchmarks, a preclinical xenograft model, two immuno-oncology trials and a multi-study atlas, tinydenseR uncovers disease and treatment history-associated effects, including subtle within-cluster heterogeneity. Designed to accelerate discovery in clinical, preclinical, and translational research, the open-source package is available at GitHub.com/Novartis/tinydenseR.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data 97%
- geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq. 96%
- Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction 96%
Similar papers in this journal
Similar papers in this journal
- CelLink: integrating single-cell multi-omics data with weak feature linkage and imbalanced cell populations 96%
- Coralysis enables sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration 96%
- cellHarmony: Cell-level matching and holistic comparison of single-cell transcriptomes 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.