SCimilarity is a unifying representation of single cell expression profiles that quantifies similarity between expression states and generalizes to represent new studies without additional training.
This enables a novel cell search capability, which sifts through millions of profiles to find cells similar to a query cell state and allows researchers to quickly and systematically leverage massive public scRNA-seq atlases to learn about a cell state of interest.
Tutorials and API documentation can be found at: https://genentech.github.io/scimilarity/index.html
The latest API release can be installed from PyPI:
pip install scimilarity
A docker container with SCimilarity preinstalled can be pulled from: https://ghcr.io/genentech/scimilarity
Pretrained model weights, embeddings, kNN graphs, a single-cell metadata can be downloaded from: https://zenodo.org/records/10685499
Human tissue atlas scRNA-seq data with metadata and SCimilarity embeddings can be downloaded from: https://zenodo.org/records/10895214
To cite SCimilarity in publications please use:
A cell atlas foundation model for scalable search of similar human cells. Graham Heimberg*, Tony Kuo*, Daryle J. DePianto, Tobias Heigl, Nathaniel Diamant, Omar Salem, Gabriele Scalia, Tommaso Biancalani, Jason R. Rock, Shannon J. Turley, Héctor Corrada Bravo, Josh Kaminker**, Jason A. Vander Heiden**, Aviv Regev**. Nature (2024). https://doi.org/10.1038/s41586-024-08411-y