SCIM: Universal Single-Cell Matching with Unpaired Feature Sets
Stefan G. Stark, Joanna Ficek, Francesco Locatello, Ximena Bonilla, Stéphane Chevrier, Franziska Singer, Ruedi H. Aebersold, Faisal Alquaddoomi, Jonas B. Albinus, Ilaria Alborelli, Sonali Andani, Per-Olof Attinger, Marina Bacac, Daniel Baumhoer, Beatrice Beck‐Schimmer, , Vipin T. Sreedharan
SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.
Bioinformatics · Sep 14, 2020Peer-reviewedBioinformatics