SCIM: universal single-cell matching with unpaired feature sets
Authors: Stark Stefan G , Ficek Joanna , Locatello Francesco , Bonilla Ximena , Chevrier Stéphane , Singer Franziska ,   Tumor Profiler Consortium, Aebersold Rudolf , Al-Quaddoomi Faisal S , Albinus Jonas , Alborelli Ilaria , Andani Sonali , Attinger Per-Olof , Bacac Marina , Baumhoer Daniel , Beck-Schimmer Beatrice , Beerenwinkel Niko , Beisel Christian , Bernasconi Lara , Bertolini Anne , Bodenmiller Bernd , Bonilla Ximena , Casanova Ruben , Chevrier Stéphane , Chicherova Natalia , D'Costa Maya , Danenberg Esther , Davidson Natalie , gan Monica-Andreea Dră , Dummer Reinhard , Engler Stefanie , Erkens Martin , Eschbach Katja , Esposito Cinzia , Fedier André , Ferreira Pedro , Ficek Joanna , Frei Anja L , Frey Bruno , Goetze Sandra , Grob Linda , Gut Gabriele , Günther Detlef , Haberecker Martina , Haeuptle Pirmin , Heinzelmann-Schwarz Viola , Herter Sylvia , Holtackers Rene , Huesser Tamara , Irmisch Anja , Jacob Francis , Jacobs Andrea , Jaeger Tim M , Jahn Katharina , James Alva R , Jermann Philip M , Kahles André , Kahraman Abdullah , Koelzer Viktor H , Kuebler Werner , Kuipers Jack , Kunze Christian P , Kurzeder Christian , Lehmann Kjong-Van , Levesque Mitchell , Lugert Sebastian , Maass Gerd , Manz Markus , Markolin Philipp , Mena Julien , Menzel Ulrike , Metzler Julian M , Miglino Nicola , Milani Emanuela S , Moch Holger , Muenst Simone , Murri Riccardo , Ng Charlotte KY , Nicolet Stefan , Nowak Marta , Pedrioli Patrick GA , Pelkmans Lucas , Piscuoglio Salvatore , Prummer Michael , Ritter Mathilde , Rommel Christian , Rosano-González María L , Rätsch Gunnar , Santacroce Natascha , Castillo Jacobo Sarabia del , Schlenker Ramona , Schwalie Petra C , Schwan Severin , Schär Tobias , Senti Gabriela , Singer Franziska , Sivapatham Sujana , Snijder Berend , Sobottka Bettina , Sreedharan Vipin T , Stark Stefan , Stekhoven Daniel J , Theocharides Alexandre PA , Thomas Tinu M , Tolnay Markus , Tosevski Vinko , Toussaint Nora C , Tuncel Mustafa A , Tusup Marina , Drogen Audrey Van , Vetter Marcus , Vlajnic Tatjana , Weber Sandra , Weber Walter P , Wegmann Rebekka , Weller Michael , Wendt Fabian , Wey Norbert , Wicki Andreas , Wollscheid Bernd , Yu Shuqing , Ziegler Johanna , Zimmermann Marc , Zoche Martin , Zuend Gregor , Rätsch Gunnar , Lehmann Kjong-Van 
Published: September 2020

Abstract

Motivation
Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed.

Results
We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively.

Availability and implementation
https://github.com/ratschlab/scim.

Supplementary information
Supplementary data are available at Bioinformatics online.