Open AccessSCIM: universal single-cell matching with unpaired feature sets
Author(s) -
Stefan G. Stark,
Joanna Ficek,
Francesco Locatello,
Ximena Bonilla,
Stéphane Chevrier,
Franziska Singer,
Rudolf Aebersold,
Faisal Alquaddoomi,
Jonas Albinus,
Ilaria Alborelli,
Sonali Andani,
Per-Olof Attinger,
Marina Bacac,
Daniel Baumhoer,
Beatrice BeckSchimmer,
Niko Beerenwinkel,
Christian Beisel,
Lara Bernasconi,
Anne Bertolini,
Bernd Bodenmiller,
Ruben Casanova,
Natalia Chicherova,
Maya D’Costa,
Esther Danenberg,
Natalie R. Davidson,
Monica-Andreea Dră gan,
Reinhard Dummer,
Stefanie Engler,
Martin Erkens,
Katja Eschbach,
Cinzia Esposito,
André Fedier,
Pedro Ferreira,
Anja Frei,
Bruno S. Frey,
Sandra Goetze,
Linda Grob,
Gabriele Gut,
Detlef Günther,
Martina Haberecker,
Pirmin Haeuptle,
Viola HeinzelmannSchwarz,
Sylvia Herter,
René Holtackers,
Tamara Huesser,
Anja Irmisch,
Francis Jacob,
Alice K. Jacobs,
Tim M. Jaeger,
Katharina Jahn,
Alva Rani James,
Philip Jermann,
André Kahles,
Abdullah Kahraman,
Viktor H. Koelzer,
Werner Kuebler,
Jack Kuipers,
Christian P. Kunze,
Christian Kurzeder,
Kjong-Van Lehmann,
Mitchell Levesque,
Sebastian Lugert,
Gerd Maass,
Markus G. Manz,
Philipp Markolin,
Julien Mena,
Ulrike Menzel,
Julian M. Metzler,
Nicola Miglino,
Emanuela S. Milani,
Holger Moch,
Simone Muenst,
Riccardo Murri,
Charlotte K.Y. Ng,
Stefan Nicolet,
Marta Nowak,
Patrick G. A. Pedrioli,
Lucas Pelkmans,
Salvatore Piscuoglio,
Michael Prummer,
Mathilde Ritter,
Christian Rommel,
María L. Rosano-González,
Gunnar Rätsch,
Natascha Santacroce,
Jacobo Sarabia del Castillo,
Ramona Schlenker,
Petra Schwalie,
Severin Schwan,
Tobias Schär,
Gabriela Senti,
Sujana Sivapatham,
Berend Snijder,
Bettina Sobottka,
Vipin T. Sreedharan,
Daniel J. Stekhoven,
Alexandre Theocharides,
Tinu M. Thomas,
Markus Tolnay,
Vinko Toševski,
Nora C. Toussaint,
Anıl Tuncel,
Marina Tusup,
Audrey van Drogen,
Marcus Vetter,
Tatjana Vlajnic,
Sandra Weber,
William P. Weber,
Rebekka Wegmann,
Michael Weller,
Fabian Wendt,
Norbert Wey,
Andreas Wicki,
Bernd Wollscheid,
Shuqing Yu,
Johanna Ziegler,
Marc Zimmermann,
Martin Zoche,
Gregor Zuend
Publication year - 2020
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btaa843
Subject(s) - computer science , pairwise comparison , scalability , autoencoder , matching (statistics) , data mining , pattern recognition (psychology) , artificial intelligence , mathematics , deep learning , statistics , database
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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom