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SCIM: universal single-cell matching with unpaired feature sets

Authors :
Stark, Stefan G
Ficek, Joanna
Locatello, Francesco
Bonilla, Ximena
Chevrier, Stéphane
Singer, Franziska
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
Casanova, Ruben
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
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
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
University of Zurich
Source :
Bioinformatics, Bioinformatics, 36 (S2)
Publication Year :
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.<br />Bioinformatics, 36 (S2)<br />ISSN:1367-4803<br />ISSN:1460-2059

Details

Language :
English
ISSN :
13674803 and 14602059
Database :
OpenAIRE
Journal :
Bioinformatics, Bioinformatics, 36 (S2)
Accession number :
edsair.doi.dedup.....4925abb7a5d25d3c8452a01e981de017