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Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data

View ORCID ProfileDaniel Dimitrov, View ORCID ProfileDénes Türei, View ORCID ProfileCharlotte Boys, View ORCID ProfileJames S. Nagai, View ORCID ProfileRicardo O. Ramirez Flores, View ORCID ProfileHyojin Kim, View ORCID ProfileBence Szalai, View ORCID ProfileIvan G. Costa, View ORCID ProfileAurélien Dugourd, View ORCID ProfileAlberto Valdeolivas, View ORCID ProfileJulio Saez-Rodriguez
doi: https://doi.org/10.1101/2021.05.21.445160
Daniel Dimitrov
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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Dénes Türei
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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Charlotte Boys
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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James S. Nagai
3Institute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Aachen 52074 Germany
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Ricardo O. Ramirez Flores
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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Hyojin Kim
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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Bence Szalai
2Faculty of Medicine, Department of Physiology, Semmelweis University, Budapest, Hungary
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Ivan G. Costa
3Institute for Computational Genomics, Faculty of Medicine, RWTH Aachen University, Aachen 52074 Germany
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  • ORCID record for Ivan G. Costa
Aurélien Dugourd
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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Alberto Valdeolivas
4Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland
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Julio Saez-Rodriguez
1Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
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  • For correspondence: julio.saez@uni-heidelberg.de
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Abstract

The growing availability of single-cell data has sparked an increased interest in the inference of cell-cell communication from this data. Many tools have been developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we created a framework, available at https://github.com/saezlab/ligrec_decoupler, to facilitate a comparative assessment of methods for inferring cell-cell communication from single cell transcriptomics data and then compared 15 resources and 6 methods. We found few unique interactions and a varying degree of overlap among the resources, and observed uneven coverage in terms of pathways and biological categories. We analysed a colorectal cancer single cell RNA-Seq dataset using all possible combinations of methods and resources. We found major differences among the highest ranked intercellular interactions inferred by each method even when using the same resources. The varying predictions lead to fundamentally different biological interpretations, highlighting the need to benchmark resources and methods.

Findings

  • Built a framework to systematically combine 15 resources and 6 methods to estimate cell-cell communication from single-cell RNA data

  • Cell-cell communication resources are often built from the same original databases and very few interactions are unique to a single resource. Yet overlap varies among resources and certain biological terms are unevenly represented

  • Different methods and resources provided notably different results

  • The observed disagreement among the methods could have a considerable impact on the interpretation of results

Competing Interest Statement

JSR has received funding from GSK and Sanofi and consultant fees from Travere Therapeutics. AV is currently employed by F. Hoffmann-La Roche Ltd. The authors declare that they have no other competing interests.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted May 23, 2021.
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Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data
Daniel Dimitrov, Dénes Türei, Charlotte Boys, James S. Nagai, Ricardo O. Ramirez Flores, Hyojin Kim, Bence Szalai, Ivan G. Costa, Aurélien Dugourd, Alberto Valdeolivas, Julio Saez-Rodriguez
bioRxiv 2021.05.21.445160; doi: https://doi.org/10.1101/2021.05.21.445160
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Comparison of Resources and Methods to infer Cell-Cell Communication from Single-cell RNA Data
Daniel Dimitrov, Dénes Türei, Charlotte Boys, James S. Nagai, Ricardo O. Ramirez Flores, Hyojin Kim, Bence Szalai, Ivan G. Costa, Aurélien Dugourd, Alberto Valdeolivas, Julio Saez-Rodriguez
bioRxiv 2021.05.21.445160; doi: https://doi.org/10.1101/2021.05.21.445160

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