Cell-cell Communication Inference from Single-cell RNA-Seq Data: a Comparison of Methods and Resources

Research Square

Abstract

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/liana/tree/biorxiv-clean, 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.

Authors 11

1. Daniel Dimitrov

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant

2. Dénes Türei

   European Molecular Biology Laboratory · European Molecular Biology Laboratory

   Affiliation as printed

   European Molecular Biology Laboratory (EMBL), Structural and Computational Biology Unit, Meyerhofstrasse 1, D-69117 Heidelberg

3. Charlotte Boys

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant

4. James Shiniti Nagai Aachen Institute for Computational Genomics Faculty of Medicine

   RWTH Aachen University

   Affiliation as printed

   Institute for Computational Genomics, Faculty of Medicine, RWTH Aachen University

5. Ricardo Ramirez Flores

   Heidelberg University

   Affiliation as printed

   Heidelberg University

6. Hyojin Kim

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant

7. Bence Szalai

   Semmelweis University

   Affiliation as printed

   Faculty of Medicine, Department of Physiology, Semmelweis University

8. Ivan G. Costa Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

9. Aurélien J. F. Dugourd

   Heidelberg University

   Affiliation as printed

   Heidelberg University

10. Alberto Valdeolivas

    Roche (Switzerland)

    Affiliation as printed

    Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel

11. Julio Sáez-Rodríguez

    Heidelberg University

    Affiliation as printed

    Heidelberg University