simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods.

Autor: Kanduri C; Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway.; UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway., Scheffer L; Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway., Pavlović M; Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway.; UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway., Rand KD; Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway., Chernigovskaya M; Department of Immunology and Oslo University Hospital, University of Oslo, 0373 Oslo, Norway., Pirvandy O; Faculty of Engineering, Bar-Ilan University, 5290002, Israel., Yaari G; Faculty of Engineering, Bar-Ilan University, 5290002, Israel., Greiff V; Department of Immunology and Oslo University Hospital, University of Oslo, 0373 Oslo, Norway., Sandve GK; Centre for Bioinformatics, Department of Informatics, University of Oslo, 0373 Oslo, Norway.; UiORealArt Convergence Environment, University of Oslo, 0373 Oslo, Norway.
Jazyk: angličtina
Zdroj: GigaScience [Gigascience] 2022 Dec 28; Vol. 12. Date of Electronic Publication: 2023 Oct 17.
DOI: 10.1093/gigascience/giad074
Abstrakt: Background: Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires.
Results: We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets.
Conclusions: This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.
(© The Author(s) 2023. Published by Oxford University Press GigaScience.)
Databáze: MEDLINE
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