A Novel Approach to T-Cell Receptor Beta Chain (TCRB) Repertoire Encoding Using Lossless String Compression

Konstantinovsky, Thomas; Yaari, Gur

doi:10.1101/2023.01.30.526195

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…Further work is needed on the biological understanding of immune signals for improved simulation (Kanduri et al 2022; Widrich et al 2020). Moreover, current speed bottleneck computations, such as those of generation probability, may be alleviated by novel encoding algorithms (Konstantinovsky and Yaari 2023)), thus improving overall simulation speed.…”

Section: Discussionmentioning

confidence: 99%

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning

Chernigovskaya,

Pavlović,

Kanduri

et al. 2023

Preprint

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Machine-learning methods (ML) have shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML approaches where experimental data is inaccessible or insufficient as of yet. The challenge for simulated data to be useful is the ability to incorporate key features observed in experimental repertoires. These features, such as complex antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a modular software suite, which simulates AIRR data for the development and benchmarking of AIRR-based machine learning. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO simulation supports the development and validation of AIRR-ML methods: (1) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (2) how immune information co-occurring in the same AIRs have an impact on the performance of conventional receptor-level encoding and repertoire-level classification approaches. The LIgO software guides the advancement and assessment of interpretable AIRR-ML methods.

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Section: Discussionmentioning

confidence: 99%