Repertoire-based diagnostics using statistical biophysics
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Abstract A fundamental challenge in immunology is diagnostic classification based on repertoire sequence. We used the principle of maximum entropy (MaxEnt) to build compact representations of antibody (IgH) and T-cell receptor (TCRβ) CDR3 repertoires based on the statistical biophysical patterns latent in the frequency and ordering of repertoires’ constituent amino acids. This approach results in substantial advantages in quality, dimensionality, and training speed compared to MaxEnt models based solely on the standard 20-letter amino-acid alphabet. Descriptor-based models learn patterns that pure amino-acid-based models cannot. We demonstrate the utility of descriptor models by successfully classifying influenza vaccination status (AUC=0.97, p=4×10 -3 ), requiring only 31 samples from 14 individuals. Descriptor-based MaxEnt modeling is a powerful new method for dissecting, encoding, and classifying complex repertoires.
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Statistical classifiers can identify immune profiles of patients with chronic lymphocytic leukemia, multiple sclerosis or influenza from NGS data alone.
“It was demonstrated that statistical classifiers can identify immune profiles of patients with chronic lymphocytic leukemia [220], multiple sclerosis [221] or influenza [222] from NGS data alone.”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [222]
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“It was demonstrated that statistical classifiers can identify immune profiles of patients with chronic lymphocytic leukemia [220], multiple sclerosis [221] or influenza [222] from NGS data alone.”
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- Authors
- Arora, R., Kaplinsky, J., Li, A., & Arnaout, R.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Technical University of Denmark (DK)
- Corresponding author
- Arnaout, Ramy
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2019-01-13
- OpenAlex
- W2908668466
- Cited by
- 10
- References
- 37
- Citations by year
- 2022: 1 · 2021: 3 · 2020: 3 · 2019: 2 · 2018: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- Funders
- National Science Foundation (ACI-1 548 562, 1548562)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Physical Sciences › Engineering › Biomedical Engineering › Artificial Immune Systems Applications
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- immune repertoire, CDR3 sequences, antibody repertoire, T cell receptor repertoire, maximum entropy model, amino acid descriptors, influenza vaccination status, immunodiagnosis
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