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Structured decomposition improves systems serology prediction and interpretation

Madeleine Murphy, Scott D. Taylor, View ORCID ProfileAaron S. Meyer
doi: https://doi.org/10.1101/2021.01.03.425138
Madeleine Murphy
1Computational and Systems Biology, University of California, Los Angeles
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Scott D. Taylor
2Department of Bioengineering, University of California, Los Angeles
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Aaron S. Meyer
2Department of Bioengineering, University of California, Los Angeles
4Department of Bioinformatics, University of California, Los Angeles
5Jonsson Comprehensive Cancer Center, University of California, Los Angeles
6Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles
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  • ORCID record for Aaron S. Meyer
  • For correspondence: ameyer@ucla.edu
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Abstract

Systems serology measurements provide a comprehensive view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. Identifying patterns in these measurements will help to guide vaccine and therapeutic antibody development, and improve our understanding of disorders. Furthermore, consistent patterns across diseases may reflect conserved regulatory mechanisms; recognizing these may help to combine modalities such as vaccines, antibody-based interventions, and other immunotherapies to maximize protection. A common feature of systems serology studies is structured biophysical profiling across disease-relevant antigen targets, properties of antibodies’ interaction with the immune system, and serological samples. These are typically produced alongside additional measurements that are not antigen-specific. Here, we report a new form of tensor factorization, total tensor-matrix factorization (TMTF), which can greatly reduce these data into consistently observed patterns by recognizing the structure of these data. We use a previous study of HIV-infected subjects as an example. TMTF outperforms standard methods like principal components analysis in the extent of reduction possible. Data reduction, in turn, improves the prediction of immune functional responses, classification of subjects based on their HIV control status, and interpretation of these resulting models. Interpretability is improved specifically by applying further data reduction, separation of the Fc from antigen-binding effects, and recognizing consistent patterns across individual measurements. Therefore, we propose that TMTF will be an effective general strategy for exploring and using systems serology.

Summary points

  • Structured decomposition provides substantial data reduction without loss of information.

  • Predictions based on decomposed factors are accurate and robust to missing measurements.

  • Decomposition structure improves the interpretability of modeling results.

  • Decomposed factors represent meaningful patterns in the HIV humoral response.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • · Github murphymadeleine21, · Github scottdtaylor95 · Github aarmey · twitter aarmey

  • https://github.com/meyer-lab/systemsSerology

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-NC-ND 4.0 International license.
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Posted January 22, 2021.
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Structured decomposition improves systems serology prediction and interpretation
Madeleine Murphy, Scott D. Taylor, Aaron S. Meyer
bioRxiv 2021.01.03.425138; doi: https://doi.org/10.1101/2021.01.03.425138
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Structured decomposition improves systems serology prediction and interpretation
Madeleine Murphy, Scott D. Taylor, Aaron S. Meyer
bioRxiv 2021.01.03.425138; doi: https://doi.org/10.1101/2021.01.03.425138

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