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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

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Tensor-structured decomposition improves systems serology analysis.

Zhixin Cyrillus Tan¹, Madeleine C Murphy², Hakan S Alpay³

¹Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.

Molecular Systems Biology

|September 6, 2021

Summary

This summary is machine-generated.

Coupled matrix-tensor factorization (CMTF) effectively reduces complex systems serology data, revealing consistent patterns in antibody function. This method enhances understanding of humoral immunity and aids vaccine development.

Keywords:

HIV SARS-CoV-2 effector function systems serology tensor decomposition

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Area of Science:

Immunology
Computational Biology
Data Science

Background:

Systems serology offers a comprehensive view of humoral immunity by analyzing antibody antigen-binding and Fc properties.
Understanding these properties is crucial for developing vaccines, therapeutics, and deciphering disease mechanisms.

Purpose of the Study:

To introduce coupled matrix-tensor factorization (CMTF) as a novel method for analyzing complex systems serology data.
To demonstrate CMTF's ability to identify consistent patterns and reduce data dimensionality.

Main Methods:

Applied coupled matrix-tensor factorization (CMTF) to systems serology datasets from HIV and SARS-CoV-2 studies.
Compared CMTF performance against standard methods like principal components analysis (PCA).

Main Results:

CMTF achieved greater data reduction than PCA while maintaining predictive accuracy for immune responses and disease status.
CMTF improved model interpretability by separating Fc and antigen-binding effects and identifying cross-measurement patterns.

Conclusions:

CMTF is a powerful and generalizable strategy for exploring and interpreting systems serology data.
This approach enhances the replicability and understanding of humoral immunity studies.