Abstract
Systems serology provides a broad view of humoral immunity by profiling both the antigen‐binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease‐relevant antigen targets, alongside additional measurements made for single antigens or in an antigen‐generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix–tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV‐ and SARS‐CoV‐2‐infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen‐binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology.
Highlights
Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies
To integrate the antigen-specific array and gp120-exclusive glycan measurements, we used a form of tensor-based dimensionality reduction, coupled matrix–tensor factorization (CMTF; Fig 1B and C)
By concatenating both the unfolded tensor and matrix during the alternating least squares (ALS) solving for the subject dimension, we achieve the optimal low-rank approximation for both datasets (Fig 1D, see Materials and Methods). This structure is like canonical polyadic (CP) decomposition on a single tensor, or principal components analysis (PCA) on a single matrix (Fig 1D)
Summary
Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease-relevant antigen targets, alongside additional measurements made for single antigens or in an antigen-generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen-binding effects, and recognition of consistent patterns across individual measurements. We propose that CMTF is an effective general strategy for data exploration in systems serology
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