Tensor-structured decomposition improves systems serology analysis.
Mol Syst Biol
; 17(9): e10243, 2021 09.
Article
in English
| MEDLINE | ID: covidwho-1395372
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
AIDS Serodiagnosis
/
HIV Infections
/
Data Interpretation, Statistical
/
COVID-19 Serological Testing
/
COVID-19
Type of study:
Diagnostic study
/
Prognostic study
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
Mol Syst Biol
Journal subject:
Molecular Biology
/
Biotechnology
Year:
2021
Document Type:
Article
Affiliation country:
Msb.202110243
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