Cargando…

Tensor‐structured decomposition improves systems serology analysis

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 antige...

Descripción completa

Detalles Bibliográficos
Autores principales: Tan, Zhixin Cyrillus, Murphy, Madeleine C, Alpay, Hakan S, Taylor, Scott D, Meyer, Aaron S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8420856/
https://www.ncbi.nlm.nih.gov/pubmed/34487431
http://dx.doi.org/10.15252/msb.202110243
_version_ 1783748964288299008
author Tan, Zhixin Cyrillus
Murphy, Madeleine C
Alpay, Hakan S
Taylor, Scott D
Meyer, Aaron S
author_facet Tan, Zhixin Cyrillus
Murphy, Madeleine C
Alpay, Hakan S
Taylor, Scott D
Meyer, Aaron S
author_sort Tan, Zhixin Cyrillus
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8420856
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-84208562021-09-13 Tensor‐structured decomposition improves systems serology analysis Tan, Zhixin Cyrillus Murphy, Madeleine C Alpay, Hakan S Taylor, Scott D Meyer, Aaron S Mol Syst Biol Articles 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. John Wiley and Sons Inc. 2021-09-06 /pmc/articles/PMC8420856/ /pubmed/34487431 http://dx.doi.org/10.15252/msb.202110243 Text en © 2021 The Authors. Published under the terms of the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Articles
Tan, Zhixin Cyrillus
Murphy, Madeleine C
Alpay, Hakan S
Taylor, Scott D
Meyer, Aaron S
Tensor‐structured decomposition improves systems serology analysis
title Tensor‐structured decomposition improves systems serology analysis
title_full Tensor‐structured decomposition improves systems serology analysis
title_fullStr Tensor‐structured decomposition improves systems serology analysis
title_full_unstemmed Tensor‐structured decomposition improves systems serology analysis
title_short Tensor‐structured decomposition improves systems serology analysis
title_sort tensor‐structured decomposition improves systems serology analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8420856/
https://www.ncbi.nlm.nih.gov/pubmed/34487431
http://dx.doi.org/10.15252/msb.202110243
work_keys_str_mv AT tanzhixincyrillus tensorstructureddecompositionimprovessystemsserologyanalysis
AT murphymadeleinec tensorstructureddecompositionimprovessystemsserologyanalysis
AT alpayhakans tensorstructureddecompositionimprovessystemsserologyanalysis
AT taylorscottd tensorstructureddecompositionimprovessystemsserologyanalysis
AT meyeraarons tensorstructureddecompositionimprovessystemsserologyanalysis