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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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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 |
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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 |
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