Quantitatively Visualizing Bipartite Datasets
As experiments continue to increase in size and scope, a fundamental challenge of subsequent analyses is to recast the wealth of information into an intuitive and readily interpretable form. Often, each measurement conveys only the relationship between a pair of entries, and it is difficult to integ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2023-04-01
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| Series: | Physical Review X |
| Online Access: | http://doi.org/10.1103/PhysRevX.13.021002 |
| _version_ | 1797852511367856128 |
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| author | Tal Einav Yuehaw Khoo Amit Singer |
| author_facet | Tal Einav Yuehaw Khoo Amit Singer |
| author_sort | Tal Einav |
| collection | DOAJ |
| description | As experiments continue to increase in size and scope, a fundamental challenge of subsequent analyses is to recast the wealth of information into an intuitive and readily interpretable form. Often, each measurement conveys only the relationship between a pair of entries, and it is difficult to integrate these local interactions across a dataset to form a cohesive global picture. The classic localization problem tackles this question, transforming local measurements into a global map that reveals the underlying structure of a system. Here, we examine the more challenging bipartite localization problem, where pairwise distances are available only for bipartite data comprising two classes of entries (such as antibody-virus interactions, drug-cell potency, or user-rating profiles). We modify previous algorithms to solve bipartite localization and examine how each method behaves in the presence of noise, outliers, and partially observed data. As a proof of concept, we apply these algorithms to antibody-virus neutralization measurements to create a basis set of antibody behaviors, formalize how potently inhibiting some viruses necessitates weakly inhibiting other viruses, and quantify how often combinations of antibodies exhibit degenerate behavior. |
| first_indexed | 2024-04-09T19:34:26Z |
| format | Article |
| id | doaj.art-629dde2a660d48588dc2a7085b93a33a |
| institution | Directory Open Access Journal |
| issn | 2160-3308 |
| language | English |
| last_indexed | 2024-04-09T19:34:26Z |
| publishDate | 2023-04-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review X |
| spelling | doaj.art-629dde2a660d48588dc2a7085b93a33a2023-04-04T14:06:26ZengAmerican Physical SocietyPhysical Review X2160-33082023-04-0113202100210.1103/PhysRevX.13.021002Quantitatively Visualizing Bipartite DatasetsTal EinavYuehaw KhooAmit SingerAs experiments continue to increase in size and scope, a fundamental challenge of subsequent analyses is to recast the wealth of information into an intuitive and readily interpretable form. Often, each measurement conveys only the relationship between a pair of entries, and it is difficult to integrate these local interactions across a dataset to form a cohesive global picture. The classic localization problem tackles this question, transforming local measurements into a global map that reveals the underlying structure of a system. Here, we examine the more challenging bipartite localization problem, where pairwise distances are available only for bipartite data comprising two classes of entries (such as antibody-virus interactions, drug-cell potency, or user-rating profiles). We modify previous algorithms to solve bipartite localization and examine how each method behaves in the presence of noise, outliers, and partially observed data. As a proof of concept, we apply these algorithms to antibody-virus neutralization measurements to create a basis set of antibody behaviors, formalize how potently inhibiting some viruses necessitates weakly inhibiting other viruses, and quantify how often combinations of antibodies exhibit degenerate behavior.http://doi.org/10.1103/PhysRevX.13.021002 |
| spellingShingle | Tal Einav Yuehaw Khoo Amit Singer Quantitatively Visualizing Bipartite Datasets Physical Review X |
| title | Quantitatively Visualizing Bipartite Datasets |
| title_full | Quantitatively Visualizing Bipartite Datasets |
| title_fullStr | Quantitatively Visualizing Bipartite Datasets |
| title_full_unstemmed | Quantitatively Visualizing Bipartite Datasets |
| title_short | Quantitatively Visualizing Bipartite Datasets |
| title_sort | quantitatively visualizing bipartite datasets |
| url | http://doi.org/10.1103/PhysRevX.13.021002 |
| work_keys_str_mv | AT taleinav quantitativelyvisualizingbipartitedatasets AT yuehawkhoo quantitativelyvisualizingbipartitedatasets AT amitsinger quantitativelyvisualizingbipartitedatasets |