Quantitatively Visualizing Bipartite Datasets

doi:10.1103/PhysRevX.13.021002

Quantitatively Visualizing Bipartite Datasets

2023-04-04

0

OA

AI

DOI：10.1103/PhysRevX.13.021002

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

DISTANCE GEOMETRY

INFLUENZA

LOCALIZATION

ANTIBODIES

Quantitatively Visualizing Bipartite Datasets

Abstract

Journal

Organization