Quantitatively visualizing bipartite datasets

TL;DR

This paper addresses the challenge of visualizing bipartite datasets by adapting localization algorithms to reveal the underlying structure, demonstrated through antibody-virus interaction data.

Contribution

It introduces modified bipartite localization algorithms and analyzes their robustness, providing a new method for visualizing complex bipartite data structures.

Findings

Algorithms successfully visualize bipartite data structure

Analysis of noise and outlier effects on localization methods

Application to antibody-virus interaction data reveals biological insights

Abstract

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 only conveys 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 only available 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.