Assessing 2D visual encoding of 3D spatial connectivity

TL;DR

This study compares different visual layouts for showing spatial connectivity data and finds that circular layouts work best for small datasets.

Contribution

The paper introduces a crowdsourcing approach to evaluate visual layouts for spatial connectivity data in bioinformatics.

Findings

The circular layout was most accurate and intuitive for Mechanical Turk participants.

Experts found circular and half-matrix layouts more accurate than the matrix layout.

Crowdsourcing can help identify effective visual layouts for bioinformatics data challenges.

Abstract

Introduction: When visualizing complex data, the layout method chosen can greatly affect the ability to identify outliers, spot incorrect modeling assumptions, or recognize unexpected patterns. Additionally, visual layout can play a crucial role in communicating results to peers.

Methods: In this paper, we compared the effectiveness of three visual layouts—the adjacency matrix, a half-matrix layout, and a circular layout—for visualizing spatial connectivity data, e.g., contacts derived from chromatin conformation capture experiments. To assess these visual layouts, we conducted a study comprising 150 participants from Amazon’s Mechanical Turk, as well as a second expert study comprising 30 biomedical research scientists.

Results: The Mechanical Turk study found that the circular layout was the most accurate and intuitive, while the expert study found that the circular and half-matrix layouts were more accurate than the matrix layout.

Discussion: We concluded that the circular layout may be a good default choice for visualizing smaller datasets with relatively few spatial contacts, while, for larger datasets, the half- matrix layout may be a better choice. Our results also demonstrated how crowdsourcing methods could be used to determine which visual layouts are best for addressing specific data challenges in bioinformatics.

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