Scalable Insets

You are here

Feature-Centric Exploration of Hi-C Maps with Scalable Insets

Peter Kerpedjiev
Michael Behrisch

Scalable Insets is a new technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visual spaces such as genome interaction maps from Hi-C experiments. Our technique visualizes annotated features, such as loops or TADs, too small to be identifiable at certain zoom levels using insets, i.e., magnified thumbnail views of the features. Insets are dynamically placed either within the viewport or along the boundary of the viewport to offer a compromise between locality and context preservation. Annotated features are interactively clustered by location and type. They are visually represented as an aggregated inset to provide scalable exploration within a single viewport. Finds out more in the project page and our 5-mins introductory video.

Release Date:
May, 2018
Data type:
2D, Static mapping, Network / hierarchy graph, Spatial representation
Insets, Detail-in-Context
Package or library, Web based
JavaScript, Python, Canvas, HTML, CSS
Linux, Mac OSX, Windows
HiGlass, Modern webbrowser

Project development

Institution: Harvard University

We implemented Scalable Insets as an extension to HiGlass, a flexible web application for viewing large tile-based genomic datasets. Besides genome interaction maps, our implementation currently supports gigapixel images and geographic maps too. The tool can easily be applied to existing BEDPE annotation files. The source code is available on GitHub