HiPiler: Visual Exploration of Large Genome Interaction Matrices with Interactive Small Multiples

Abstract

(1) with an overview (1A) and detail (1B) matrix. The snippet view (2) presents regions of the matrix as interactive small multiples. In this example, snippets are arranged with t-SNE (2C) and a well-pronounced pile of snippets is highlighted (2A). View menus for operation are located at the bottom (1C and 2B).Abstract-This paper presents an interactive visualization interface-HiPiler-for the exploration and visualization of regions-of-interest in large genome interaction matrices. Genome interaction matrices approximate the physical distance of pairs of regions on the genome to each other and can contain up to 3 million rows and columns with many sparse regions. Regions of interest (ROIs) can be defined, e.g., by sets of adjacent rows and columns, or by specific visual patterns in the matrix. However, traditional matrix aggregation or pan-and-zoom interfaces fail in supporting search, inspection, and comparison of ROIs in such large matrices. In HiPiler, ROIs are first-class objects, represented as thumbnail-like "snippets". Snippets can be interactively explored and grouped or laid out automatically in scatterplots, or through dimension reduction methods. Snippets are linked to the entire navigable genome interaction matrix through brushing and linking. The design of HiPiler is based on a series of semi-structured interviews with 10 domain experts involved in the analysis and interpretation of genome interaction matrices. We describe six exploration tasks that are crucial for analysis of interaction matrices and demonstrate how HiPiler supports these tasks. We report on a user study with a series of data exploration sessions with domain experts to assess the usability of HiPiler as well as to demonstrate respective findings in the data.

Pattern-Driven Navigation in 2D Multiscale Visualizations with Scalable Insets

“…Hereby, separate panels work well as the user has already seen each location in detail before it appears as a thumbnail, but it is not clear how effective such an approach would be for guidance to new locations. HiPiler [41] supports exploration and comparisons of many patterns through interactive small multiples in a separated view. While this approach works well for out-of-context comparison, it has not been designed for guided navigation within the viewport of a multiscale visualization.…”

“…We designed two approaches to visually represent clusters. When exploring matrices, clusters are aggregated into piles and feature a 2D cover matrix together with 1D previews [1,41]. The cover matrix represents a statistical summary of the patterns on the cluster, e.g., the arithmetic mean or variance.…”

“…These heatmaps contain various types of visual patterns that act as proxies for cellular properties and functions and are annotated algorithmically. The frequency of these patterns ranges from a few hundred to hundreds of thousands per matrix, and analysts are interested in finding, comparing, and contextualizing patterns across many zoom levels (more details are described elsewhere [41]).…”

Communicating Genome Architecture: Biovisualization of the Genome, from Data Analysis and Hypothesis Generation to Communication and Learning

“…5d) [100] and by computer-assisted clustering (Fig. 5d) [101,102]. Pattern finding within 3D models by side-by-side or overlaid comparison is a common idiom of standard molecular viewers but the expansive spatial datasets of the genome can overwhelm visual memory and their dispersed arrangement can induce change blindness [103].…”

Coolpup.py:versatile pile-up analysis of Hi-C data

“…Since its inception in the current form (Rao et al, 2014), originally termed APA ("Aggregate Peak Analysis"), pile-up analysis has been used in numerous publications, both to analyse single-cell Hi-C data (Flyamer et al, 2017;Gassler et al, 2017;Nagano et al, 2017) and as a general way of quantifying feature strength (Fudenberg et al, 2016;Schwarzer et al, 2017;Bonev et al, 2017;Nora et al, 2017;McLaughlin et al, 2019;Rao et al, 2017;Díaz et al, 2018;Kruse et al, 2019;Abdennur et al, 2018;Rowley et al, 2019;Krietenstein et al, 2019;Hsieh et al, 2019). A visual interactive tool to semi-manually classify and pile-up predefined regions has also been developed (Lekschas et al, 2018). However no single computational tool can perform all the various kinds of pile-up analyses that have been used in the literature, including local and rescaled (features of different size or shape are averaged, e.g.…”