Various computational approaches predict chromatin structure, yielding concrete models that position genomic loci in physical space and help reveal genome organization and function. While prior visualization research has explored data and task abstractions for genomics, the design space for depicting these three-dimensional (3D) genome models-and associated genome-mapped data-remains unclear. In this paper, we investigate the visualization of genomic data with a spatial component. First, we systematically survey how 3D genome models are used and depicted in computational biology. We analyze over 300 papers with figures that visualize 3D genomic data and categorize the methods for visual representation. From this survey, we derive a design space for visualizing 3D genome data, identifying common patterns and key properties such as representation, visual channels, and composition. We position these findings within an existing genomics visualization taxonomy, refining and extending existing classifications. Second, we augment Gosling, a declarative visualization grammar for genomics, to support 3D genomic data. Our integration enables expressive authoring of visualizations that connect traditional genome-mapped information with 3D genome models, emphasizing their spatial characteristics. To demonstrate its utility, we employ our extended grammar to recreate interactive examples, showcasing its ability to represent complex visual designs. Comprehensive examples and an interactive editor are available at 3d.gosling-lang.org.

Projecting high-dimensional vectors into two dimensions for visualization, known as embedding visualization, facilitates perceptual reasoning and interpretation. Comparing multiple embedding visualizations drives decision-making in many domains, but traditional comparison methods are limited by a reliance on direct point correspondences. This requirement precludes comparisons without point correspondences, such as two different datasets of annotated images, and fails to capture meaningful higher-level relationships among point groups. To address these shortcomings, we propose a general framework for comparing embedding visualizations based on shared class labels rather than individual points. Our approach partitions points into regions corresponding to three key class concepts-confusion, neighborhood, and relative size-to characterize intra- and inter-class relationships. Informed by a preliminary user study, we implemented our framework using perceptual neighborhood graphs to define these regions and introduced metrics to quantify each concept. We demonstrate the generality of our framework with usage scenarios from machine learning and single-cell biology, highlighting our metrics' ability to draw insightful comparisons across label hierarchies. To assess the effectiveness of our approach, we conducted an evaluation study with five machine learning researchers and six single-cell biologists using an interactive and scalable prototype built with Python, JavaScript, and Rust. Our metrics enable more structured comparisons through visual guidance and increased participants' confidence in their findings.

Coordinated multiple views (CMV) in a visual analytics system can help users explore multiple data representations simultaneously with linked interactions. However, the implementation of coordinated multiple views can be challenging. Without standard software libraries, visualization designers need to re-implement CMV during the development of each system. We introduce use-coordination, a grammar and software library that supports the efficient implementation of CMV. The grammar defines a JSON-based representation for an abstract coordination model from the information visualization literature. We contribute an optional extension to the model and grammar that allows for hierarchical coordination. Through three use cases, we show that use-coordinationenables implementation of CMV in systems containing not only basic statistical charts but also more complex visualizations such as medical imaging volumes. We describe six software extensions, including a graphical editor for manipulation of coordination, which showcase the potential to build upon our coordination-focused declarative approach. The software is open-source and available at https://use-coordination.dev.

SUMMARY: Gos is a declarative Python library designed to create interactive multiscale visualizations of genomics and epigenomics data. It provides a consistent and simple interface to the flexible Gosling visualization grammar. Gos hides technical complexities involved with configuring web-based genome browsers and integrates seamlessly within computational notebooks environments to enable new interactive analysis workflows. AVAILABILITY AND IMPLEMENTATION: Gos is released under the MIT License and available on the Python Package Index (PyPI). The source code is publicly available on GitHub (https://github.com/gosling-lang/gos), and documentation with examples can be found at https://gosling-lang.github.io/gos.

Survival outcome prediction is a challenging weakly-supervised and ordinal regression task in computational pathology that involves modeling complex interactions within the tumor microenvironment in gigapixel whole slide images (WSIs). Despite recent progress in formulating WSIs as bags for multiple instance learning (MIL), representation learning of entire WSIs remains an open and challenging problem, especially in overcoming: 1) the computational complexity of feature aggregation in large bags, and 2) the data heterogeneity gap in incorporating biological priors such as genomic measurements. In this work, we present a Multimodal Co-Attention Transformer (MCAT) framework that learns an interpretable, dense co-attention mapping between WSIs and genomic features formulated in an embedding space. Inspired by approaches in Visual Question Answering (VQA) that can attribute how word embed-dings attend to salient objects in an image when answering a question, MCAT learns how histology patches attend to genes when predicting patient survival. In addition to visualizing multimodal interactions, our co-attention trans-formation also reduces the space complexity of WSI bags, which enables the adaptation of Transformer layers as a general encoder backbone in MIL. We apply our proposed method on five different cancer datasets (4,730 WSIs, 67 million patches). Our experimental results demonstrate that the proposed method consistently achieves superior performance compared to the state-of-the-art methods.