arXiv subjects

Mark S. Keller

Publications and source records attributed to Mark S. Keller.

6 recordsLinked to original sources

arXiv2026

How Do We Visualize Space in Molecular Biology? A Study of Spatial Transcriptomics Visualization Practices

A cell's identity depends on where it sits in tissue: for example, a macrophage behaves differently in a tumor core than at its edge. Spatial transcriptomics has transformed how we study this by recovering that lost coordinate, but it does so by producing data that is simultaneously high-dimensional, multimodal, and uncertain. Visualizing this combination is a hard problem in its own right, and one that warrants an assessment of how the field currently represents it, what has worked, and what is still missing. We surveyed 148 papers and 1,824 figure panels using a What-Why-How coding framework grounded in Munzner's nested model, connecting the data represented, the biological tasks motivating each visualization, and the design choices through which they are expressed; a subset of the surveyed work also contributed dedicated interactive visualization software that was not necessarily reflected in the static figures, and we looked at what interaction capabilities those tools supported as well. We close by outlining where the field stands and the challenges ahead for bioinformatics and visualization researchers to tackle together.

cs.HC↗

arXiv2026

HuBMAP Data Portal: a resource for multimodal spatial and single-cell data of healthy human tissues

The NIH Human BioMolecular Atlas Program (HuBMAP) Data Portal (https://portal.hubmapconsortium.org/) serves as a comprehensive repository for multimodal, multi-scale spatial and single-cell data from healthy human tissues. As of August 2026, the portal hosts 9,316 public datasets from 26 data types spanning 29 organ classes across 501 donors. Portal infrastructure and user interfaces support data search and discovery, visualization, and analysis directly in web browsers. These capabilities include metadata- and data-driven search, collaborative Workspaces with access to high-performance compute, and interactive Vitessce visualizations across non-spatial, 2D, and 3D spatial datasets. Data-type-specific uniform processing pipelines and rigorous quality control processes ensure comparability of results across laboratories, organs, and donors, while externally processed community-contributed datasets provide complementary perspectives. Here we describe portal functionality, infrastructure, and design, and highlight its role as a platform for large-scale spatial single-cell research across diverse data types, organs, and scales.

q-bio.QM↗

arXiv2026

Pluot: Towards 'write once, run everywhere' visualization software

Tools used for implementing visualization software systems can generally be divided into camps such as static versus interactive and desktop versus web-based. We contribute Pluot, an architecture that bridges these divides, enabling a single software implementation of a visualization to be used regardless of the target level of interactivity or computing environment. With Pluot, a visualization developer implements a given visualization rendering function once, using the Rust programming language. Then, bindings to the Rust program can be generated to enable reproducible execution of the rendering function from other languages, such as Python or JavaScript. Pluot can render visualizations to bitmap or vector graphics format, bridging gaps between interactive performance and publication-quality figure creation. The software is available at https://pluot.dev.

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arXiv2025

Interactive visualization of kidney micro-compartmental segmentations and associated pathomics on whole slide images

Application of machine learning techniques enables segmentation of functional tissue units in histology whole-slide images (WSIs). We built a pipeline to apply previously validated segmentation models of kidney structures and extract quantitative features from these structures. Such quantitative analysis also requires qualitative inspection of results for quality control, exploration, and communication. We extend the Vitessce web-based visualization tool to enable visualization of segmentations of multiple types of functional tissue units, such as, glomeruli, tubules, arteries/arterioles in the kidney. Moreover, we propose a standard representation for files containing multiple segmentation bitmasks, which we define polymorphically, such that existing formats including OME-TIFF, OME-NGFF, AnnData, MuData, and SpatialData can be used. We demonstrate that these methods enable researchers and the broader public to interactively explore datasets containing multiple segmented entities and associated features, including for exploration of renal morphometry of biopsies from the Kidney Precision Medicine Project (KPMP) and the Human Biomolecular Atlas Program (HuBMAP).

q-bio.QM↗

arXiv2025

EasyVitessce: auto-magically adding interactivity to Scverse single-cell and spatial biology plots

EasyVitessce is a Python package that turns existing static Scanpy and SpatialData plots into interactive visualizations by virtue of adding a single line of Python code. The package uses Vitessce internally to render interactive plots, and abstracts away technical details involved with configuration of Vitessce. The resulting interactive plots can be viewed in computational notebook environments or their configurations can be exported for usage in other contexts such as web applications, enhancing the utility of popular Scverse Python plotting APIs. EasyVitessce is released under the MIT License and available on the Python Package Index (PyPI). The source code is publicly available on GitHub.

cs.HC↗

arXiv2025

scellop: A Scalable Redesign of Cell Population Plots for Single-Cell Data

Summary: Cell population plots are visualizations showing cell population distributions in biological samples with single-cell data, traditionally shown with stacked bar charts. Here, we address issues with this approach, particularly its limited scalability with increasing number of cell types and samples, and present scellop, a novel interactive cell population viewer combining visual encodings optimized for common user tasks in studying populations of cells across samples or conditions. Availability and Implementation: Scellop is available under the MIT licence at https://github.com/hms-dbmi/scellop, and is available on PyPI (https://pypi.org/project/cellpop/) and NPM (https://www.npmjs.com/package/cellpop). A demo is available at https://scellop.netlify.app/.

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