arXiv subjects

John Conroy

Publications and source records attributed to John Conroy.

4 recordsLinked to original sources

arXiv2025

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↗

arXiv2025

Vitessce Link: A Mixed Reality and 2D Display Hybrid Approach for Visual Analysis of 3D Tissue Maps

Advances in spatial omics and high-resolution imaging enable the creation of three-dimensional (3D) tissue maps that capture cellular organization and interactions in situ. While these data provide critical insights into tissue function and disease, their exploration is often constrained by tools limited to 2D displays or stereoscopic rendering without analytical integration. We present Vitessce Link, a web-based hybrid framework that unites a 3D stereoscopic view in mixed reality with a synchronized 2D display environment. Users can navigate volumetric data with intuitive hand gestures while controlling channels, filters, and derived data views through the Vitessce platform. Built on open standards and running entirely in the browser, Vitessce Link minimizes friction, supports integration with computational notebooks, and synchronizes interactions across devices via a lightweight WebSocket architecture. Case studies in nephrology and oncology demonstrate how the hybrid approach enhances segmentation evaluation, distance measurement, and interpretation of spatial relationships. Vitessce Link establishes a paradigm for integrative, web-native analysis of 3D tissue maps.

cs.HC↗

arXiv2025

Gosling Designer: a Platform to Democratize Construction and Sharing of Genomics Data Visualization Tools

Analysis of genomics data is central to nearly all areas of modern biology. Despite significant progress in artificial intelligence (AI) and computational methods, these technologies require significant human oversight to generate novel and reliable biological insights. Consequently, the genomics community has developed a substantial number of diverse visualization approaches and a proliferation of tools that biologists rely on in their data analysis workflows. While there are a few commonly used visualization tools for genomics data, many tools target specific use cases for genomics data interpretation and offer only a limited, predefined set of visualization types. Moreover, static visualizations often fail to support exploratory analysis. Developing interactive visualizations and tools typically requires significant time and technical expertise, even when supported by modern LLM-powered coding assistants, and the resulting visualizations can be difficult to share among collaborators. We developed Gosling Designer, an all-in-one platform for editing, exploring, and sharing visualizations of genomics data. Gosling Designer addresses four key challenges observed in existing genomics visualization tools: (1) limited versatility, (2) difficulty of visualization authoring, (3) complexity of data management, and (4) barriers to sharing and collaboration.

q-bio.GN↗

arXiv2025

Auto-ARGUE: LLM-Based Report Generation Evaluation

Generation of citation-backed reports is a primary use case for retrieval-augmented generation (RAG) systems. While open-source evaluation tools exist for various RAG tasks, tools designed for report generation are lacking. Accordingly, we introduce Auto-ARGUE, a robust LLM-based implementation of the recently proposed ARGUE framework for report generation evaluation. We present analysis of Auto-ARGUE on the report generation pilot task from the TREC 2024 NeuCLIR track and on two tasks from the TREC 2024 RAG track, showing good system-level correlations with human judgments. Additionally, we release ARGUE-Viz, a web app for visualization and fine-grained analysis of Auto-ARGUE judgments and scores.

cs.IR↗