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

2025-10-10

AI Summary

Traditional stacked bar charts lack scalability and interactivity for visualizing high-dimensional cell types across multiple samples in single-cell data. To address this, we present scellop—a web-based, interactive browser for exploring cell population distributions in large-scale single-cell datasets. scellop innovatively integrates multiple visual encodings—including grouped heatmaps, nested circular layouts, and condition-driven color mapping—to enhance readability and analytical efficiency in cross-sample and cross-condition comparisons. Built on a modular web architecture (Python backend + JavaScript frontend), it supports dynamic data loading and real-time interactive exploration. The tool is fully open-source (available on GitHub, PyPI, and NPM) and includes an online demo. scellop fills a critical gap in the single-cell multi-omics ecosystem by providing the first scalable, interactive, and integrative visualization solution for population-level analysis across diverse experimental conditions and cohorts.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Humans and AI: Crowd Sourcing and Human Computation
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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/.

Problem

• Addressing limited scalability in cell population visualization
• Improving interactive analysis of cell types across samples
• Optimizing visual encodings for common biological research tasks

Innovation

• Interactive viewer combining optimized visual encodings
• Scalable redesign addressing cell type limitations
• Novel approach for cross-sample population analysis