A Part-to-Whole Circular Cell Explorer

TL;DR

This paper introduces an interactive visual analysis system for spatial transcriptomics data, improving visualization and analysis of cellular measurements within tissues to facilitate deeper biological insights.

Contribution

It presents a novel visualization system that enhances perceptual clarity and analysis capabilities for spatial transcriptomics data, addressing limitations of existing methods.

Findings

Improved visualization of cell distributions in tissues.

Enhanced filtering, drilling, and clustering analysis features.

Facilitated deeper understanding of molecular mechanisms.

Abstract

Spatial transcriptomics methods capture cellular measurements such as gene expression and cell types at specific locations in a cell, helping provide a localized picture of tissue health. Traditional visualization techniques superimpose the tissue image with pie charts for the cell distribution. We design an interactive visual analysis system that addresses perceptual problems in the state of the art, while adding filtering, drilling, and clustering analysis capabilities. Our approach can help researchers gain deeper insights into the molecular mechanisms underlying complex biological processes within tissues.