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Visual Compositional Data Analytics for Spatial Transcriptomics

David Hägele, Yuxuan Tang, Daniel Weiskopf

TL;DR

A visual analytics system as a redesign for the scatter pie chart visualization of cell type proportions of spatial transcriptomics data, which shifts the pattern recognition workload from the human visual system to computational methods commonly used in visual analytics.

Abstract

For the Bio+Med-Vis Challenge 2024, we propose a visual analytics system as a redesign for the scatter pie chart visualization of cell type proportions of spatial transcriptomics data. Our design uses three linked views: a view of the histological image of the tissue, a stacked bar chart showing cell type proportions of the spots, and a scatter plot showing a dimensionality reduction of the multivariate proportions. Furthermore, we apply a compositional data analysis framework, the Aitchison geometry, to the proportions for dimensionality reduction and $k$-means clustering. Leveraging brushing and linking, the system allows one to explore and uncover patterns in the cell type mixtures and relate them to their spatial locations on the cellular tissue. This redesign shifts the pattern recognition workload from the human visual system to computational methods commonly used in visual analytics. We provide the code and setup instructions of our visual analytics system on GitHub (https://github.com/UniStuttgart-VISUS/va-for-spatial-transcriptomics).

Visual Compositional Data Analytics for Spatial Transcriptomics

TL;DR

A visual analytics system as a redesign for the scatter pie chart visualization of cell type proportions of spatial transcriptomics data, which shifts the pattern recognition workload from the human visual system to computational methods commonly used in visual analytics.

Abstract

For the Bio+Med-Vis Challenge 2024, we propose a visual analytics system as a redesign for the scatter pie chart visualization of cell type proportions of spatial transcriptomics data. Our design uses three linked views: a view of the histological image of the tissue, a stacked bar chart showing cell type proportions of the spots, and a scatter plot showing a dimensionality reduction of the multivariate proportions. Furthermore, we apply a compositional data analysis framework, the Aitchison geometry, to the proportions for dimensionality reduction and -means clustering. Leveraging brushing and linking, the system allows one to explore and uncover patterns in the cell type mixtures and relate them to their spatial locations on the cellular tissue. This redesign shifts the pattern recognition workload from the human visual system to computational methods commonly used in visual analytics. We provide the code and setup instructions of our visual analytics system on GitHub (https://github.com/UniStuttgart-VISUS/va-for-spatial-transcriptomics).
Paper Structure (3 sections, 1 figure)

This paper contains 3 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Design
  3. Limitations and Outlook

Figures (1)

  • Figure 1: Selecting different blobs in the dimensionality reduction view highlights corresponding areas that exhibit different proportion patterns.