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A Simplified Positional Cell Type Visualization using Spatially Aggregated Clusters

Lee Mason, Jonas Almeida

TL;DR

A novel method for overlaying cell type proportion data onto tissue images by clustering the data and aggregating neighboring points of the same cluster into polygons is introduced.

Abstract

We introduce a novel method for overlaying cell type proportion data onto tissue images. This approach preserves spatial context while avoiding visual clutter or excessively obscuring the underlying slide. Our proposed technique involves clustering the data and aggregating neighboring points of the same cluster into polygons.

A Simplified Positional Cell Type Visualization using Spatially Aggregated Clusters

TL;DR

A novel method for overlaying cell type proportion data onto tissue images by clustering the data and aggregating neighboring points of the same cluster into polygons is introduced.

Abstract

We introduce a novel method for overlaying cell type proportion data onto tissue images. This approach preserves spatial context while avoiding visual clutter or excessively obscuring the underlying slide. Our proposed technique involves clustering the data and aggregating neighboring points of the same cluster into polygons.

Paper Structure

This paper contains 3 sections, 1 figure.

Table of Contents

  1. Identifying the problem
  2. Our solution: spatially aggregated clustering
  3. Discussion & Future Work

Figures (1)

  • Figure 1: A breakdown of the proposed solution. a) We first cluster the points using k-means, assign a color to each cluster, and color each point according to its assigned cluster label. b) We aggregate neighboring points with the same cluster and generate polygons of each group with a concave hull algorithm. c) We subtract overlapping polygons to ensure polygons are exclusive.