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spatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images.

Jianhai Zhang1, Le Zhang1, Brendan Gongol1

  • 1Institute for Integrative Genome Biology, Department of Botany and Plant Sciences, 1207F Genomics Building, University of California, Riverside, CA 92521, USA.

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|February 5, 2024
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Summary
This summary is machine-generated.

The spatialHeatmap software visualizes spatial assay data on anatomical images, integrating omics data for biological insights. It offers tools for identifying spatially selective biomolecules and clusters, aiding tissue-level organization analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Visualizing spatial assay data in anatomical images is crucial for understanding biological processes at cellular, tissue, and organ levels.
  • Existing technologies for omics experiments (e.g., RNA-seq, proteomics) require robust methods for spatial data integration.

Purpose of the Study:

  • To introduce spatialHeatmap, a software package for visualizing spatial assay data in anatomical images.
  • To enable co-visualization of single-cell data with spatial heatmaps for enhanced biological interpretation.
  • To provide tools for identifying spatially selective biomolecules and clusters with similar abundance profiles.

Main Methods:

  • spatialHeatmap utilizes anatomical images and biomolecular abundance data to generate spatial heatmap plots.
  • It supports public or custom anatomical images and various omics data formats.
  • The software integrates spatial heatmaps with embedding plots for composite visualization of single-cell data.

Main Results:

  • spatialHeatmap generates spatial heatmap plots by coloring image features based on biomolecule abundance.
  • It enables co-visualization of spatial heatmaps and high-dimensional data embeddings for single-cell data.
  • Automated identification of spatially selective biomolecules and co-expressed gene clusters is achieved.

Conclusions:

  • spatialHeatmap provides essential spatial context for understanding tissue-level organization in omics data.
  • The software offers both graphical and command-line interfaces, catering to diverse user needs.
  • As an open-source Bioconductor package, spatialHeatmap is accessible for installation on various computing platforms.