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Related Experiment Video

Updated: Dec 23, 2025

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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SpatialCPie: an R/Bioconductor package for spatial transcriptomics cluster evaluation.

Joseph Bergenstråhle1, Ludvig Bergenstråhle2, Joakim Lundeberg2,3

  • 1Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden. j.bergenstrahle@scilifelab.se.

BMC Bioinformatics
|May 1, 2020
PubMed
Summary

SpatialCPie is a new R package for spatial transcriptomics analysis. It helps researchers evaluate clusters and visualize their relationships, simplifying complex spatial data interpretation.

Keywords:
Cluster analysisData visualizationR packageSpatial transcriptomics

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Spatial transcriptomics enables transcriptomic data analysis within its spatial context.
  • Clustering is a common but challenging step in spatial transcriptomics data analysis, particularly in determining the optimal number of clusters and their interrelationships.

Purpose of the Study:

  • To introduce SpatialCPie, an R package designed to aid in the evaluation of clusters for spatial transcriptomics data.
  • To provide intuitive visualizations for understanding cluster similarities and relationships across different resolutions.

Main Methods:

  • SpatialCPie clusters spatial transcriptomics data at multiple resolutions.
  • The package generates visualizations including pie charts for spatial region-cluster similarity and a cluster graph illustrating relationships between clusters at various resolutions.

Main Results:

  • SpatialCPie effectively clusters spatial transcriptomics data at multiple resolutions.
  • The generated visualizations offer clear insights into the similarity between spatial regions and clusters.
  • The cluster graph effectively displays relationships between clusters across different resolutions.

Conclusions:

  • SpatialCPie offers intuitive and effective visualizations for exploring cluster relationships in spatial transcriptomics data.
  • The R package facilitates a more accessible interpretation of complex spatial transcriptomics datasets.