Spatial Transcriptomics Toolbox
The Spatial Transcriptomics Toolbox is a collection of tools for processing, quality control, analysis, and visualization of spatial transcriptomics data. The toolbox supports common challenges in imaging-based and high-dimensional spatial omics workflows, including cell overlap detection, spatial clustering, spatially informed dimensionality reduction, cell-segmentation-free analysis, and automated color assignment for complex datasets.
Key benefits
Collection of complementary tools for spatial transcriptomics workflows
Supports quality control, processing, analysis, and visualization tasks
Addresses imaging-based spatial transcriptomics data challenges
Includes methods for spatial clustering and dimensionality reduction
Provides tools for both cell-based and cell-segmentation-free analysis
Applications
Detection of overlapping cells in imaging-based spatial transcriptomics data
Spatial clustering of cells or spatial observations
Spatially informed dimensionality reduction of high-dimensional datasets
Cell-segmentation-free analysis of spatial transcriptomics data
Automated color assignment for visualization of high-dimensional results
Included tools
ovrl.py – cell overlap detection in imaging-based spatial transcriptomics data
SpatialLeiden – spatial clustering for spatial transcriptomics analysis
multiSPAETI – spatially informed dimensionality reduction
sainsc – cell-segmentation-free analysis
clrmappy – automatic color assignment to high-dimensional data
Intended use
The Spatial Transcriptomics Toolbox is intended for spatial transcriptomics researchers, bioinformaticians, computational biologists, bioimage analysts, and single-cell researchers working with high-dimensional spatial omics data. It is particularly suited for users who need modular tools for spatial data quality control, clustering, dimensionality reduction, visualization, or analysis workflows that do not rely strictly on prior cell segmentation.
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