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  "Title": "Spatial Image Analysis of Tissues",
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  "Description": "SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues)\nis an R package with a suite of data processing, quality\ncontrol, visualization and data analysis tools. SPIAT is\ncompatible with data generated from single-cell spatial\nproteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler).\nSPIAT reads spatial data in the form of X and Y coordinates of\ncells, marker intensities and cell phenotypes. SPIAT includes\nsix analysis modules that allow visualization, calculation of\ncell colocalization, categorization of the immune\nmicroenvironment relative to tumor areas, analysis of cellular\nneighborhoods, and the quantification of spatial heterogeneity,\nproviding a comprehensive toolkit for spatial data analysis.",
  "License": "Artistic-2.0 + file LICENSE",
  "Encoding": "UTF-8",
  "biocViews": "BiomedicalInformatics, CellBiology, Spatial, Clustering,\nDataImport, ImmunoOncology, QualityControl, SingleCell,\nSoftware, Visualization",
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  "Date/Publication": "2026-04-28 12:59:05 UTC",
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      "title": "The difference in AUC of the cross function curves",
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      "topics": [
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      "title": "format_colData_to_spe",
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      "title": "Format a HALO image into a SpatialExperiment object",
      "topics": [
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      "title": "Format an image into a SpatialExperiment object",
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      "title": "Format an inForm image into a SpatialExperiment object",
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      "topics": [
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    {
      "page": "image_no_markers",
      "title": "SPE object of a formatted image without marker intensities (simulated by `spaSim` package)",
      "topics": [
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    },
    {
      "page": "image_splitter",
      "title": "Split a large image into sub images",
      "topics": [
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    },
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      "page": "marker_prediction_plot",
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      "topics": [
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      "topics": [
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      "page": "measure_association_to_cell_properties",
      "title": "measure_association_to_cell_properties",
      "topics": [
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      "topics": [
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      "page": "plot_average_intensity",
      "title": "plot_average_intensity",
      "topics": [
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      "page": "plot_cell_categories",
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      "topics": [
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      "page": "plot_composition_heatmap",
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      "page": "R_BC",
      "title": "The ratio of border cell count to cluster cell count",
      "topics": [
        "R_BC"
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      "page": "select_celltypes",
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        "Mixing Score (MS) and Normalised Mixing Score (NMS)",
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        "Cross-K Intersection (CKI)",
        "You can access the vignettes for other modules of SPIAT here:",
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        "Reading in data from inForm",
        "Reading in data from HALO",
        "Inspecting the SpaitalExperiment object",
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        "Splitting images",
        "Predicting cell phenotypes",
        "Specifying cell types",
        "You can access the vignettes for other modules of SPIAT here:",
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        "Fishnet grid",
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