Package: SPIAT 1.15.0

Yuzhou Feng

SPIAT: Spatial Image Analysis of Tissues

SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis.

Authors:Anna Trigos [aut], Yuzhou Feng [aut, cre], Tianpei Yang [aut], Mabel Li [aut], John Zhu [aut], Volkan Ozcoban [aut], Maria Doyle [aut]

SPIAT_1.15.0.tar.gz
SPIAT_1.15.0.zip(r-4.7)SPIAT_1.15.0.zip(r-4.6)SPIAT_1.15.0.zip(r-4.5)
SPIAT_1.15.0.tgz(r-4.6-any)SPIAT_1.15.0.tgz(r-4.5-any)
SPIAT_1.15.0.tar.gz(r-4.7-any)SPIAT_1.15.0.tar.gz(r-4.6-any)
SPIAT_1.15.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
SPIAT/json (API)
NEWS

# Install 'SPIAT' in R:
install.packages('SPIAT', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/trigosteam/spiat/issues

Pkgdown/docs site:https://trigosteam.github.io

Datasets:
  • defined_image - SPE object of a simulated image with defined cell types based on marker combinations.
  • image_no_markers - SPE object of a formatted image without marker intensities
  • simulated_image - SPE object of a formatted image

On BioConductor:SPIAT-1.15.0(bioc 3.24)SPIAT-1.14.0(bioc 3.23)

biomedicalinformaticscellbiologyspatialclusteringdataimportimmunooncologyqualitycontrolsinglecellsoftwarevisualization

8.47 score 28 stars 74 scripts 442 downloads 52 exports 103 dependencies

Last updated from:7e6bef6188. Checks:8 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksNOTE267
linux-devel-x86_64NOTE473
source / vignettesOK338
linux-release-x86_64NOTE464
macos-release-arm64NOTE310
macos-oldrel-arm64NOTE234
windows-develNOTE337
windows-releaseNOTE345
windows-oldrelNOTE352
wasm-releaseOK178

Exports:AUC_of_cross_functionaverage_marker_intensity_within_radiusaverage_minimum_distanceaverage_nearest_neighbor_indexaverage_percentage_of_cells_within_radiuscalculate_cell_proportionscalculate_cross_functionscalculate_distance_to_margincalculate_entropycalculate_minimum_distances_between_celltypescalculate_pairwise_distances_between_celltypescalculate_percentage_of_gridscalculate_proportions_of_cells_in_structurecalculate_spatial_autocorrelationcalculate_summary_distances_between_celltypescalculate_summary_distances_of_cells_to_borderscomposition_of_neighborhoodscompute_gradientcrossing_of_crossKdefine_celltypesdefine_structuredimensionality_reduction_plotentropy_gradient_aggregatedformat_cellprofiler_to_speformat_codex_to_speformat_colData_to_speformat_halo_to_speformat_image_to_speformat_inform_to_speformat_spe_to_pppgrid_metricsidentify_bordering_cellsidentify_neighborhoodsimage_splittermarker_intensity_boxplotmarker_prediction_plotmarker_surface_plotmarker_surface_plot_stackmeasure_association_to_cell_propertiesmixing_score_summarynumber_of_cells_within_radiusplot_average_intensityplot_cell_categoriesplot_cell_distances_violinplot_cell_marker_levelsplot_cell_percentagesplot_composition_heatmapplot_distance_heatmapplot_marker_level_heatmappredict_phenotypesR_BCselect_celltypes

Dependencies:abindapclusteraskpassBiobaseBiocFileCacheBiocGenericsbitbit64blobcachemclicolorspacecowplotcpp11crayoncurlDBIdbplyrdbscanDelayedArraydeldirdittoSeqdplyrfarverfastmapfilelockgenericsGenomicRangesggplot2ggrepelggridgesgluegoftestgridExtragtablegtoolshmshttr2IRangesisobandlabelinglatticelifecyclemagickmagrittrMatrixMatrixGenericsmatrixStatsmemoisemmandnlmeopensslpheatmappillarpkgconfigplyrpolyclippracmaprettyunitsprogresspurrrR6RANNrappdirsrasterRColorBrewerRcppreshape2rjsonrlangRSQLiteS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentspSparseArraySpatialExperimentspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrSummarizedExperimentsystensorterratibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithrXVector

Basic analyses with SPIAT

Yuzhou Feng

Rendered frombasic_analysis.Rmdusingknitr::rmarkdownon May 20 2026.

Last update: 2022-12-05
Started: 2022-12-02

Characterising tissue structure with SPIAT

Yuzhou Feng

Rendered fromtissue-structure.Rmdusingknitr::rmarkdownon May 20 2026.

Last update: 2022-12-05
Started: 2022-12-02

Identifying cellular neighborhood with SPIAT

Yuzhou Feng

Rendered fromneighborhood.Rmdusingknitr::rmarkdownon May 20 2026.

Last update: 2026-01-31
Started: 2022-12-02

Overview of the SPIAT package

Anna Trigos, Yuzhou Feng, Tianpei Yang, Mabel Li, John Zhu, Volkan Ozcoban, Maria Doyle

Rendered fromSPIAT.Rmdusingknitr::rmarkdownon May 20 2026.

Last update: 2022-12-05
Started: 2022-12-05

Quality control and visualisation with SPIAT

Yuzhou Feng

Rendered fromquality-control_visualisation.Rmdusingknitr::rmarkdownon May 20 2026.

Last update: 2022-12-05
Started: 2022-12-02

Quantifying cell colocalisation with SPIAT

Yuzhou Feng

Rendered fromcell-colocalisation.Rmdusingknitr::rmarkdownon May 20 2026.

Last update: 2023-11-02
Started: 2022-12-02

Reading in data and data formatting in SPIAT

Yuzhou Feng

Rendered fromdata_reading-formatting.Rmdusingknitr::rmarkdownon May 20 2026.

Last update: 2022-12-05
Started: 2022-12-01

Spatial heterogeneity with SPIAT

Yuzhou Feng

Rendered fromspatial-heterogeneity.Rmdusingknitr::rmarkdownon May 20 2026.

Last update: 2022-12-05
Started: 2022-12-02

Readme and manuals

Help Manual

Help pageTopics
The difference in AUC of the cross function curvesAUC_of_cross_function
average_marker_intensity_within_radiusaverage_marker_intensity_within_radius
average_minimum_distanceaverage_minimum_distance
Average nearest neighbor index for point pattern (clustering or dispersion)average_nearest_neighbor_index
average_percentage_of_cells_within_radiusaverage_percentage_of_cells_within_radius
calculate_cell_proportionscalculate_cell_proportions
calculate_cross_functionscalculate_cross_functions
calculate the distances of each cell to the margincalculate_distance_to_margin
calculate_entropycalculate_entropy
calculate_minimum_distances_between_celltypescalculate_minimum_distances_between_celltypes
calculate_pairwise_distances_between_celltypescalculate_pairwise_distances_between_celltypes
calculate_percentage_of_gridscalculate_percentage_of_grids
calculate_proportions_of_cells_in_structurecalculate_proportions_of_cells_in_structure
calculate_spatial_autocorrelationcalculate_spatial_autocorrelation
calculate_summary_distances_between_celltypescalculate_summary_distances_between_celltypes
calculate_summary_distances_of_cells_to_borderscalculate_summary_distances_of_cells_to_borders
composition_of_neighborhoodscomposition_of_neighborhoods
compute_gradientcompute_gradient
crossing_of_crossKcrossing_of_crossK
define_celltypesdefine_celltypes
define_structuredefine_structure
SPE object of a simulated image with defined cell types based on marker combinations.defined_image
Dimensionality reduction plotdimensionality_reduction_plot
The aggregated gradient of entropy and the peak of the gradiententropy_gradient_aggregated
Format a cellprofiler image into a SpatialExperiment objectformat_cellprofiler_to_spe
Format a CODEX image into a SpatialExperiment objectformat_codex_to_spe
format_colData_to_speformat_colData_to_spe
Format a HALO image into a SpatialExperiment objectformat_halo_to_spe
Format an image into a SpatialExperiment objectformat_image_to_spe
Format an inForm image into a SpatialExperiment objectformat_inform_to_spe
Format SPE object as a ppp object (`spatstat` package)format_spe_to_ppp
Split an image into grid and calculates a metric for each grid squaregrid_metrics
identify_bordering_cellsidentify_bordering_cells
identify_neighborhoodsidentify_neighborhoods
SPE object of a formatted image without marker intensities (simulated by `spaSim` package)image_no_markers
Split a large image into sub imagesimage_splitter
marker_intensity_boxplotmarker_intensity_boxplot
marker_prediction_plotmarker_prediction_plot
marker_surface_plotmarker_surface_plot
marker_surface_plot_stackmarker_surface_plot_stack
measure_association_to_cell_propertiesmeasure_association_to_cell_properties
Calculate the (normalised) mixing score for interested cell typesmixing_score_summary
Number of cells within a radiusnumber_of_cells_within_radius
plot_average_intensityplot_average_intensity
plot_cell_categoriesplot_cell_categories
plot_cell_distances_violinplot_cell_distances_violin
plot_cell_marker_levelsplot_cell_marker_levels
plot_cell_percentagesplot_cell_percentages
plot_composition_heatmapplot_composition_heatmap
plot_distance_heatmapplot_distance_heatmap
plot_marker_level_heatmapplot_marker_level_heatmap
predict_phenotypespredict_phenotypes
The ratio of border cell count to cluster cell countR_BC
select_celltypesselect_celltypes
SPE object of a formatted image (simulated by `spaSim` package)simulated_image