Cellular neighborhood | Average Nearest Neighbour Index (ANNI) | You can access the vignettes for other modules of SPIAT here: | Reproducibility | Author Contributions
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:
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✨
DESCRIPTION |NEWS
card.svg |card.png
SPIAT/json (API)
| # 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
Last updated from:7e6bef6188. Checks:8 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| bioc-checks | NOTE | 243 | ||
| linux-devel-x86_64 | NOTE | 452 | ||
| source / vignettes | OK | 342 | ||
| linux-release-x86_64 | NOTE | 445 | ||
| macos-release-arm64 | NOTE | 316 | ||
| macos-oldrel-arm64 | NOTE | 220 | ||
| windows-devel | NOTE | 361 | ||
| windows-release | NOTE | 343 | ||
| windows-oldrel | NOTE | 344 | ||
| wasm-release | OK | 172 |
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
Identifying cellular neighborhood with SPIAT
Last update: 2026-01-31
Started: 2022-12-02
Quantifying cell colocalisation with SPIAT
Cells In Neighbourhood (CIN) | Mixing Score (MS) and Normalised Mixing Score (NMS) | Cross K function | Cross-K Intersection (CKI) | You can access the vignettes for other modules of SPIAT here: | Reproducibility | Author Contributions
Last update: 2023-11-02
Started: 2022-12-02
Basic analyses with SPIAT
Cell percentages | Cell distances | Pairwise cell distances | Minimum cell distances | You can access the vignettes for other modules of SPIAT here: | Reproducibility | Author Contributions
Last update: 2022-12-05
Started: 2022-12-02
Characterising tissue structure with SPIAT
Characterising the distribution of the cells of interest in identified tissue regions | Determining whether there is a clear tumour margin | Automatic identification of the tumour margin | Classification of cells based on their locations relative to the margin | You can access the vignettes for other modules of SPIAT here: | Reproducibility | Author Contributions
Last update: 2022-12-05
Started: 2022-12-02
Overview of the SPIAT package
Introduction | You can access the vignettes for other modules of SPIAT here: | Data reading and formatting | Quality control and visualisation | Basic analysis | Cell colocalisation | Spatial heterogeneity | Tissue structure | Cellular neighborhood | Installing SPIAT | Citing SPIAT | Author Contributions
Last update: 2022-12-05
Started: 2022-12-05
Quality control and visualisation with SPIAT
Visualise marker levels | Boxplots of marker intensities | Scatter plots of marker levels | Heatmaps of marker levels | Identifying incorrect phenotypes | Removing cells with incorrect phenotypes | Dimensionality reduction to identify misclassified cells | Visualising tissues | Categorical dot plot | 3D surface plot | 3D stacked surface plot | You can access the vignettes for other modules of SPIAT here: | Reproducibility | Author Contributions
Last update: 2022-12-05
Started: 2022-12-02
Reading in data and data formatting in SPIAT
Reading in data | Reading in data through the 'general' option (RECOMMENDED) | Reading in data pre-formatted by other software | Reading in data from inForm | Reading in data from HALO | Inspecting the SpaitalExperiment object | Structure of a SPIAT SpatialExperiment object | Nomenclature | Splitting images | Predicting cell phenotypes | Specifying cell types | You can access the vignettes for other modules of SPIAT here: | Reproducibility | Author Contributions
Last update: 2022-12-05
Started: 2022-12-01
Spatial heterogeneity with SPIAT
Localised Entropy | Fishnet grid | Gradients (based on concentric circles) | You can access the vignettes for other modules of SPIAT here: | Reproducibility | Author Contributions
Last update: 2022-12-05
Started: 2022-12-02
