Package: SPIAT 1.9.0
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_1.9.0.tar.gz
SPIAT_1.9.0.zip(r-4.5)SPIAT_1.9.0.zip(r-4.4)SPIAT_1.9.0.zip(r-4.3)
SPIAT_1.9.0.tgz(r-4.4-any)SPIAT_1.9.0.tgz(r-4.3-any)
SPIAT_1.9.0.tar.gz(r-4.5-noble)SPIAT_1.9.0.tar.gz(r-4.4-noble)
SPIAT_1.9.0.tgz(r-4.4-emscripten)SPIAT_1.9.0.tgz(r-4.3-emscripten)
SPIAT.pdf |SPIAT.html✨
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:https://trigosteam.github.io
- 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-1.9.0(bioc 3.21)SPIAT-1.8.0(bioc 3.20)
biomedicalinformaticscellbiologyspatialclusteringdataimportimmunooncologyqualitycontrolsinglecellsoftwarevisualization
Last updated 2 months agofrom:da43adc71c. Checks:OK: 1 NOTE: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 18 2024 |
| R-4.5-win | NOTE | Dec 18 2024 |
| R-4.5-linux | NOTE | Dec 18 2024 |
| R-4.4-win | NOTE | Dec 18 2024 |
| R-4.4-mac | NOTE | Dec 18 2024 |
| R-4.3-win | NOTE | Dec 18 2024 |
| R-4.3-mac | NOTE | Dec 18 2024 |
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:abindapclusteraskpassBiobaseBiocFileCacheBiocGenericsbitbit64blobcachemclicolorspacecowplotcpp11crayoncurlDBIdbplyrdbscanDelayedArraydeldirdittoSeqdplyrfansifarverfastmapfilelockgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2ggrepelggridgesgluegoftestgridExtragtablegtoolshmshttrIRangesisobandjsonlitelabelinglatticelifecyclemagickmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimemmandmunsellnlmeopensslpheatmappillarpkgconfigplogrplyrpolyclippracmaprettyunitsprogresspurrrR6RANNrasterRColorBrewerRcppreshape2rjsonrlangRSQLiteS4ArraysS4VectorsscalesSingleCellExperimentspSparseArraySpatialExperimentspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrSummarizedExperimentsystensorterratibbletidyrtidyselecttzdbUCSC.utilsutf8vctrsviridisLitevroomwithrXVectorzlibbioc
Basic analyses with SPIAT
Rendered frombasic_analysis.Rmdusingknitr::rmarkdownon Dec 18 2024.Last update: 2022-12-05
Started: 2022-12-02
Characterising tissue structure with SPIAT
Rendered fromtissue-structure.Rmdusingknitr::rmarkdownon Dec 18 2024.Last update: 2022-12-05
Started: 2022-12-02
Identifying cellular neighborhood with SPIAT
Rendered fromneighborhood.Rmdusingknitr::rmarkdownon Dec 18 2024.Last update: 2022-12-05
Started: 2022-12-02
Overview of the SPIAT package
Rendered fromSPIAT.Rmdusingknitr::rmarkdownon Dec 18 2024.Last update: 2022-12-05
Started: 2022-12-05
Quality control and visualisation with SPIAT
Rendered fromquality-control_visualisation.Rmdusingknitr::rmarkdownon Dec 18 2024.Last update: 2022-12-05
Started: 2022-12-02
Quantifying cell colocalisation with SPIAT
Rendered fromcell-colocalisation.Rmdusingknitr::rmarkdownon Dec 18 2024.Last update: 2023-11-02
Started: 2022-12-02
Reading in data and data formatting in SPIAT
Rendered fromdata_reading-formatting.Rmdusingknitr::rmarkdownon Dec 18 2024.Last update: 2022-12-05
Started: 2022-12-01
Spatial heterogeneity with SPIAT
Rendered fromspatial-heterogeneity.Rmdusingknitr::rmarkdownon Dec 18 2024.Last update: 2022-12-05
Started: 2022-12-02
