Background for Tissue Microarray (TMA) Data | Create Multiplex ImmunoFlourescent (mif) Object | Plotting Cores | Estimating the degree of spatial clustering with Count Based Methods | Univariate | Count Based Methods | Distance Based Measures | Need for Permutations | Calculating Exact CSR | Implementation | Univariate Count-Based Methods | Sensitivity of r | Bivariate Count-Based Methods | Univariate Nearest-Neighbor Methods | Bivariate Nearest-Neighbor Methods | Permutation Distribution | Subsetting by Compartment | Follow-up Analysis | Using Custom Cell Locations
Package: spatialTIME 1.3.4-5
Fridley Lab
spatialTIME: Spatial Analysis of Vectra Immunoflourescent Data
Visualization and analysis of Vectra Immunoflourescent data. Options for calculating both the univariate and bivariate Ripley's K are included. Calculations are performed using a permutation-based approach presented by Wilson et al. <doi:10.1101/2021.04.27.21256104>.
Authors:
spatialTIME_1.3.4-5.tar.gz
spatialTIME_1.3.4-5.zip(r-4.7)spatialTIME_1.3.4-5.zip(r-4.6)spatialTIME_1.3.4-5.zip(r-4.5)
spatialTIME_1.3.4-5.tgz(r-4.6-any)spatialTIME_1.3.4-5.tgz(r-4.5-any)
spatialTIME_1.3.4-5.tar.gz(r-4.7-any)spatialTIME_1.3.4-5.tar.gz(r-4.6-any)
spatialTIME_1.3.4-5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
spatialTIME/json (API)
| # Install 'spatialTIME' in R: |
| install.packages('spatialTIME', repos = c('https://fridleylab.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fridleylab/spatialtime/issues
Datasets:
- example_clinical - Clinical variables of 229 patients
- example_spatial - Example list of 5 spatial TMA data
- example_summary - Marker summaries of 229 samples
Last updated from:391197e34d. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 178 | ||
| source / vignettes | OK | 319 | ||
| linux-release-x86_64 | OK | 177 | ||
| macos-release-arm64 | OK | 114 | ||
| macos-oldrel-arm64 | OK | 100 | ||
| windows-devel | OK | 106 | ||
| windows-release | OK | 98 | ||
| windows-oldrel | OK | 102 | ||
| wasm-release | OK | 159 |
Exports:%>%bi_NN_Gbi_pair_correlationbi_ripleys_kbi_ripleys_k_WSIcreate_mifdixons_sinteraction_variablemerge_mifsNN_Gpair_correlationplot_immunofloripleys_ksubset_mif
Dependencies:abindclicodetoolscpp11crayondeldirdigestdixondplyrfarverfurrrfuturegenericsggplot2globalsgluegoftestgtableisobandlabelinglatticelifecyclelistenvmagrittrMatrixmgcvnlmeparallellypbmcapplypillarpkgconfigpolyclippurrrR6RColorBrewerrlangrpartS7scalesspspatstatspatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.univarspatstat.utilssplancsstringistringrtensortibbletidyrtidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bivariate Nearest Neighbor G(r) | bi_NN_G |
| Bivariate Pair Correlation Function | bi_pair_correlation |
| Bivariate Ripley's K | bi_ripleys_k |
| Bivariate Ripley's K for Whole Slide Images | bi_ripleys_k_WSI |
| Calculate Count Based Measures and NN Measures of Spatial Clustering for IF data | compute_metrics |
| Create Multiplex Immunoflourescent object | create_mif |
| Dixon's S Segregation Statistic | dixons_s |
| Clinical variables of 229 patients | example_clinical |
| Example list of 5 spatial TMA data | example_spatial |
| Marker summaries of 229 samples | example_summary |
| Bivariate Interaction Variable | interaction_variable |
| Merge several MIF objects together | merge_mifs |
| Univariate Nearest Neighbor G(r) | NN_G |
| Univariate Pair Correlation Function | pair_correlation |
| Generate plot of TMA point process | plot_immunoflo |
| Calculate Ripley's K | ripleys_k |
| Subset mif object on cellular level | subset_mif |