Package: ISPAT3D 0.3.1

Sagnik Bhadury

ISPAT3D: Spatial Conditional Association Networks in Registered Tumor Volumes

Fits tumor-zone-specific conditional cell-density networks from registered three-dimensional multiplex imaging. An anisotropic Matern-3/2 Gaussian process is estimated per variable and zone using a Vecchia likelihood on spatially balanced anchors; predictions at all selected cells yield residual covariance sufficient statistics. Gaussian maximum likelihood then fits a shared-plus-zone factor covariance model. A matched section-wise planar fit uses the same selected cells and covariance estimator. This extends the spatially informed cell-density analysis of Bhadury et al. (2026) <doi:10.1038/s41598-026-35341-8>.

Authors:Sagnik Bhadury [aut, cre]

ISPAT3D_0.3.1.tar.gz
ISPAT3D_0.3.1.zip(r-4.7-any)ISPAT3D_0.3.1.zip(r-4.6-any)ISPAT3D_0.3.1.zip(r-4.5-any)
ISPAT3D_0.3.1.tgz(r-4.6-any)ISPAT3D_0.3.1.tgz(r-4.5-any)
ISPAT3D_0.3.1.tar.gz(r-4.7-any)ISPAT3D_0.3.1.tar.gz(r-4.6-any)
ISPAT3D_0.3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ISPAT3D/json (API)

# Install 'ISPAT3D' in R:
install.packages('ISPAT3D', repos = c('https://sagnikbhadury.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sagnikbhadury/ispat-3d/issues

On CRAN:

Conda:

4.70 score 9 exports 6 dependencies

Last updated from:18626f049d. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK138
source / vignettesOK190
linux-releaseOK120
macos-releaseOK154
macos-oldrelOK184
windows-develOK80
windows-releaseOK56
windows-oldrelOK89
wasm-releaseOK112

Exports:ispat3d_edge_tableispat3d_example_imageispat3d_fitispat3d_fit_2dispat3d_fit_covarianceispat3d_partial_correlationispat3d_plot_networkispat3d_plot_zonesispat3d_sample

Dependencies:data.tablegpboostlatticeMatrixR6RJSONIO

A volumetric cell-density network from a small registered image
1. Simulate a registered cell map and inspect the image | 2. Select a reproducible roster | 3. Fit the volumetric GP and factor covariance | 4. Obtain and display zone networks | 5. Apply the same path to a processed image | References
Rendered fromvolumetric-network.Rmdusingknitr::rmarkdown

Last update: 2026-09-16
Started: 2026-09-16

Matched section-wise 2D networks and a 3D comparison
1. Prepare a small serial-section image | 2. Fix the cell roster before either fit | 3. Fit the section-wise planar model | 4. Compute and plot conditional networks | 5. Compare with the volumetric fit on identical cells | 6. Use processed serial-section data | References
Rendered frommatched-planar-network.Rmdusingknitr::rmarkdown

Last update: 2026-09-16
Started: 2026-09-16