multipointR
A package to compare intensities of point patterns across samples with spatial parametric models
Bioconductor version: 3.24 · Package version: 0.99.6
`multipointR` is a package to compare the distribution of cells in an image or cross images with point process models. On a single image level point process models (`ppm`) model the spatial distribution of a cell type point pattern as a function of spatial covariates while accounting for natural spacing of cells. The main model class considered in `multipointR` are inhomgoeneous Gibb's point process models. Across multiple images, users can either compare multiple univariate `ppm` models in a for loop or fit one joint model across all images with `mppm`. `multipointR` provides an interface between `SpatialExperiment` and `SpatialFeatureExperiment` objects and let's users flexibly define their own `ppm`/`mppm` models with R's formula interface.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("multipointR") Details
| Maintainer | Martin Emons <martin.emons@uzh.ch> |
| Author | Martin Emons [aut, cre] (ORCID: <https://orcid.org/0009-0000-5219-5311>), Wolfgang Huber [aut] (ORCID: <https://orcid.org/0000-0002-0474-2218>), Mark D. Robinson [aut, fnd] (ORCID: <https://orcid.org/0000-0002-3048-5518>) |
| License | GPL (>= 3) |
| URL | https://github.com/mjemons/multipointR |
| Bug Reports | https://github.com/mjemons/multipointR/issues |
| Downloads rank | 23 |
| Source branch | devel |
| biocViews | SingleCell, Software, Spatial, Transcriptomics |
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | multipointR_0.99.6.tar.gz |
| Windows binary (x86_64) | multipointR_0.99.6.zip |
| macOS binary (arm64) | multipointR_0.99.6.tgz |
| macOS binary (x86_64) | multipointR_0.99.6.tgz |
Dependencies
Depends: R (>= 4.1.0)
Imports: SummarizedExperiment, methods, SpatialExperiment, spatstat.geom, spatstat.model, spatstat.explore, formula.tools, mgcv, dplyr, ggplot2, reformulas, S4Vectors, rlang
Suggests: knitr, BiocStyle, patchwork, SpatialFeatureExperiment, rmarkdown, SpatialDatasets, sosta, glmnet, testthat (>= 3.0.0)