R-universe search: topic:spatial

r-spatial

sf:Simple Features for R

Support for simple feature access, a standardized way to encode and analyze spatial vector data. Binds to 'GDAL' <doi:10.5281/zenodo.5884351> for reading and writing data, to 'GEOS' <doi:10.5281/zenodo.11396894> for geometrical operations, and to 'PROJ' <doi:10.5281/zenodo.5884394> for projection conversions and datum transformations. Uses by default the 's2' package for geometry operations on geodetic (long/lat degree) coordinates.

Maintained by Edzer Pebesma. Last updated 3 days ago.

gdal geos proj spatial cpp

1.4k stars 22.44 score 117k scripts 1.2k dependents

r-spatial

stars:Spatiotemporal Arrays, Raster and Vector Data Cubes

Reading, manipulating, writing and plotting spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL' bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'.

Maintained by Edzer Pebesma. Last updated 1 months ago.

raster satellite-images spatial

571 stars 18.27 score 7.2k scripts 137 dependents

rspatial

terra:Spatial Data Analysis

Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).

Maintained by Robert J. Hijmans. Last updated 9 hours ago.

geospatial raster spatial vector onetbb proj gdal geos cpp

560 stars 17.65 score 17k scripts 856 dependents

rstudio

leaflet:Create Interactive Web Maps with the JavaScript 'Leaflet' Library

Create and customize interactive maps using the 'Leaflet' JavaScript library and the 'htmlwidgets' package. These maps can be used directly from the R console, from 'RStudio', in Shiny applications and R Markdown documents.

Maintained by Joe Cheng. Last updated 25 days ago.

gis leaflet-map spatial

821 stars 17.20 score 39k scripts 178 dependents

r-tmap

tmap:Thematic Maps

Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.

Maintained by Martijn Tennekes. Last updated 15 hours ago.

choropleth-maps maps spatial thematic-maps visualisation

879 stars 16.25 score 13k scripts 24 dependents

r-spatial

mapview:Interactive Viewing of Spatial Data in R

Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.

Maintained by Tim Appelhans. Last updated 3 months ago.

gis leaflet maps spatial visualization web-mapping

526 stars 14.39 score 7.3k scripts 27 dependents

nowosad

spData:Datasets for Spatial Analysis

Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep'). Unlike other spatial data packages such as 'rnaturalearth' and 'maps', it also contains data stored in a range of file formats including GeoJSON and GeoPackage, but from version 2.3.4, no longer ESRI Shapefile - use GeoPackage instead. Some of the datasets are designed to illustrate specific analysis techniques. cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.

Maintained by Jakub Nowosad. Last updated 3 months ago.

datasets raster sf sp spatial spdep

82 stars 13.23 score 3.4k scripts 116 dependents

bioc

SpatialExperiment:S4 Class for Spatially Resolved -omics Data

Defines an S4 class for storing data from spatial -omics experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform.

Maintained by Dario Righelli. Last updated 5 months ago.

datarepresentation dataimport infrastructure immunooncology geneexpression transcriptomics singlecell spatial

59 stars 12.63 score 1.8k scripts 71 dependents

r-spatialecology

landscapemetrics:Landscape Metrics for Categorical Map Patterns

Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' (<https://www.fragstats.org/>) and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.

Maintained by Maximilian H.K. Hesselbarth. Last updated 2 months ago.

landscape-ecology landscape-metrics raster spatial cpp

240 stars 12.47 score 584 scripts 4 dependents

ropensci

stplanr:Sustainable Transport Planning

Tools for transport planning with an emphasis on spatial transport data and non-motorized modes. The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool (Lovelace et al. 2017) <doi:10.5198/jtlu.2016.862>, but has since been extended to support public transport routing and accessibility analysis (Moreno-Monroy et al. 2017) <doi:10.1016/j.jtrangeo.2017.08.012> and routing with locally hosted routing engines such as 'OSRM' (Lowans et al. 2023) <doi:10.1016/j.enconman.2023.117337>. The main functions are for creating and manipulating geographic "desire lines" from origin-destination (OD) data (building on the 'od' package); calculating routes on the transport network locally and via interfaces to routing services such as <https://cyclestreets.net/> (Desjardins et al. 2021) <doi:10.1007/s11116-021-10197-1>; and calculating route segment attributes such as bearing. The package implements the 'travel flow aggregration' method described in Morgan and Lovelace (2020) <doi:10.1177/2399808320942779> and the 'OD jittering' method described in Lovelace et al. (2022) <doi:10.32866/001c.33873>. Further information on the package's aim and scope can be found in the vignettes and in a paper in the R Journal (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053>, and in a paper outlining the landscape of open source software for geographic methods in transport planning (Lovelace, 2021) <doi:10.1007/s10109-020-00342-2>.

Maintained by Robin Lovelace. Last updated 7 months ago.

cycle cycling desire-lines origin-destination peer-reviewed pubic-transport route-network routes routing spatial transport transport-planning transportation walking

427 stars 12.31 score 684 scripts 3 dependents

hannameyer

CAST:'caret' Applications for Spatial-Temporal Models

Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. It includes the newly suggested 'Nearest neighbor distance matching' cross-validation to estimate the performance of spatial prediction models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>; Milà et al. (2022) <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022) <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2023) <doi:10.5194/egusphere-2023-1308>; Schumacher et al. (2024) <doi:10.5194/egusphere-2024-2730>. The package is described in detail in Meyer et al. (2024) <doi:10.48550/arXiv.2404.06978>.

Maintained by Hanna Meyer. Last updated 2 months ago.

autocorrelation caret feature-selection machine-learning overfitting predictive-modeling spatial spatio-temporal variable-selection

114 stars 11.85 score 298 scripts 1 dependents

prioritizr

prioritizr:Systematic Conservation Prioritization in R

Systematic conservation prioritization using mixed integer linear programming (MILP). It provides a flexible interface for building and solving conservation planning problems. Once built, conservation planning problems can be solved using a variety of commercial and open-source exact algorithm solvers. By using exact algorithm solvers, solutions can be generated that are guaranteed to be optimal (or within a pre-specified optimality gap). Furthermore, conservation problems can be constructed to optimize the spatial allocation of different management actions or zones, meaning that conservation practitioners can identify solutions that benefit multiple stakeholders. To solve large-scale or complex conservation planning problems, users should install the Gurobi optimization software (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see Gurobi Installation Guide vignette for details). Users can also install the IBM CPLEX software (<https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer>) and the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>). Additionally, the 'rcbc' R package (available at <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions using the CBC optimization software (<https://github.com/coin-or/Cbc>). For further details, see Hanson et al. (2025) <doi:10.1111/cobi.14376>.

Maintained by Richard Schuster. Last updated 2 days ago.

biodiversity conservation conservation-planner optimization prioritization solver spatial cpp

124 stars 11.71 score 584 scripts 2 dependents

riatelab

mapsf:Thematic Cartography

Create and integrate thematic maps in your workflow. This package helps to design various cartographic representations such as proportional symbols, choropleth or typology maps. It also offers several functions to display layout elements that improve the graphic presentation of maps (e.g. scale bar, north arrow, title, labels). 'mapsf' maps 'sf' objects on 'base' graphics.

Maintained by Timothée Giraud. Last updated 11 days ago.

cartography map spatial spatial-analysis

229 stars 11.32 score 414 scripts 12 dependents

ropengov

giscoR:Download Map Data from GISCO API - Eurostat

Tools to download data from the GISCO (Geographic Information System of the Commission) Eurostat database <https://ec.europa.eu/eurostat/web/gisco>. Global and European map data available. This package is in no way officially related to or endorsed by Eurostat.

Maintained by Diego Hernangómez. Last updated 3 days ago.

ropengov spatial api-wrapper eurostat gisco thematic-maps eurostat-data ggplot2 gis

75 stars 10.70 score 424 scripts 5 dependents

ropensci

geojson:Classes for 'GeoJSON'

Classes for 'GeoJSON' to make working with 'GeoJSON' easier. Includes S3 classes for 'GeoJSON' classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited 'GeoJSON'; and serializing to/from 'Geobuf' binary 'GeoJSON' format.

Maintained by Michael Sumner. Last updated 2 years ago.

geojson geospatial conversion data input-output bbox polygon geobuf crs ndgeojson spatial

32 stars 10.56 score 166 scripts 14 dependents

rvalavi

blockCV:Spatial and Environmental Blocking for K-Fold and LOO Cross-Validation

Creating spatially or environmentally separated folds for cross-validation to provide a robust error estimation in spatially structured environments; Investigating and visualising the effective range of spatial autocorrelation in continuous raster covariates and point samples to find an initial realistic distance band to separate training and testing datasets spatially described in Valavi, R. et al. (2019) <doi:10.1111/2041-210X.13107>.

Maintained by Roozbeh Valavi. Last updated 5 months ago.

cross-validation spatial spatial-cross-validation spatial-modelling species-distribution-modelling cpp

113 stars 10.49 score 302 scripts 3 dependents

hypertidy

fasterize:Fast Polygon to Raster Conversion

Provides a drop-in replacement for rasterize() from the 'raster' package that takes polygon vector or data frame objects, and is much faster. There is support for the main options provided by the rasterize() function, including setting the field used and background value, and options for aggregating multi-layer rasters. Uses the scan line algorithm attributed to Wylie et al. (1967) <doi:10.1145/1465611.1465619>. Note that repository originally was hosted at 'Github' 'ecohealthalliance/fasterize' but was migrated to 'hypertidy/fasterize' in March 2025, and can be found indexed on 'R universe' <https://cran.r-universe.dev/fasterize>.

Maintained by Michael Sumner. Last updated 21 days ago.

raster rcpp rcpparmadillo sf spatial cpp

182 stars 10.05 score 14 dependents

ropensci

spatsoc:Group Animal Relocation Data by Spatial and Temporal Relationship

Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019) <doi:10.1111/2041-210X.13215>). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks In addition, the randomizations function provides data-stream randomization methods suitable for GPS data.

Maintained by Alec L. Robitaille. Last updated 2 months ago.

animal gps network social spatial

24 stars 9.97 score 145 scripts 3 dependents

symbolixau

googleway:Accesses Google Maps APIs to Retrieve Data and Plot Maps

Provides a mechanism to plot a 'Google Map' from 'R' and overlay it with shapes and markers. Also provides access to 'Google Maps' APIs, including places, directions, roads, distances, geocoding, elevation and timezone.

Maintained by David Cooley. Last updated 7 months ago.

google-map google-maps google-maps-api google-maps-javascript-api spatial spatial-analysis

236 stars 9.67 score 536 scripts 2 dependents

jeffreyevans

spatialEco:Spatial Analysis and Modelling Utilities

Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics.

Maintained by Jeffrey S. Evans. Last updated 26 days ago.

biodiversity conservation ecology r-spatial raster spatial vector

110 stars 9.55 score 736 scripts 2 dependents

mstrimas

smoothr:Smooth and Tidy Spatial Features

Tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. Smooth curves, fill holes, and remove small fragments from lines and polygons.

Maintained by Matthew Strimas-Mackey. Last updated 2 years ago.

gis spatial

100 stars 9.53 score 440 scripts 9 dependents

bioc

SpatialFeatureExperiment:Integrating SpatialExperiment with Simple Features in sf

A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used.

Maintained by Lambda Moses. Last updated 2 months ago.

datarepresentation transcriptomics spatial

49 stars 9.40 score 322 scripts 1 dependents

mapme-initiative

mapme.biodiversity:Efficient Monitoring of Global Biodiversity Portfolios

Biodiversity areas, especially primary forest, serve a multitude of functions for local economy, regional functionality of the ecosystems as well as the global health of our planet. Recently, adverse changes in human land use practices and climatic responses to increased greenhouse gas emissions, put these biodiversity areas under a variety of different threats. The present package helps to analyse a number of biodiversity indicators based on freely available geographical datasets. It supports computational efficient routines that allow the analysis of potentially global biodiversity portfolios. The primary use case of the package is to support evidence based reporting of an organization's effort to protect biodiversity areas under threat and to identify regions were intervention is most duly needed.

Maintained by Darius A. Görgen. Last updated 23 hours ago.

environment eo gis mapme spatial sustainability

35 stars 9.24 score 287 scripts

joelgombin

concaveman:A Very Fast 2D Concave Hull Algorithm

The concaveman function ports the 'concaveman' (<https://github.com/mapbox/concaveman>) library from 'mapbox'. It computes the concave polygon(s) for one or several set of points.

Maintained by Joël Gombin. Last updated 3 years ago.

concave-hull spatial

66 stars 9.06 score 492 scripts 22 dependents

eblondel

ows4R:Interface to OGC Web-Services (OWS)

Provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service (WFS) for vector data, Web Coverage Service (WCS), Catalogue Service (CSW) for ISO/OGC metadata, Web Processing Service (WPS) for data processes, and associated standards such as the common web-service specification (OWS) and OGC Filter Encoding. Partial support is provided for the Web Map Service (WMS). The purpose is to add support for additional OGC service standards such as Web Coverage Processing Service (WCPS), the Sensor Observation Service (SOS), or even new standard services emerging such OGC API or SensorThings.

Maintained by Emmanuel Blondel. Last updated 2 months ago.

catalogue-service csw dataaccess fes geospatial iso ogc ows sdi spatial spatial-data standard webfeatureservice wfs

38 stars 9.03 score 99 scripts 5 dependents

bioc

Banksy:Spatial transcriptomic clustering

Banksy is an R package that incorporates spatial information to cluster cells in a feature space (e.g. gene expression). To incorporate spatial information, BANKSY computes the mean neighborhood expression and azimuthal Gabor filters that capture gene expression gradients. These features are combined with the cell's own expression to embed cells in a neighbor-augmented product space which can then be clustered, allowing for accurate and spatially-aware cell typing and tissue domain segmentation.

Maintained by Joseph Lee. Last updated 25 days ago.

clustering spatial singlecell geneexpression dimensionreduction clustering-algorithm single-cell-omics spatial-omics

90 stars 9.03 score 248 scripts

ropenspain

mapSpain:Administrative Boundaries of Spain

Administrative Boundaries of Spain at several levels (Autonomous Communities, Provinces, Municipalities) based on the 'GISCO' 'Eurostat' database <https://ec.europa.eu/eurostat/web/gisco> and 'CartoBase SIANE' from 'Instituto Geografico Nacional' <https://www.ign.es/>. It also provides a 'leaflet' plugin and the ability of downloading and processing static tiles.

Maintained by Diego Hernangómez. Last updated 11 days ago.

ropenspain tiles maps spatial municipalities spain gisco provinces ign administrative-boundaries ccaa static-tiles ggplot2 gis

42 stars 8.88 score 244 scripts 2 dependents

connordonegan

geostan:Bayesian Spatial Analysis

For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.

Maintained by Connor Donegan. Last updated 3 months ago.

bayesian bayesian-inference bayesian-statistics epidemiology modeling public-health rspatial spatial stan cpp

80 stars 8.80 score 46 scripts

bioc

Voyager:From geospatial to spatial omics

SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The Voyager package also implements plotting functions to plot SFE data and ESDA results.

Maintained by Lambda Moses. Last updated 3 months ago.

geneexpression spatial transcriptomics visualization bioconductor eda esda exploratory-data-analysis omics spatial-statistics spatial-transcriptomics

88 stars 8.71 score 173 scripts

bioc

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.

Maintained by Yuzhou Feng. Last updated 13 days ago.

biomedicalinformatics cellbiology spatial clustering dataimport immunooncology qualitycontrol singlecell software visualization

22 stars 8.59 score 69 scripts

bioc

sccomp:Tests differences in cell-type proportion for single-cell data, robust to outliers

A robust and outlier-aware method for testing differences in cell-type proportion in single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further.

Maintained by Stefano Mangiola. Last updated 14 days ago.

bayesian regression differentialexpression singlecell metagenomics flowcytometry spatial batch-correction composition cytof differential-proportion microbiome multilevel proportions random-effects single-cell unwanted-variation

99 stars 8.43 score 69 scripts

bioc

SPOTlight:`SPOTlight`: Spatial Transcriptomics Deconvolution

`SPOTlight`provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots).

Maintained by Marc Elosua-Bayes. Last updated 5 months ago.

singlecell spatial statisticalmethod

172 stars 8.37 score 170 scripts

eblondel

geometa:Tools for Reading and Writing ISO/OGC Geographic Metadata

Provides facilities to read, write and validate geographic metadata defined with ISO TC211 / OGC ISO geographic information metadata standards, and encoded using the ISO 19139 and ISO 19115-3 (XML) standard technical specifications. This includes ISO 19110 (Feature cataloguing), 19115 (dataset metadata), 19119 (service metadata) and 19136 (GML). Other interoperable schemas from the OGC are progressively supported as well, such as the Sensor Web Enablement (SWE) Common Data Model, the OGC GML Coverage Implementation Schema (GMLCOV), or the OGC GML Referenceable Grid (GMLRGRID).

Maintained by Emmanuel Blondel. Last updated 5 days ago.

geometa gml inspire iso iso19110 iso19115 iso19119 iso19136 iso19139 metadata metadata-validation ogc spatial xml

47 stars 8.16 score 109 scripts 7 dependents

symbolixau

googlePolylines:Encoding Coordinates into 'Google' Polylines

Encodes simple feature ('sf') objects and coordinates, and decodes polylines using the 'Google' polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).

Maintained by David Cooley. Last updated 16 days ago.

geospatial gis google-maps polyline-encoder r-spatial spatial cpp

18 stars 8.11 score 9 dependents

mlr-org

mlr3spatiotempcv:Spatiotemporal Resampling Methods for 'mlr3'

Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.

Maintained by Patrick Schratz. Last updated 4 months ago.

cross-validation mlr3 resampling resampling-methods spatial temporal

50 stars 8.09 score 123 scripts

bioc

spicyR:Spatial analysis of in situ cytometry data

The spicyR package provides a framework for performing inference on changes in spatial relationships between pairs of cell types for cell-resolution spatial omics technologies. spicyR consists of three primary steps: (i) summarizing the degree of spatial localization between pairs of cell types for each image; (ii) modelling the variability in localization summary statistics as a function of cell counts and (iii) testing for changes in spatial localizations associated with a response variable.

Maintained by Ellis Patrick. Last updated 25 days ago.

singlecell cellbasedassays spatial

9 stars 8.02 score 57 scripts 1 dependents

dieghernan

nominatimlite:Interface with 'Nominatim' API Service

Lite interface for getting data from 'OSM' service 'Nominatim' <https://nominatim.org/release-docs/latest/>. Extract coordinates from addresses, find places near a set of coordinates and return spatial objects on 'sf' format.

Maintained by Diego Hernangómez. Last updated 11 days ago.

geocoding openstreetmap address nominatim reverse-geocoding shapefile spatial api-wrapper api gis

20 stars 8.01 score 41 scripts 1 dependents

bioc

mistyR:Multiview Intercellular SpaTial modeling framework

mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution.

Maintained by Jovan Tanevski. Last updated 5 months ago.

software biomedicalinformatics cellbiology systemsbiology regression decisiontree singlecell spatial bioconductor biology intercellular machine-learning modular molecular-biology multiview spatial-transcriptomics

51 stars 7.87 score 160 scripts

ropensci

NLMR:Simulating Neutral Landscape Models

Provides neutral landscape models (<doi:10.1007/BF02275262>, <http://sci-hub.tw/10.1007/bf02275262>). Neutral landscape models range from "hard" neutral models (completely random distributed), to "soft" neutral models (definable spatial characteristics) and generate landscape patterns that are independent of ecological processes. Thus, these patterns can be used as null models in landscape ecology. 'NLMR' combines a large number of algorithms from other published software for simulating neutral landscapes. The simulation results are obtained in a spatial data format (raster* objects from the 'raster' package) and can, therefore, be used in any sort of raster data operation that is performed with standard observation data.

Maintained by Marco Sciaini. Last updated 7 months ago.

landscape-ecology neutral-landscape-model peer-reviewed spatial cpp

65 stars 7.74 score 193 scripts

jeremygelb

spNetwork:Spatial Analysis on Network

Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).

Maintained by Jeremy Gelb. Last updated 11 hours ago.

kernel kernel-density-estimation network network-analysis spatial spatial-analysis spatial-data-analysis cpp

38 stars 7.74 score 52 scripts

bioc

scDesign3:A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.

Maintained by Dongyuan Song. Last updated 27 days ago.

software singlecell sequencing geneexpression spatial

89 stars 7.59 score 25 scripts

bioc

ggsc:Visualizing Single Cell and Spatial Transcriptomics

Useful functions to visualize single cell and spatial data. It supports visualizing 'Seurat', 'SingleCellExperiment' and 'SpatialExperiment' objects through grammar of graphics syntax implemented in 'ggplot2'.

Maintained by Guangchuang Yu. Last updated 5 months ago.

dimensionreduction geneexpression singlecell software spatial transcriptomics visualization openblas cpp openmp

47 stars 7.59 score 18 scripts

bioc

imcRtools:Methods for imaging mass cytometry data analysis

This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected.

Maintained by Daniel Schulz. Last updated 5 months ago.

immunooncology singlecell spatial dataimport clustering imc single-cell

24 stars 7.58 score 126 scripts

bioc

nnSVG:Scalable identification of spatially variable genes in spatially-resolved transcriptomics data

Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.

Maintained by Lukas M. Weber. Last updated 1 months ago.

spatial singlecell transcriptomics geneexpression preprocessing

17 stars 7.57 score 183 scripts 1 dependents

nowosad

motif:Local Pattern Analysis

Describes spatial patterns of categorical raster data for any defined regular and irregular areas. Patterns are described quantitatively using built-in signatures based on co-occurrence matrices but also allows for any user-defined functions. It enables spatial analysis such as search, change detection, and clustering to be performed on spatial patterns (Nowosad (2021) <doi:10.1007/s10980-020-01135-0>).

Maintained by Jakub Nowosad. Last updated 7 months ago.

categorical-raster global-ecology landscape-ecology spatial cpp

63 stars 7.48 score 48 scripts

bioc

SpatialDecon:Deconvolution of mixed cells from spatial and/or bulk gene expression data

Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data.

Maintained by Maddy Griswold. Last updated 5 months ago.

immunooncology featureextraction geneexpression transcriptomics spatial

37 stars 7.41 score 58 scripts

bioc

standR:Spatial transcriptome analyses of Nanostring's DSP data in R

standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations.

Maintained by Ning Liu. Last updated 2 months ago.

spatial transcriptomics geneexpression differentialexpression qualitycontrol normalization experimenthubsoftware

18 stars 7.39 score 45 scripts

16eagle

basemaps:Accessing Spatial Basemaps in R

A lightweight package to access spatial basemaps from open sources such as 'OpenStreetMap', 'Carto', 'Mapbox' and others in R.

Maintained by Jakob Schwalb-Willmann. Last updated 4 months ago.

basemaps carto esri mapbox maptiler openstreetmap osm spatial stadia stamen thunderforest

60 stars 7.38 score 307 scripts

ropenspain

CatastRo:Interface to the API 'Sede Electronica Del Catastro'

Access public spatial data available under the 'INSPIRE' directive. Tools for downloading references and addresses of properties, as well as map images.

Maintained by Diego Hernangómez. Last updated 5 days ago.

catastro gis maps ropenspain spain spatial static-tiles

23 stars 7.22 score 14 scripts

ropensci

mapscanner:Print Maps, Draw on Them, Scan Them Back in

Enables preparation of maps to be printed and drawn on. Modified maps can then be scanned back in, and hand-drawn marks converted to spatial objects.

Maintained by Mark Padgham. Last updated 2 months ago.

maps scan spatial cpp

90 stars 7.13 score 2 scripts

bioc

GeomxTools:NanoString GeoMx Tools

Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included.

Maintained by Maddy Griswold. Last updated 5 months ago.

geneexpression transcription cellbasedassays dataimport transcriptomics proteomics mrnamicroarray proprietaryplatforms rnaseq sequencing experimentaldesign normalization spatial

7.11 score 239 scripts 3 dependents

josiahparry

sfdep:Spatial Dependence for Simple Features

An interface to 'spdep' to integrate with 'sf' objects and the 'tidyverse'.

Maintained by Dexter Locke. Last updated 7 months ago.

r-spatial spatial

130 stars 7.01 score 130 scripts

jlacko

gmapsdistance:Distance and Travel Time Between Two Points from Google Maps

Get distance and travel time between two points from Google Maps. Four possible modes of transportation (bicycling, walking, driving and public transportation).

Maintained by Jindra Lacko. Last updated 19 days ago.

spatial

2 stars 6.98 score 89 scripts

nowosad

sabre:Spatial Association Between Regionalizations

Calculates a degree of spatial association between regionalizations or categorical maps using the information-theoretical V-measure (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also offers an R implementation of the MapCurve method (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).

Maintained by Jakub Nowosad. Last updated 4 months ago.

entropy polygons regionalizations spatial spatial-analysis

36 stars 6.95 score 25 scripts

ropensci

waywiser:Ergonomic Methods for Assessing Spatial Models

Assessing predictive models of spatial data can be challenging, both because these models are typically built for extrapolating outside the original region represented by training data and due to potential spatially structured errors, with "hot spots" of higher than expected error clustered geographically due to spatial structure in the underlying data. Methods are provided for assessing models fit to spatial data, including approaches for measuring the spatial structure of model errors, assessing model predictions at multiple spatial scales, and evaluating where predictions can be made safely. Methods are particularly useful for models fit using the 'tidymodels' framework. Methods include Moran's I ('Moran' (1950) <doi:10.2307/2332142>), Geary's C ('Geary' (1954) <doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995) <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from 'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>) and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an implementation of the area of applicability methodology from 'Meyer' and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an implementation of multi-scale assessment as described in 'Riemann' 'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).

Maintained by Michael Mahoney. Last updated 12 days ago.

spatial spatial-analysis tidymodels tidyverse

37 stars 6.93 score 19 scripts

eblondel

cleangeo:Cleaning Geometries from Spatial Objects

Provides a set of utility tools to inspect spatial objects, facilitate handling and reporting of topology errors and geometry validity issue with sp objects. Finally, it provides a geometry cleaner that will fix all geometry problems, and eliminate (at least reduce) the likelihood of having issues when doing spatial data processing.

Maintained by Emmanuel Blondel. Last updated 2 years ago.

cleaning cleaning-geometries gis sp spatial

45 stars 6.82 score 99 scripts 1 dependents

bioc

ggspavis:Visualization functions for spatial transcriptomics data

Visualization functions for spatial transcriptomics data. Includes functions to generate several types of plots, including spot plots, feature (molecule) plots, reduced dimension plots, spot-level quality control (QC) plots, and feature-level QC plots, for datasets from the 10x Genomics Visium and other technological platforms. Datasets are assumed to be in either SpatialExperiment or SingleCellExperiment format.

Maintained by Lukas M. Weber. Last updated 5 months ago.

spatial singlecell transcriptomics geneexpression qualitycontrol dimensionreduction

3 stars 6.80 score 264 scripts

mlr-org

mlr3spatial:Support for Spatial Objects Within the 'mlr3' Ecosystem

Extends the 'mlr3' ML framework with methods for spatial objects. Data storage and prediction are supported for packages 'terra', 'raster' and 'stars'.

Maintained by Marc Becker. Last updated 1 years ago.

mlr3 raster-prediction spatial spatial-modelling

43 stars 6.75 score 66 scripts

bioc

escheR:Unified multi-dimensional visualizations with Gestalt principles

The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide this open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows.

Maintained by Boyi Guo. Last updated 5 months ago.

spatial singlecell transcriptomics visualization software multidimensional single-cell spatial-omics

6 stars 6.74 score 153 scripts 1 dependents

bioc

SpotSweeper:Spatially-aware quality control for spatial transcriptomics

Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts.

Maintained by Michael Totty. Last updated 3 months ago.

software spatial transcriptomics qualitycontrol geneexpression bioconductor quality-control spatial-transcriptomics

5 stars 6.66 score 77 scripts

bioc

lisaClust:lisaClust: Clustering of Local Indicators of Spatial Association

lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution.

Maintained by Ellis Patrick. Last updated 4 months ago.

singlecell cellbasedassays spatial

3 stars 6.64 score 48 scripts

crazycapivara

mapboxer:An R Interface to 'Mapbox GL JS'

Makes 'Mapbox GL JS' <https://docs.mapbox.com/mapbox-gl-js/api/>, an open source JavaScript library that uses WebGL to render interactive maps, available within R via the 'htmlwidgets' package. Visualizations can be used from the R console, in R Markdown documents and in Shiny apps.

Maintained by Stefan Kuethe. Last updated 3 years ago.

htmlwidgets mapbox-gl-js spatial webgl

55 stars 6.61 score 49 scripts 1 dependents

hemingnm

SESraster:Raster Randomization for Null Hypothesis Testing

Randomization of presence/absence species distribution raster data with or without including spatial structure for calculating standardized effect sizes and testing null hypothesis. The randomization algorithms are based on classical algorithms for matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for raster data.

Maintained by Neander Marcel Heming. Last updated 5 months ago.

null-models randomization raster spatial spatial-analysis species-distribution-modelling

7 stars 6.61 score 32 scripts 2 dependents

bioc

SpaceMarkers:Spatial Interaction Markers

Spatial transcriptomic technologies have helped to resolve the connection between gene expression and the 2D orientation of tissues relative to each other. However, the limited single-cell resolution makes it difficult to highlight the most important molecular interactions in these tissues. SpaceMarkers, R/Bioconductor software, can help to find molecular interactions, by identifying genes associated with latent space interactions in spatial transcriptomics.

Maintained by Atul Deshpande. Last updated 12 days ago.

singlecell geneexpression software spatial transcriptomics

5 stars 6.55 score 21 scripts

bioc

SpotClean:SpotClean adjusts for spot swapping in spatial transcriptomics data

SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses.

Maintained by Zijian Ni. Last updated 5 months ago.

dataimport rnaseq sequencing geneexpression spatial singlecell transcriptomics preprocessing rna-seq spatial-transcriptomics

31 stars 6.52 score 36 scripts

bioc

CatsCradle:This package provides methods for analysing spatial transcriptomics data and for discovering gene clusters

This package addresses two broad areas. It allows for in-depth analysis of spatial transcriptomic data by identifying tissue neighbourhoods. These are contiguous regions of tissue surrounding individual cells. 'CatsCradle' allows for the categorisation of neighbourhoods by the cell types contained in them and the genes expressed in them. In particular, it produces Seurat objects whose individual elements are neighbourhoods rather than cells. In addition, it enables the categorisation and annotation of genes by producing Seurat objects whose elements are genes.

Maintained by Michael Shapiro. Last updated 12 days ago.

biologicalquestion statisticalmethod geneexpression singlecell transcriptomics spatial

3 stars 6.52 score

bioc

Statial:A package to identify changes in cell state relative to spatial associations

Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with.

Maintained by Farhan Ameen. Last updated 5 months ago.

singlecell spatial classification single-cell

5 stars 6.49 score 23 scripts

bioc

MoleculeExperiment:Prioritising a molecule-level storage of Spatial Transcriptomics Data

MoleculeExperiment contains functions to create and work with objects from the new MoleculeExperiment class. We introduce this class for analysing molecule-based spatial transcriptomics data (e.g., Xenium by 10X, Cosmx SMI by Nanostring, and Merscope by Vizgen). This allows researchers to analyse spatial transcriptomics data at the molecule level, and to have standardised data formats accross vendors.

Maintained by Shila Ghazanfar. Last updated 5 months ago.

dataimport datarepresentation infrastructure software spatial transcriptomics

12 stars 6.45 score 39 scripts

crazycapivara

deckgl:An R Interface to 'deck.gl'

Makes 'deck.gl' <https://deck.gl/>, a WebGL-powered open-source JavaScript framework for visual exploratory data analysis of large datasets, available within R via the 'htmlwidgets' package. Furthermore, it supports basemaps from 'mapbox' <https://www.mapbox.com/> via 'mapbox-gl-js' <https://github.com/mapbox/mapbox-gl-js>.

Maintained by Stefan Kuethe. Last updated 2 years ago.

deck-gl htmlwidgets mapbox-gl maps rspatial spatial webgl

94 stars 6.40 score 54 scripts

nowosad

supercells:Superpixels of Spatial Data

Creates superpixels based on input spatial data. This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters). It is based on the SLIC algorithm (Achanta et al. (2012) <doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary dissimilarity measures.

Maintained by Jakub Nowosad. Last updated 7 months ago.

rspatial spatial cpp

68 stars 6.33 score 52 scripts

ksawicka

spup:Spatial Uncertainty Propagation Analysis

Uncertainty propagation analysis in spatial environmental modelling following methodology described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model outputs. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for. The MC realizations may be used as input to the environmental models called from R, or externally.

Maintained by Kasia Sawicka. Last updated 1 years ago.

monte-carlo spatial uncertainty-analysis uncertainty-propagation

9 stars 6.31 score 57 scripts

nowosad

comat:Creates Co-Occurrence Matrices of Spatial Data

Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>).

Maintained by Jakub Nowosad. Last updated 1 years ago.

co-occurrence raster spatial cpp

6 stars 6.31 score 25 scripts 3 dependents

bioc

signifinder:Collection and implementation of public transcriptional cancer signatures

signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows to compute a variety of signatures, based on gene expression values, and return single-sample scores. Currently, signifinder contains more than 60 distinct signatures collected from the literature, relating to multiple tumors and multiple cancer processes.

Maintained by Stefania Pirrotta. Last updated 3 months ago.

geneexpression genetarget immunooncology biomedicalinformatics rnaseq microarray reportwriting visualization singlecell spatial genesignaling

7 stars 6.28 score 15 scripts

bioc

spatialHeatmap:spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Large-Scale Data Extensions

The spatialHeatmap package offers the primary functionality for visualizing cell-, tissue- and organ-specific assay data in spatial anatomical images. Additionally, it provides extended functionalities for large-scale data mining routines and co-visualizing bulk and single-cell data. A description of the project is available here: https://spatialheatmap.org.

Maintained by Jianhai Zhang. Last updated 4 months ago.

spatial visualization microarray sequencing geneexpression datarepresentation network clustering graphandnetwork cellbasedassays atacseq dnaseq tissuemicroarray singlecell cellbiology genetarget

5 stars 6.26 score 12 scripts

bioc

concordexR:Identify Spatial Homogeneous Regions with concordex

Spatial homogeneous regions (SHRs) in tissues are domains that are homogenous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. concordex relies on analysis of k-nearest-neighbor (kNN) graphs. The tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs.

Maintained by Kayla Jackson. Last updated 2 months ago.

singlecell clustering spatial transcriptomics

13 stars 6.23 score 13 scripts

eblondel

geosapi:GeoServer REST API R Interface

Provides an R interface to the GeoServer REST API, allowing to upload and publish data in a GeoServer web-application and expose data to OGC Web-Services. The package currently supports all CRUD (Create,Read,Update,Delete) operations on GeoServer workspaces, namespaces, datastores (stores of vector data), featuretypes, layers, styles, as well as vector data upload operations. For more information about the GeoServer REST API, see <https://docs.geoserver.org/stable/en/user/rest/>.

Maintained by Emmanuel Blondel. Last updated 28 days ago.

api geoserver gis publication rest spatial

34 stars 6.23 score 33 scripts

nowosad

spDataLarge:Large datasets for spatial analysis

Large datasets for spatial analysis. The data from this package could be retrived using the spData package.

Maintained by Jakub Nowosad. Last updated 6 months ago.

raster sf spatial spatial-data

27 stars 6.15 score 1.2k scripts 1 dependents

brownag

rgeedim:Search, Composite, and Download 'Google Earth Engine' Imagery with the 'Python' Module 'geedim'

Search, composite, and download 'Google Earth Engine' imagery with 'reticulate' bindings for the 'Python' module 'geedim' by Dugal Harris. Read the 'geedim' documentation here: <https://geedim.readthedocs.io/>. Wrapper functions are provided to make it more convenient to use 'geedim' to download images larger than the 'Google Earth Engine' size limit <https://developers.google.com/earth-engine/apidocs/ee-image-getdownloadurl>. By default the "High Volume" API endpoint <https://developers.google.com/earth-engine/cloud/highvolume> is used to download data and this URL can be customized during initialization of the package.

Maintained by Andrew Brown. Last updated 7 days ago.

geedim geotiff gis google-earth-engine python raster remote-sensing satellite-imagery spatial terra

50 stars 6.13 score 27 scripts

mkln

meshed:Bayesian Regression with Meshed Gaussian Processes

Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>, Peruzzi and Dunson (2024) <arXiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.

Maintained by Michele Peruzzi. Last updated 8 months ago.

bayesian mcmc multivariate regression spatial spatiotemporal openblas cpp openmp

13 stars 6.11 score 49 scripts

geocompx

geocompkg:Geocomputation with R Metapackage

Package supporting the book Geocomputation with R (\url{https://r.geocompx.org}). The packages in the Imports are required to build the first chapter of the book. The packages in Suggests are required for Part II and III.

Maintained by Jakub Nowosad. Last updated 6 months ago.

geocomputation spatial

21 stars 6.10 score 2 scripts

bioc

knowYourCG:Functional analysis of DNA methylome datasets

KnowYourCG (KYCG) is a supervised learning framework designed for the functional analysis of DNA methylation data. Unlike existing tools that focus on genes or genomic intervals, KnowYourCG directly targets CpG dinucleotides, featuring automated supervised screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait-epigenome associations. KnowYourCG addresses the challenges of data sparsity in various methylation datasets, including low-pass Nanopore sequencing, single-cell DNA methylomes, 5-hydroxymethylation profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies and epigenetic clocks.

Maintained by Goldberg David. Last updated 3 months ago.

epigenetics dnamethylation sequencing singlecell spatial methylationarray zlib

2 stars 6.10 score 4 scripts

bioc

simpleSeg:A package to perform simple cell segmentation

Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images.

Maintained by Ellis Patrick. Last updated 5 months ago.

classification survival singlecell normalization spatial spatial-statistics

5.96 score 19 scripts 2 dependents

bafuentes

rassta:Raster-Based Spatial Stratification Algorithms

Algorithms for the spatial stratification of landscapes, sampling and modeling of spatially-varying phenomena. These algorithms offer a simple framework for the stratification of geographic space based on raster layers representing landscape factors and/or factor scales. The stratification process follows a hierarchical approach, which is based on first level units (i.e., classification units) and second-level units (i.e., stratification units). Nonparametric techniques allow to measure the correspondence between the geographic space and the landscape configuration represented by the units. These correspondence metrics are useful to define sampling schemes and to model the spatial variability of environmental phenomena. The theoretical background of the algorithms and code examples are presented in Fuentes, Dorantes, and Tipton (2021). <doi:10.31223/X50S57>.

Maintained by Bryan A. Fuentes. Last updated 3 years ago.

ecology geoinformatics hierarchical modeling sampling spatial

16 stars 5.96 score 19 scripts

bioc

scFeatures:scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

scFeatures constructs multi-view representations of single-cell and spatial data. scFeatures is a tool that generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types. These features can then be used for a variety of analyses using other software in Biocondutor.

Maintained by Yue Cao. Last updated 5 months ago.

cellbasedassays singlecell spatial software transcriptomics

10 stars 5.95 score 15 scripts

bioc

SpaNorm:Spatially-aware normalisation for spatial transcriptomics data

This package implements the spatially aware library size normalisation algorithm, SpaNorm. SpaNorm normalises out library size effects while retaining biology through the modelling of smooth functions for each effect. Normalisation is performed in a gene- and cell-/spot- specific manner, yielding library size adjusted data.

Maintained by Dharmesh D. Bhuva. Last updated 5 months ago.

software geneexpression transcriptomics spatial cellbiology

9 stars 5.95 score 3 scripts

jancaha

SpatialKDE:Kernel Density Estimation for Spatial Data

Calculate Kernel Density Estimation (KDE) for spatial data. The algorithm is inspired by the tool 'Heatmap' from 'QGIS'. The method is described by: Hart, T., Zandbergen, P. (2014) <doi:10.1108/PIJPSM-04-2013-0039>, Nelson, T. A., Boots, B. (2008) <doi:10.1111/j.0906-7590.2008.05548.x>, Chainey, S., Tompson, L., Uhlig, S.(2008) <doi:10.1057/palgrave.sj.8350066>.

Maintained by Jan Caha. Last updated 2 years ago.

kde qgis spatial cpp

9 stars 5.95 score 33 scripts 1 dependents

pepijn-devries

CopernicusMarine:Search Download and Handle Data from Copernicus Marine Service Information

Subset and download data from EU Copernicus Marine Service Information: <https://data.marine.copernicus.eu>. Import data on the oceans physical and biogeochemical state from Copernicus into R without the need of external software.

Maintained by Pepijn de Vries. Last updated 3 months ago.

data spatial

29 stars 5.94 score 20 scripts 2 dependents

doserjef

rFIA:Estimation of Forest Variables using the FIA Database

The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.

Maintained by Jeffrey Doser. Last updated 22 days ago.

compute-estimates fia fia-database fia-datamart forest-inventory forest-variables inventories space-time spatial

49 stars 5.93 score

bioc

tidySpatialExperiment:SpatialExperiment with tidy principles

tidySpatialExperiment provides a bridge between the SpatialExperiment package and the tidyverse ecosystem. It creates an invisible layer that allows you to interact with a SpatialExperiment object as if it were a tibble; enabling the use of functions from dplyr, tidyr, ggplot2 and plotly. But, underneath, your data remains a SpatialExperiment object.

Maintained by William Hutchison. Last updated 5 months ago.

infrastructure rnaseq geneexpression sequencing spatial transcriptomics singlecell

6 stars 5.88 score 12 scripts

bioc

miRspongeR:Identification and analysis of miRNA sponge regulation

This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network.

Maintained by Junpeng Zhang. Last updated 5 months ago.

geneexpression biomedicalinformatics networkenrichment survival microarray software singlecell spatial rnaseq cerna mirna sponge

5 stars 5.88 score 8 scripts

bioc

SpatialExperimentIO:Read in Xenium, CosMx, MERSCOPE or STARmapPLUS data as SpatialExperiment object

Read in imaging-based spatial transcriptomics technology data. Current available modules are for Xenium by 10X Genomics, CosMx by Nanostring, MERSCOPE by Vizgen, or STARmapPLUS from Broad Institute. You can choose to read the data in as a SpatialExperiment or a SingleCellExperiment object.

Maintained by Yixing E. Dong. Last updated 2 months ago.

datarepresentation dataimport infrastructure transcriptomics singlecell spatial geneexpression

9 stars 5.81 score 16 scripts

bioc

HuBMAPR:Interface to 'HuBMAP'

'HuBMAP' provides an open, global bio-molecular atlas of the human body at the cellular level. The `datasets()`, `samples()`, `donors()`, `publications()`, and `collections()` functions retrieves the information for each of these entity types. `*_details()` are available for individual entries of each entity type. `*_derived()` are available for retrieving derived datasets or samples for individual entries of each entity type. Data files can be accessed using `bulk_data_transfer()`.

Maintained by Christine Hou. Last updated 2 months ago.

software singlecell dataimport thirdpartyclient spatial infrastructure bioconductor-package client hubmap rstudio

3 stars 5.80 score 1 scripts

r-spatialecology

belg:Boltzmann Entropy of a Landscape Gradient

Calculates the Boltzmann entropy of a landscape gradient. This package uses the analytical method created by Gao, P., Zhang, H. and Li, Z., 2018 (<doi:10.1111/tgis.12315>) and by Gao, P. and Li, Z., 2019 (<doi:10.1007/s10980-019-00854-3>). It also extend the original ideas by allowing calculations on data with missing values.

Maintained by Jakub Nowosad. Last updated 2 years ago.

entropy landscape raster spatial cpp

19 stars 5.80 score 11 scripts 1 dependents

brownag

gpkg:Utilities for the Open Geospatial Consortium 'GeoPackage' Format

Build Open Geospatial Consortium 'GeoPackage' files (<https://www.geopackage.org/>). 'GDAL' utilities for reading and writing spatial data are provided by the 'terra' package. Additional 'GeoPackage' and 'SQLite' features for attributes and tabular data are implemented with the 'RSQLite' package.

Maintained by Andrew Brown. Last updated 13 days ago.

dbi geopackage geospatial gis raster spatial sqlite vector

21 stars 5.72 score

bioc

sosta:A package for the analysis of anatomical tissue structures in spatial omics data

sosta (Spatial Omics STructure Analysis) is a package for analyzing spatial omics data to explore tissue organization at the anatomical structure level. It reconstructs morphologically relevant structures based on molecular features or cell types. It further calculates a range of structural and shape metrics to quantitatively describe tissue architecture. The package is designed to integrate with other packages for the analysis of spatial (omics) data.

Maintained by Samuel Gunz. Last updated 8 days ago.

software spatial transcriptomics visualization

1 stars 5.68 score 2 scripts 1 dependents

egpivo

SpatPCA:Regularized Principal Component Analysis for Spatial Data

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Maintained by Wen-Ting Wang. Last updated 7 months ago.

admm covariance-estimation eigenfunctions lasso matrix-factorization pca rcpparmadillo rcppparallel regularization spatial spatial-data-analysis splines openblas cpp openmp

20 stars 5.53 score 17 scripts

cpanse

recmap:Compute the Rectangular Statistical Cartogram

Implements the RecMap MP2 construction heuristic <doi:10.1109/INFVIS.2004.57>. This algorithm draws maps according to a given statistical value, e.g., election results, population, or epidemiological data. The basic idea of the RecMap algorithm is that each map region, e.g., different countries, is represented by a rectangle. The area of each rectangle represents the statistical value given as input (maintain zero cartographic error). C++ is used to implement the computationally intensive tasks. The vignette included in this package provides documentation about the usage of the recmap algorithm.

Maintained by Christian Panse. Last updated 3 months ago.

cartogram demographics genetic-algorithm geovisualization graph-drawing spatial spatial-data-analysis value-by-area-maps cpp

21 stars 5.51 score 31 scripts

bioc

cytoviewer:An interactive multi-channel image viewer for R

This R package supports interactive visualization of multi-channel images and segmentation masks generated by imaging mass cytometry and other highly multiplexed imaging techniques using shiny. The cytoviewer interface is divided into image-level (Composite and Channels) and cell-level visualization (Masks). It allows users to overlay individual images with segmentation masks, integrates well with SingleCellExperiment and SpatialExperiment objects for metadata visualization and supports image downloads.

Maintained by Lasse Meyer. Last updated 5 months ago.

immunooncology software singlecell onechannel twochannel multichannel spatial dataimport bioconductor imaging shiny visualization

7 stars 5.50 score 15 scripts

bioc

VisiumIO:Import Visium data from the 10X Space Ranger pipeline

The package allows users to readily import spatial data obtained from either the 10X website or from the Space Ranger pipeline. Supported formats include tar.gz, h5, and mtx files. Multiple files can be imported at once with *List type of functions. The package represents data mainly as SpatialExperiment objects.

Maintained by Marcel Ramos. Last updated 2 months ago.

software infrastructure dataimport singlecell spatial bioconductor-package genomics u24ca289073

5.50 score 14 scripts 1 dependents

cidm-ph

cartographer:Turn Place Names into Map Data

A tool for easily matching spatial data when you have a list of place/region names. You might have a data frame that came from a spreadsheet tracking some data by suburb or state. This package can convert it into a spatial data frame ready for plotting. The actual map data is provided by other packages (or your own code).

Maintained by Carl Suster. Last updated 1 years ago.

spatial

3 stars 5.43 score 15 scripts 2 dependents

riatelab

tanaka:Design Shaded Contour Lines (or Tanaka) Maps

The Tanaka method enhances the representation of topography on a map using shaded contour lines. In this simplified implementation of the method, north-west white contours represent illuminated topography and south-east black contours represent shaded topography. See Tanaka (1950) <doi:10.2307/211219>.

Maintained by Timothée Giraud. Last updated 1 years ago.

map spatial tanaka

79 stars 5.40 score 21 scripts 1 dependents

bioc

visiumStitched:Enable downstream analysis of Visium capture areas stitched together with Fiji

This package provides helper functions for working with multiple Visium capture areas that overlap each other. This package was developed along with the companion example use case data available from https://github.com/LieberInstitute/visiumStitched_brain. visiumStitched prepares SpaceRanger (10x Genomics) output files so you can stitch the images from groups of capture areas together with Fiji. Then visiumStitched builds a SpatialExperiment object with the stitched data and makes an artificial hexogonal grid enabling the seamless use of spatial clustering methods that rely on such grid to identify neighboring spots, such as PRECAST and BayesSpace. The SpatialExperiment objects created by visiumStitched are compatible with spatialLIBD, which can be used to build interactive websites for stitched SpatialExperiment objects. visiumStitched also enables casting SpatialExperiment objects as Seurat objects.

Maintained by Nicholas J. Eagles. Last updated 4 months ago.

software spatial transcriptomics transcription geneexpression visualization dataimport 10xgenomics bioconductor spatial-transcriptomics spatialexperiment spatiallibd visium

1 stars 5.36 score 4 scripts

bioc

hoodscanR:Spatial cellular neighbourhood scanning in R

hoodscanR is an user-friendly R package providing functions to assist cellular neighborhood analysis of any spatial transcriptomics data with single-cell resolution. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. The package can result in cell-level neighborhood annotation output, along with funtions to perform neighborhood colocalization analysis and neighborhood-based cell clustering.

Maintained by Ning Liu. Last updated 2 months ago.

spatial transcriptomics singlecell clustering cpp

4 stars 5.32 score 15 scripts

pedersen-fisheries-lab

sspm:Spatial Surplus Production Model Framework for Northern Shrimp Populations

Implement a GAM-based (Generalized Additive Models) spatial surplus production model (spatial SPM), aimed at modeling northern shrimp population in Atlantic Canada but potentially to any stock in any location. The package is opinionated in its implementation of SPMs as it internally makes the choice to use penalized spatial gams with time lags. However, it also aims to provide options for the user to customize their model. The methods are described in Pedersen et al. (2022, <https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html>).

Maintained by Valentin Lucet. Last updated 2 months ago.

gam model spatial surplus

3 stars 5.28 score 21 scripts

bioc

BulkSignalR:Infer Ligand-Receptor Interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics

Inference of ligand-receptor (LR) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics. BulkSignalR bases its inferences on the LRdb database included in our other package, SingleCellSignalR available from Bioconductor. It relies on a statistical model that is specific to bulk data sets. Different visualization and data summary functions are proposed to help navigating prediction results.

Maintained by Jean-Philippe Villemin. Last updated 3 months ago.

network rnaseq software proteomics transcriptomics networkinference spatial

5.22 score 15 scripts

nowosad

raceland:Pattern-Based Zoneless Method for Analysis and Visualization of Racial Topography

Implements a computational framework for a pattern-based, zoneless analysis, and visualization of (ethno)racial topography (Dmowska, Stepinski, and Nowosad (2020) <doi:10.1016/j.apgeog.2020.102239>). It is a reimagined approach for analyzing residential segregation and racial diversity based on the concept of 'landscape’ used in the domain of landscape ecology.

Maintained by Jakub Nowosad. Last updated 2 years ago.

information-theory landscape racial-diversity raster residential-segregation spatial cpp

9 stars 5.21 score 12 scripts

bioc

spatialFDA:A Tool for Spatial Multi-sample Comparisons

spatialFDA is a package to calculate spatial statistics metrics. The package takes a SpatialExperiment object and calculates spatial statistics metrics using the package spatstat. Then it compares the resulting functions across samples/conditions using functional additive models as implemented in the package refund. Furthermore, it provides exploratory visualisations using functional principal component analysis, as well implemented in refund.

Maintained by Martin Emons. Last updated 1 months ago.

software spatial transcriptomics

3 stars 5.18 score 6 scripts

hautaniemilab

jellyfisher:Visualize Spatiotemporal Tumor Evolution with Jellyfish Plots

Generates interactive Jellyfish plots to visualize spatiotemporal tumor evolution by integrating sample and phylogenetic trees into a unified plot. This approach provides an intuitive way to analyze tumor heterogeneity and evolution over time and across anatomical locations. The Jellyfish plot visualization design was first introduced by Lahtinen, Lavikka, et al. (2023, <doi:10.1016/j.ccell.2023.04.017>). This package also supports visualizing ClonEvol results, a tool developed by Dang, et al. (2017, <doi:10.1093/annonc/mdx517>), for analyzing clonal evolution from multi-sample sequencing data. The 'clonevol' package is not available on CRAN but can be installed from its GitHub repository (<https://github.com/hdng/clonevol>).

Maintained by Kari Lavikka. Last updated 1 months ago.

visualization phylogenetics software spatial bioinformatics phylogenetic-analysis tumor-evolution tumor-heterogeneity visualization-tool

3 stars 5.18 score 2 scripts

bioc

spaSim:Spatial point data simulator for tissue images

A suite of functions for simulating spatial patterns of cells in tissue images. Output images are multitype point data in SingleCellExperiment format. Each point represents a cell, with its 2D locations and cell type. Potential cell patterns include background cells, tumour/immune cell clusters, immune rings, and blood/lymphatic vessels.

Maintained by Yuzhou Feng. Last updated 5 months ago.

statisticalmethod spatial biomedicalinformatics

2 stars 5.18 score 25 scripts

aestears

plantTracker:Extract Demographic and Competition Data from Fine-Scale Maps

Extracts growth, survival, and local neighborhood density information from repeated, fine-scale maps of organism occurrence. Further information about this package can be found in our journal article, "plantTracker: An R package to translate maps of plant occurrence into demographic data" published in 2022 in Methods in Ecology and Evolution (Stears, et al., 2022) <doi:10.1111/2041-210X.13950>.

Maintained by Alice Stears. Last updated 2 years ago.

demography spatial

7 stars 5.12 score 19 scripts

bioc

smoppix:Analyze Single Molecule Spatial Omics Data Using the Probabilistic Index

Test for univariate and bivariate spatial patterns in spatial omics data with single-molecule resolution. The tests implemented allow for analysis of nested designs and are automatically calibrated to different biological specimens. Tests for aggregation, colocalization, gradients and vicinity to cell edge or centroid are provided.

Maintained by Stijn Hawinkel. Last updated 1 months ago.

transcriptomics spatial singlecell cpp

1 stars 5.10 score 4 scripts

bioc

retrofit:RETROFIT: Reference-free deconvolution of cell mixtures in spatial transcriptomics

RETROFIT is a Bayesian non-negative matrix factorization framework to decompose cell type mixtures in ST data without using external single-cell expression references. RETROFIT outperforms existing reference-based methods in estimating cell type proportions and reconstructing gene expressions in simulations with varying spot size and sample heterogeneity, irrespective of the quality or availability of the single-cell reference. RETROFIT recapitulates known cell-type localization patterns in a Slide-seq dataset of mouse cerebellum without using any single-cell data.

Maintained by Adam Park. Last updated 5 months ago.

transcriptomics visualization rnaseq bayesian spatial software geneexpression dimensionreduction featureextraction singlecell cpp

3 stars 5.08 score 9 scripts

bioc

scider:Spatial cell-type inter-correlation by density in R

scider is a user-friendly R package providing functions to model the global density of cells in a slide of spatial transcriptomics data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. After modelling density, the package allows for serveral downstream analysis, including colocalization analysis, boundary detection analysis and differential density analysis.

Maintained by Yunshun Chen. Last updated 5 months ago.

spatial transcriptomics

3 stars 5.08 score 3 scripts

martinschobben

oceanexplorer:Explore Our Planet's Oceans with NOAA

Provides tools for easy exploration of the world ocean atlas of the US agency National Oceanic and Atmospheric Administration (NOAA). It includes functions to extract NetCDF data from the repository and code to visualize several physical and chemical parameters of the ocean. A Shiny app further allows interactive exploration of the data. The methods for data collecting and quality checks are described in several papers, which can be found here: <https://www.ncei.noaa.gov/products/world-ocean-atlas>.

Maintained by Martin Schobben. Last updated 1 years ago.

earth earth-observation earth-science noaa noaa-data ocean ocean-sciences oceanography open-data shiny spatial spatial-analysis spatial-data

9 stars 5.01 score 23 scripts

bioc

jazzPanda:Finding spatially relevant marker genes in image based spatial transcriptomics data

This package contains the function to find marker genes for image-based spatial transcriptomics data. There are functions to create spatial vectors from the cell and transcript coordiantes, which are passed as inputs to find marker genes. Marker genes are detected for every cluster by two approaches. The first approach is by permtuation testing, which is implmented in parallel for finding marker genes for one sample study. The other approach is to build a linear model for every gene. This approach can account for multiple samples and backgound noise.

Maintained by Melody Jin. Last updated 27 days ago.

spatial geneexpression differentialexpression statisticalmethod transcriptomics correlation linear-models marker-genes spatial-transcriptomics

2 stars 5.00 score

bioc

OSTA.data:OSTA book data

'OSTA.data' is a companion package for the "Orchestrating Spatial Transcriptomics Analysis" (OSTA) with Bioconductor online book. Throughout OSTA, we rely on a set of publicly available datasets that cover different sequencing- and imaging-based platforms, such as Visium, Visium HD, Xenium (10x Genomics) and CosMx (NanoString). In addition, we rely on scRNA-seq (Chromium) data for tasks, e.g., spot deconvolution and label transfer (i.e., supervised clustering). These data been deposited in an Open Storage Framework (OSF) repository, and can be queried and downloaded using functions from the 'osfr' package. For convenience, we have implemented 'OSTA.data' to query and retrieve data from our OSF node, and cache retrieved Zip archives using 'BiocFileCache'.

Maintained by Yixing E. Dong. Last updated 1 months ago.

dataimport datarepresentation experimenthubsoftware infrastructure immunooncology geneexpression transcriptomics singlecell spatial

2 stars 5.00 score

tomroh

bcputility:Wrapper for SQL Server bcp Utility

Provides functions to utilize a command line utility that does bulk inserts and exports from SQL Server databases.

Maintained by Thomas Roh. Last updated 8 months ago.

database spatial sql sqlserver

14 stars 4.99 score 28 scripts

mikejohnson51

AOI:Areas of Interest

A consistent tool kit for forward and reverse geocoding and defining boundaries for spatial analysis.

Maintained by Mike Johnson. Last updated 1 years ago.

aoi area-of-interest bounding-boxes gis spatial subset

37 stars 4.98 score 174 scripts 1 dependents

mpadge

spatialcluster:R port of redcap

R port of redcap (Regionalization with dynamically constrained agglomerative clustering and partitioning).

Maintained by Mark Padgham. Last updated 2 months ago.

cluster clustering-algorithm spatial cpp

31 stars 4.97 score 1 scripts

riatelab

linemap:Line Maps

Create maps made of lines. The package contains one function: linemap(). linemap() displays a map made of lines using a raster or gridded data.

Maintained by Timothée Giraud. Last updated 1 years ago.

cp1919 map spatial

116 stars 4.94 score 15 scripts

bioc

scatterHatch:Creates hatched patterns for scatterplots

The objective of this package is to efficiently create scatterplots where groups can be distinguished by color and texture. Visualizations in computational biology tend to have many groups making it difficult to distinguish between groups solely on color. Thus, this package is useful for increasing the accessibility of scatterplot visualizations to those with visual impairments such as color blindness.

Maintained by Atul Deshpande. Last updated 5 months ago.

visualization singlecell cellbiology software spatial

7 stars 4.89 score 11 scripts

robitalec

distanceto:Calculate Distance to Features

Calculates distances from point locations to features. The usual approach for eg. resource selection function analyses is to generate a complete distance to features surface then sample it with your observed and random points. Since these raster based approaches can be pretty costly with large areas, and often lead to memory issues in R, the distanceto package opts to compute these distances using efficient, vector based approaches. As a helper, there's a decidedly low-res raster based approach for visually inspecting your region's distance surface. But the workhorse is distance_to.

Maintained by Alec L. Robitaille. Last updated 2 years ago.

animal distance-to ecology resource-selection rsf spatial

5 stars 4.88 score 10 scripts 1 dependents

levisc8

spind:Spatial Methods and Indices

Functions for spatial methods based on generalized estimating equations (GEE) and wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR), conducting multi-model inference, and stepwise model selection. Further, contains functions for spatially corrected model accuracy measures.

Maintained by Sam Levin. Last updated 1 years ago.

models r-spatial spatial

3 stars 4.84 score 46 scripts

program--

fipio:Lightweight Federal Information Processing System (FIPS) Code Information Retrieval

Provides a lightweight suite of functions for retrieving information about 5-digit or 2-digit US FIPS codes.

Maintained by Justin Singh-Mohudpur. Last updated 1 years ago.

information-retrieval spatial us-data

14 stars 4.77 score 14 scripts 2 dependents

bioc

spoon:Address the Mean-variance Relationship in Spatial Transcriptomics Data

This package addresses the mean-variance relationship in spatially resolved transcriptomics data. Precision weights are generated for individual observations using Empirical Bayes techniques. These weights are used to rescale the data and covariates, which are then used as input in spatially variable gene detection tools.

Maintained by Kinnary Shah. Last updated 10 days ago.

spatial singlecell transcriptomics geneexpression preprocessing

4.76 score 19 scripts

mharinga

spatialrisk:Calculating Spatial Risk

Methods for spatial risk calculations. It offers an efficient approach to determine the sum of all observations within a circle of a certain radius. This might be beneficial for insurers who are required (by a recent European Commission regulation) to determine the maximum value of insured fire risk policies of all buildings that are partly or fully located within a circle of a radius of 200m. See Church (1974) <doi:10.1007/BF01942293> for a description of the problem.

Maintained by Martin Haringa. Last updated 7 months ago.

actuarial-science insurance solvency-ii spatial cpp

19 stars 4.76 score 30 scripts

bioc

FuseSOM:A Correlation Based Multiview Self Organizing Maps Clustering For IMC Datasets

A correlation-based multiview self-organizing map for the characterization of cell types in highly multiplexed in situ imaging cytometry assays (`FuseSOM`) is a tool for unsupervised clustering. `FuseSOM` is robust and achieves high accuracy by combining a `Self Organizing Map` architecture and a `Multiview` integration of correlation based metrics. This allows FuseSOM to cluster highly multiplexed in situ imaging cytometry assays.

Maintained by Elijah Willie. Last updated 5 months ago.

singlecell cellbasedassays clustering spatial

1 stars 4.71 score 17 scripts

bioc

smoothclust:smoothclust

Method for segmentation of spatial domains and spatially-aware clustering in spatial transcriptomics data. The method generates spatial domains with smooth boundaries by smoothing gene expression profiles across neighboring spatial locations, followed by unsupervised clustering. Spatial domains consisting of consistent mixtures of cell types may then be further investigated by applying cell type compositional analyses or differential analyses.

Maintained by Lukas M. Weber. Last updated 6 days ago.

spatial singlecell transcriptomics geneexpression clustering

1 stars 4.70 score 7 scripts

bioc

stJoincount:stJoincount - Join count statistic for quantifying spatial correlation between clusters

stJoincount facilitates the application of join count analysis to spatial transcriptomic data generated from the 10x Genomics Visium platform. This tool first converts a labeled spatial tissue map into a raster object, in which each spatial feature is represented by a pixel coded by label assignment. This process includes automatic calculation of optimal raster resolution and extent for the sample. A neighbors list is then created from the rasterized sample, in which adjacent and diagonal neighbors for each pixel are identified. After adding binary spatial weights to the neighbors list, a multi-categorical join count analysis is performed to tabulate "joins" between all possible combinations of label pairs. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance.

Maintained by Jiarong Song. Last updated 5 months ago.

transcriptomics clustering spatial biocviews software

4 stars 4.60 score 3 scripts

bioc

CARDspa:Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics

CARD is a reference-based deconvolution method that estimates cell type composition in spatial transcriptomics based on cell type specific expression information obtained from a reference scRNA-seq data. A key feature of CARD is its ability to accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. CARD relies on an efficient optimization algorithm for constrained maximum likelihood estimation and is scalable to spatial transcriptomics with tens of thousands of spatial locations and tens of thousands of genes.

Maintained by Jing Fu. Last updated 2 days ago.

spatial singlecell transcriptomics visualization openblas cpp openmp

4.60 score 3 scripts

hypertidy

whatarelief:Get topography elevation and online imagery data

Obtain elevation data, topography relief for any region on the Earth. Topography and bathymetry data is supported by default. Sensible defaults exist for usage, with a matrix of entire planet topography(and bathymetry) returned. The geographic extent can be modified (from whole-planet) to a simple region in longitude/latitude by 'xmin,xmax,ymin,ymax' range, or by specifying a grid exactly with extent, dimension, projection in generic or spatial formats ('terra' or 'raster'). Online sources for data are used, 'GEBCO' General Bathymetric Chart of the Oceans (GEBCO) as a background (to ~500m resolution), and Copernicus GLO-30 Digital Elevation Model for higher resolution (to ~30m resolution). Custom source/s of topography may be input to override defaults, links to file/s or URLs as required.

Maintained by Michael D. Sumner. Last updated 1 years ago.

cloud online raster spatial

50 stars 4.51 score 13 scripts

program--

hilbert:Coordinate Indexing on Hilbert Curves

Provides utilities for encoding and decoding coordinates to/from Hilbert curves based on the iterative encoding implementation described in Chen et al. (2006) <doi:10.1002/spe.793>.

Maintained by Justin Singh-Mohudpur. Last updated 3 years ago.

hilbert-curve spatial cpp

5 stars 4.40 score 5 scripts

bioc

XeniumIO:Import and represent Xenium data from the 10X Xenium Analyzer

The package allows users to readily import spatial data obtained from the 10X Xenium Analyzer pipeline. Supported formats include 'parquet', 'h5', and 'mtx' files. The package mainly represents data as SpatialExperiment objects.

Maintained by Marcel Ramos. Last updated 2 months ago.

software infrastructure dataimport singlecell spatial u24ca289073

4.40 score 2 scripts

bioc

CTSV:Identification of cell-type-specific spatially variable genes accounting for excess zeros

The R package CTSV implements the CTSV approach developed by Jinge Yu and Xiangyu Luo that detects cell-type-specific spatially variable genes accounting for excess zeros. CTSV directly models sparse raw count data through a zero-inflated negative binomial regression model, incorporates cell-type proportions, and performs hypothesis testing based on R package pscl. The package outputs p-values and q-values for genes in each cell type, and CTSV is scalable to datasets with tens of thousands of genes measured on hundreds of spots. CTSV can be installed in Windows, Linux, and Mac OS.

Maintained by Jinge Yu Developer. Last updated 5 months ago.

geneexpression statisticalmethod regression spatial genetics

2 stars 4.38 score 12 scripts

bioc

tpSVG:Thin plate models to detect spatially variable genes

The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis.

Maintained by Boyi Guo. Last updated 5 months ago.

spatial transcriptomics geneexpression software statisticalmethod dimensionreduction regression preprocessing spatially-resolve spatially-variable-genes

2 stars 4.30 score 2 scripts

bioc

SpatialOmicsOverlay:Spatial Overlay for Omic Data from Nanostring GeoMx Data

Tools for NanoString Technologies GeoMx Technology. Package to easily graph on top of an OME-TIFF image. Plotting annotations can range from tissue segment to gene expression.

Maintained by Maddy Griswold. Last updated 5 months ago.

geneexpression transcription cellbasedassays dataimport transcriptomics proteomics proprietaryplatforms rnaseq spatial datarepresentation visualization openjdk

4.30 score 8 scripts

bioc

RegionalST:Investigating regions of interest and performing regional cell type-specific analysis with spatial transcriptomics data

This package analyze spatial transcriptomics data through cross-regional cell type-specific analysis. It selects regions of interest (ROIs) and identifys cross-regional cell type-specific differential signals. The ROIs can be selected using automatic algorithm or through manual selection. It facilitates manual selection of ROIs using a shiny application.

Maintained by Ziyi Li. Last updated 4 months ago.

spatial transcriptomics reactome kegg

4.30 score 8 scripts

jeffreyevans

GeNetIt:Spatial Graph-Theoretic Genetic Gravity Modelling

Implementation of spatial graph-theoretic genetic gravity models. The model framework is applicable for other types of spatial flow questions. Includes functions for constructing spatial graphs, sampling and summarizing associated raster variables and building unconstrained and singly constrained gravity models.

Maintained by Jeffrey S. Evans. Last updated 2 years ago.

landscape-genetics r-spatial spatial statistics

9 stars 4.24 score 39 scripts

ghtaranto

scapesClassification:User-Defined Classification of Raster Surfaces

Series of algorithms to translate users' mental models of seascapes, landscapes and, more generally, of geographic features into computer representations (classifications). Spaces and geographic objects are classified with user-defined rules taking into account spatial data as well as spatial relationships among different classes and objects.

Maintained by Gerald H. Taranto. Last updated 3 years ago.

classification-algorithm object-detection raster spatial

1 stars 4.22 score 33 scripts

bioc

tomoseqr:R Package for Analyzing Tomo-seq Data

`tomoseqr` is an R package for analyzing Tomo-seq data. Tomo-seq is a genome-wide RNA tomography method that combines combining high-throughput RNA sequencing with cryosectioning for spatially resolved transcriptomics. `tomoseqr` reconstructs 3D expression patterns from tomo-seq data and visualizes the reconstructed 3D expression patterns.

Maintained by Ryosuke Matsuzawa. Last updated 5 months ago.

geneexpression sequencing rnaseq transcriptomics spatial visualization software

4.18 score 15 scripts

bioc

betaHMM:A Hidden Markov Model Approach for Identifying Differentially Methylated Sites and Regions for Beta-Valued DNA Methylation Data

A novel approach utilizing a homogeneous hidden Markov model. And effectively model untransformed beta values. To identify DMCs while considering the spatial. Correlation of the adjacent CpG sites.

Maintained by Koyel Majumdar. Last updated 3 months ago.

dnamethylation differentialmethylation immunooncology biomedicalinformatics methylationarray software multiplecomparison sequencing spatial coverage genetarget hiddenmarkovmodel microarray

4.18 score

bioc

spatialSimGP:Simulate Spatial Transcriptomics Data with the Mean-variance Relationship

This packages simulates spatial transcriptomics data with the mean- variance relationship using a Gaussian Process model per gene.

Maintained by Kinnary Shah. Last updated 5 months ago.

spatial transcriptomics geneexpression

4.18 score 2 scripts

eblondel

geonapi:'GeoNetwork' API R Interface

Provides an R interface to the 'GeoNetwork' API (<https://geonetwork-opensource.org/#api>) allowing to upload and publish metadata in a 'GeoNetwork' web-application and expose it to OGC CSW.

Maintained by Emmanuel Blondel. Last updated 4 days ago.

api geonetwork interface metadata spatial

7 stars 4.02 score 7 scripts

bioc

DESpace:DESpace: a framework to discover spatially variable genes and differential spatial patterns across conditions

Intuitive framework for identifying spatially variable genes (SVGs) and differential spatial variable pattern (DSP) between conditions via edgeR, a popular method for performing differential expression analyses. Based on pre-annotated spatial clusters as summarized spatial information, DESpace models gene expression using a negative binomial (NB), via edgeR, with spatial clusters as covariates. SVGs are then identified by testing the significance of spatial clusters. For multi-sample, multi-condition datasets, we again fit a NB model via edgeR, incorporating spatial clusters, conditions and their interactions as covariates. DSP genes-representing differences in spatial gene expression patterns across experimental conditions-are identified by testing the interaction between spatial clusters and conditions.

Maintained by Peiying Cai. Last updated 20 hours ago.

spatial singlecell rnaseq transcriptomics geneexpression sequencing differentialexpression statisticalmethod visualization

4 stars 4.02 score 13 scripts

prioritizr

prepr:Automatic Repair of Spatial Polygons

Automatically repair broken spatial polygons using constrained triangulation. The computational methodology is derived from Ledoux et al. (2014) <doi:10.1016/j.cageo.2014.01.009>.

Maintained by Jeffrey O Hanson. Last updated 4 months ago.

gis spatial gdal gmp cpp

6 stars 4.01 score 17 scripts

bioc

pengls:Fit Penalised Generalised Least Squares models

Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data.

Maintained by Stijn Hawinkel. Last updated 5 months ago.

transcriptomics regression timecourse spatial

4.00 score 4 scripts

bioc

tomoda:Tomo-seq data analysis

This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots.

Maintained by Wendao Liu. Last updated 5 months ago.

geneexpression sequencing rnaseq transcriptomics spatial clustering visualization

4.00 score 2 scripts

riatelab

fisheye:Transform Base Maps Using Log-Azimuthal Projection

Base maps are transformed to focus on a specific location using an azimuthal logarithmic distance transformation.

Maintained by Timothée Giraud. Last updated 11 months ago.

map spatial

16 stars 3.90 score 2 scripts

frbcesab

worldpa:An Interface to the World Database on Protected Areas (WDPA)

This package is an interface to the World Database on Protected Areas <https://www.protectedplanet.net> and its API <https://api.protectedplanet.net>. User can download terrestrial and marine protected areas for the world countries (one country at the time).

Maintained by Nicolas Casajus. Last updated 4 years ago.

protected-areas protected-planet shapefile spatial world

14 stars 3.85 score 1 scripts

afrimapr

afrilearndata:Small Africa Map Datasets for Learning

Small African datasets to help with learning and teaching of spatial techniques and mapping. Part of afrimapr project. To provide analysts based in Africa with more easily relateable example datasets. R objects for points, lines, polygons and raster. Source files including .gpkg, .shp, .kml, .tif, .grd, .csv.

Maintained by Andy South. Last updated 3 years ago.

map spatial teaching visualization

15 stars 3.68 score 64 scripts

rkbauer

RchivalTag:Analyzing and Interactive Visualization of Archival Tagging Data

A set of functions to generate, access and analyze standard data products from archival tagging data.

Maintained by Robert K. Bauer. Last updated 2 months ago.

data-visuali depth depth-temperature-profiles dygraphs ggpot leaflet minipat pelagic plotly satellite sensor spatial star-oddi temperature time-series tracks wildlife-computers

1 stars 3.59 score 26 scripts

clozanoruiz

sgo:Simple Geographical Operations (with OSGB36)

Methods focused in performing the OSGB36/ETRS89 transformation (Great Britain and the Isle of Man only) by using the Ordnance Survey's OSTN15/OSGM15 transformation model. Calculation of distances and areas from sets of points defined in any of the supported Coordinated Systems is also available.

Maintained by Carlos Lozano Ruiz. Last updated 3 days ago.

bng grid-reference ngr osgb36 ostn15 spatial

4 stars 3.30 score 7 scripts

robitalec

camtrapmonitoring:Camera Trap Monitoring For Estimating Wildlife Density

Evaluating potential camera trap locations. Potential locations are evaluated using relevant spatial layers producing a dataset of selected locations with covariates that can be used to quantify sampling bias. Soon - density estimation methods.

Maintained by Alec L. Robitaille. Last updated 9 months ago.

camera-trap camera-traps spatial wildlife

2 stars 2.00 score 2 scripts