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.
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datarepresentationtranscriptomicsspatial
10.44 score 58 stars 4 dependents 796 scripts 817 downloadsVoyager - 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.
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geneexpressionspatialtranscriptomicsvisualizationbioconductoredaesdaexploratory-data-analysisomicsspatial-statisticsspatial-transcriptomics
9.51 score 106 stars 1 dependents 507 scripts 664 downloadswompwomp - Optimizing Alluvial Plots
Sort k-partite graphs with node order, layer order, and node grouping optimized with a heuristic to (nearly) minimize edge crossings, and render the result as an alluvial plot. Useful for improving visualizations with alluvial plots by "untangling" the graphs.
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cpp
7.55 score 33 stars 15 scripts 300 downloadsconcordexR - 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.
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singlecellclusteringspatialtranscriptomics
7.04 score 15 stars 183 scripts 364 downloadsSFEData - Example SpatialFeatureExperiment datasets
Example spatial transcriptomics datasets with Simple Feature annotations as SpatialFeatureExperiment objects. Technologies include Visium, slide-seq, Nanostring CoxMX, Vizgen MERFISH, and 10X Xenium. Tissues include mouse skeletal muscle, human melanoma metastasis, human lung, breast cancer, and mouse liver.
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experimenthubexpressiondatamus_musculus_datahomo_sapiens_dataspatialdatatissuesinglecelldatadatapkg
6.79 score 2 stars 1 dependents 343 scriptsalabaster.sfe - Language agnostic on disk serialization of SpatialFeatureExperiment
Builds upon the existing ArtifactDB project, expending alabaster.spatial for language agnostic on disk serialization of SpatialFeatureExperiment.
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datarepresentationspatialopenjdk
5.40 score 252 scripts 197 downloads