Visualization and Analysis of Spatial Heterogeneity in Spatially-Resolved Gene Expression
compare_SThet: Compares spatial autocorrelation statistics across samp...
dim: Prints the dimensions of count arrays within an STList object.
per_unit_counts: Generates distribution plots of spot/cell meta data o...
filter_data: Filters cells/spots, genes, or samples
gene_interpolation: Spatial interpolation of gene expression
get_gene_meta: Extract gene-level metadata and statistics
load_images: Place tissue images within STlist
plot_counts: Generates plots for the distribution of counts
plot_image: Generate a ggplot object of the tissue image
pseudobulk_dim_plot: Plot PCA of pseudobulk samples
pseudobulk_heatmap: Heatmap of pseudobulk samples
pseudobulk_samples: Aggregates counts into "pseudo bulk" samples
show: Prints overview of STList oject.
spatial_metadata: Prints the names of the available spot/cell annotati...
STclust: Detect clusters of spots/cells
STdiff_volcano: Generates volcano plots from STdiff results
STdiff: Differential gene expression analysis for spatial transcriptom...
STenrich
STgradient: Tests of gene expression spatial gradients
SThet: Computes global spatial autocorrelation statistics on gene expr...
Definition of an STlist object class.
STlist: Creation of STlist objects for spatial transcriptomics analysi...
STplot_interpolation: Visualize gene expression surfaces
STplot: Plots of gene expression, cluster memberships, and metadata in...
summarize_STlist: Generates a data frame with summary statistics
summary: Prints overview of STList oject.
tissue_names: Prints the names of the tissue samples in the STlist
transform_data: Transformation of spatial transcriptomics data
Visualization and analysis of spatially resolved transcriptomics data. The 'spatialGE' R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and differential expression analysis using spatial mixed models.