Changes in version 2.3.1 New SpaceMarkersExperiment class - Added SpaceMarkersExperiment (SME), an S4 class extending SpatialExperiment that carries hotspots, influence maps, and pattern metadata alongside expression and spatial coordinates. - Added SME-aware methods and dispatch for get_spatial_features, get_interacting_genes, get_pairwise_interacting_genes, and related accessors so users can stay in a single Bioconductor object end-to-end. - Re-exported SpatialExperiment / SummarizedExperiment accessors so SME workflows do not require attaching upstream packages. AnnData I/O - Added load_anndata() to read .h5ad files directly into a SpaceMarkersExperiment. - Added save_anndata() to write SME objects back to .h5ad with round-trip fidelity. - Added direct SingleCellExperiment → SpaceMarkersExperiment coercion. Directed interaction analysis and visualization - Added overlap_map() for per-spot directed interaction classification (3-level factor per direction). - plot_spatial() gained a source argument (colData/assay/hotspots/influence_map/interaction) and directed interaction labels for three-way hotspot overlays. - Hotspot plots in vignettes now use distinct colors per pattern. Vignettes - New SpaceMarkersExperiment_vignette walks through the SME-first workflow. - SpaceMarkersStepByStep_vignette restructured as a tutorial with per-step plots interleaved with narrative. - Vignette data downloads now route through BiocFileCache instead of ad hoc temp files. - Compact JPEG raster output keeps rendered HTML vignettes under Bioconductor's per-file size guideline. Bioconductor readiness - Runnable examples and \value sections added across exported functions for BiocCheck. - Tightened .Rbuildignore and .gitignore for a clean source tarball. - Renamed R/OneSpaceMarkers.R → R/SpaceMarkers.R and regenerated Collate order. Performance - Avoided dense coercion in the gene filter and skipped materializing the spPatterns data frame, reducing memory pressure on large samples. Changes in version 2.0.0 - Added support for directed cell-cell interaction (see calculate_gene_scores_directed, calculate_influence) - Functions for supporting ligand-receptor interactions based on directed cell-cell interactions - Added ligand–receptor and gene-set scoring utilities: calculate_lr_scores, calculate_gene_set_score, and calculate_gene_set_specificity. - Added support for handling Visium HD - plotting functions using circlize for showing ligand-receiver interactions. - API and naming standardization (backwards-incompatible) Major public functions renamed to snake_case for consistency (examples: calcInfluence → calculate_influence, getInteractingGenes → get_interacting_genes, getPairwiseInteractingGenes → get_pairwise_interacting_genes, getSpatialFeatures → get_spatial_features, getSpatialParameters → get_spatial_parameters) Changes in version 1.5.0 - Added findAllHotspots to use with getPairwiseInteractingGenes - Return empty interacting genes list instead of failing with error when no genes pass fdr threshold. Changes in version 1.2.0 - Updated SpaceMarkersMetric by fixing signage and log transformed to scale magnitude - Added get_spatial_paramsExternal which enables getting spatial parameters from file or from the user. - Deprecated getSpatialParameters - Enabled includeSelf = TRUE in getInteractingGenes.R to improve hotspot detection - Enabled load10XCoords to read coordinates from VisiumHD directory - Optimized the long running row.dunn.test() function - Corrected sparse -> dense conversions - Added getPairwiseInteractingGenes which enables pairwise analysis of interacting patterns - getSpatialFeatures: add default method to infer the object passed to it. Changes in version 0.1.0 - Added a NEWS.md file to track changes to the package.