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SVG 1.0.0

First Release

New Features

• Unified interface: CalSVG() function provides a single entry point for all SVG detection methods
• Six SVG detection methods implemented with consistent output format:
  • CalSVG_MERINGUE(): Moran’s I with binary spatial network
  • CalSVG_Seurat(): Moran’s I with inverse distance weights
    
  • CalSVG_binSpect(): Binary spatial enrichment test (Giotto)
  • CalSVG_SPARKX(): Non-parametric kernel-based test
  • CalSVG_nnSVG(): Nearest-neighbor Gaussian processes
  • CalSVG_MarkVario(): Mark variogram method

Spatial Network Construction

• buildSpatialNetwork(): Build spatial neighborhood networks using Delaunay triangulation or KNN
• getSpatialNeighbors_Delaunay(): Delaunay triangulation-based network
• getSpatialNeighbors_KNN(): K-nearest neighbors network

Statistical Utilities

• moranI(): Calculate Moran’s I statistic
• moranI_test(): Hypothesis testing for spatial autocorrelation
• ACAT_combine(): Aggregated Cauchy Association Test for p-value combination
• binarize_expression(): Multiple methods for expression binarization

Data Simulation

• simulate_spatial_data(): Generate simulated spatial transcriptomics data with known SVGs
• Support for multiple spatial patterns: gradient, hotspot, periodic, cluster

Performance

• C++ implementation via Rcpp/RcppArmadillo for computationally intensive operations
• Parallel processing support via n_threads parameter
• Efficient memory usage for large-scale data

Documentation

• Comprehensive vignette with mathematical background
• Complete function documentation with examples
• Benchmark comparison between methods

Dependencies

• Core: Matrix, Rcpp, RcppArmadillo
• Optional: geometry, RANN, BRISC, CompQuadForm, BiocParallel, spatstat

Authors

• Zaoqu Liu (maintainer) - liuzaoqu@163.com

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.