data_simulation

<p>Functions to generate simulated spatial transcriptomics data with known
spatially variable genes (ground truth). Useful for benchmarking and testing.</p>

A unified framework for detecting spatially variable genes (SVGs)
in spatial transcriptomics data. This package integrates multiple
state-of-the-art SVG detection methods including 'MERINGUE' (Moran's I based
spatial autocorrelation), 'Giotto' binSpect (binary spatial enrichment test),
'SPARK-X' (non-parametric kernel-based test), and 'nnSVG' (nearest-neighbor
Gaussian processes). Each method is implemented with optimized performance
through vectorization, parallelization, and 'C++' acceleration where applicable.
Methods are described in Miller et al. (2021) <doi:10.1101/gr.271288.120>,
Dries et al. (2021) <doi:10.1186/s13059-021-02286-2>,
Zhu et al. (2021) <doi:10.1186/s13059-021-02404-0>, and
Weber et al. (2023) <doi:10.1038/s41467-023-39748-z>.

data_simulation: Simulate Spatial Transcriptomics Data with Known SVGs

Description

Arguments

Value