moranI_full_cpp

Computes Moran's I along with expected value and standard deviation
under the null hypothesis. Optimized with Armadillo vectorization.

internal

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>.

Zaoqu Liu

Spatially Variable Genes Detection Methods for Spatial
Transcriptomics

moranI_full_cpp function

<dl><dt>expr_matrix</dt>
<dd>Numeric matrix (genes x spots)</dd>
<dt>W</dt>
<dd>Spatial weights matrix (spots x spots), row-standardized</dd></dl>

Arguments

Fast Moran's I with Full Statistics — moranI_full_cpp

<dl>

<dt>expr_matrix</dt>
<dd>Numeric matrix (genes x spots)</dd>


<dt>W</dt>
<dd>Spatial weights matrix (spots x spots), row-standardized</dd>

</dl>

moranI_full_cpp: Fast Moran's I with Full Statistics

Description

Usage

Value

Arguments

Details