binarize_kmeans_cpp

Binarizes each row of an expression matrix using k-means clustering.

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

binarize_kmeans_cpp function

<dl><dt>expr_matrix</dt>
<dd>Numeric matrix (genes x spots)</dd>
<dt>max_iter</dt>
<dd>Maximum iterations for k-means</dd></dl>

Arguments

Fast Binarization using K-means (k=2) — binarize_kmeans_cpp

<dl>

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


<dt>max_iter</dt>
<dd>Maximum iterations for k-means</dd>

</dl>

binarize_kmeans_cpp: Fast Binarization using K-means (k=2)

Description

Usage

Value

Arguments