This is the released version of nnSVG; for the devel version, see nnSVG.
Scalable identification of spatially variable genes in spatially-resolved transcriptomics dataBioconductor version: Release (3.21)
Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations.
Author: Lukas M. Weber [aut, cre] ORCID: 0000-0002-3282-1730 , Stephanie C. Hicks [aut] ORCID: 0000-0002-7858-0231
Maintainer: Lukas M. Weber <lmweb012 at gmail.com>
Citation (from within R, entercitation("nnSVG")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("nnSVG")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("nnSVG")Follow Installation instructions to use this package in your R session.
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