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Scalable identification of spatially variable genes in spatially-resolved transcriptomics data

Bioconductor version: Release (3.19)

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] , Stephanie C. Hicks [aut]

Maintainer: Lukas M. Weber <lmweberedu at>

Citation (from within R, enter citation("nnSVG")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

nnSVG Tutorial HTML R Script
Reference Manual PDF


biocViews GeneExpression, Preprocessing, SingleCell, Software, Spatial, Transcriptomics
Version 1.8.0
In Bioconductor since BioC 3.15 (R-4.2) (2 years)
License MIT + file LICENSE
Depends R (>= 4.2)
Imports SpatialExperiment, SingleCellExperiment, SummarizedExperiment, BRISC, BiocParallel, Matrix, matrixStats, stats, methods
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Suggests BiocStyle, knitr, rmarkdown, STexampleData, WeberDivechaLCdata, scran, ggplot2, testthat
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Imports Me spoon
Suggests Me tpSVG
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Follow Installation instructions to use this package in your R session.

Source Package nnSVG_1.8.0.tar.gz
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macOS Binary (x86_64) nnSVG_1.8.0.tgz
macOS Binary (arm64) nnSVG_1.8.0.tgz
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