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nnSVG

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

Bioconductor version: 3.23 · Package version: 1.16.1

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.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("nnSVG")

Details

MaintainerLukas M. Weber <weberlm3@gmail.com>
AuthorLukas M. Weber [aut, cre] (ORCID: <https://orcid.org/0000-0002-3282-1730>), Stephanie C. Hicks [aut] (ORCID: <https://orcid.org/0000-0002-7858-0231>)
LicenseMIT + file LICENSE
URLhttps://github.com/lmweber/nnSVG
Bug Reportshttps://github.com/lmweber/nnSVG/issues
Downloads rank362
Source branchRELEASE_3_23
biocViewsGeneExpression, Preprocessing, SingleCell, Software, Spatial, Transcriptomics

Documentation

Download

Dependencies

Depends: R (>= 4.2)

Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, BRISC, BiocParallel, Matrix, matrixStats, stats, methods

Suggests: BiocStyle, knitr, rmarkdown, STexampleData, WeberDivechaLCdata, scran, ggplot2, testthat

Reverse dependencies

Imports Me (1): spoon

Suggests Me (2): SEraster, tpSVG