DOI: 10.18129/B9.bioc.nnSVG  

This is the development version of nnSVG; for the stable release version, see nnSVG.

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

Bioconductor version: Development (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 <lukas.weber.edu at gmail.com>

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biocViews GeneExpression, Preprocessing, SingleCell, Software, Spatial, Transcriptomics
Version 1.7.0
In Bioconductor since BioC 3.15 (R-4.2) (1.5 years)
License MIT + file LICENSE
Depends R (>= 4.2)
Imports SpatialExperiment, SingleCellExperiment, SummarizedExperiment, BRISC, BiocParallel, Matrix, matrixStats, stats, methods
Suggests BiocStyle, knitr, rmarkdown, STexampleData, WeberDivechaLCdata, scran, ggplot2, testthat
URL https://github.com/lmweber/nnSVG
BugReports https://github.com/lmweber/nnSVG/issues
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