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Thin plate models to detect spatially variable genes

Bioconductor version: Release (3.19)

The goal of `tpSVG` is to detect and visualize spatial variation in the gene expression for spatially resolved transcriptomics data analysis. Specifically, `tpSVG` introduces a family of count-based models, with generalizable parametric assumptions such as Poisson distribution or negative binomial distribution. In addition, comparing to currently available count-based model for spatially resolved data analysis, the `tpSVG` models improves computational time, and hence greatly improves the applicability of count-based models in SRT data analysis.

Author: Boyi Guo [aut, cre] , Lukas M. Weber [ctb] , Stephanie C. Hicks [aut]

Maintainer: Boyi Guo < at>

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


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:

intro_to_tpSVG HTML R Script
Reference Manual PDF


biocViews DimensionReduction, GeneExpression, Preprocessing, Regression, Software, Spatial, StatisticalMethod, Transcriptomics
Version 1.0.0
In Bioconductor since BioC 3.19 (R-4.4) (< 6 months)
License MIT + file LICENSE
Depends mgcv, R (>= 4.4)
Imports stats, BiocParallel, MatrixGenerics, methods, SingleCellExperiment, SummarizedExperiment, SpatialExperiment
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Suggests BiocStyle, knitr, nnSVG, rmarkdown, scran, scuttle, STexampleData, escheR, ggpubr, colorspace, BumpyMatrix, sessioninfo, testthat (>= 3.0.0)
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Follow Installation instructions to use this package in your R session.

Source Package tpSVG_1.0.0.tar.gz
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macOS Binary (x86_64) tpSVG_1.0.0.tgz
macOS Binary (arm64) tpSVG_1.0.0.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
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Old Source Packages for BioC 3.19 Source Archive