This is the released version of SVP; for the devel version, see SVP.
Predicting cell states and their variability in single-cell or spatial omics dataBioconductor version: Release (3.21)
SVP uses the distance between cells and cells, features and features, cells and features in the space of MCA to build nearest neighbor graph, then uses random walk with restart algorithm to calculate the activity score of gene sets (such as cell marker genes, kegg pathway, go ontology, gene modules, transcription factor or miRNA target sets, reactome pathway, ...), which is then further weighted using the hypergeometric test results from the original expression matrix. To detect the spatially or single cell variable gene sets or (other features) and the spatial colocalization between the features accurately, SVP provides some global and local spatial autocorrelation method to identify the spatial variable features. SVP is developed based on SingleCellExperiment class, which can be interoperable with the existing computing ecosystem.
Author: Shuangbin Xu [aut, cre] ORCID: 0000-0003-3513-5362 , Guangchuang Yu [aut, ctb] ORCID: 0000-0002-6485-8781
Maintainer: Shuangbin Xu <xshuangbin at 163.com>
Citation (from within R, entercitation("SVP")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SVP")
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("SVP")Follow Installation instructions to use this package in your R session.
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