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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "SVP" in publications use:'
type: software
license: GPL-3.0-only
title: 'SVP: Predicting cell states and their variability in single-cell or spatial
  omics data'
version: 1.5.0
doi: 10.32614/CRAN.package.SVP
abstract: 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.
authors:
- family-names: Xu
  given-names: Shuangbin
  email: xshuangbin@163.com
  orcid: https://orcid.org/0000-0003-3513-5362
- family-names: Yu
  given-names: Guangchuang
  email: guangchuangyu@gmail.com
  orcid: https://orcid.org/0000-0002-6485-8781
repository: https://bioc.r-universe.dev
repository-code: https://github.com/YuLab-SMU/SVP
commit: 4e36f875d987ca752bd7ccc2de7198ab0907e106
url: https://github.com/YuLab-SMU/SVP
date-released: '2026-04-28'
contact:
- family-names: Xu
  given-names: Shuangbin
  email: xshuangbin@163.com
  orcid: https://orcid.org/0000-0003-3513-5362