DESCRIPTION

Package: SVP
Title: Predicting cell states and their variability in single-cell or spatial omics data
Version: 0.99.0
Authors@R: c(
  person("Shuangbin", "Xu", email = "xshuangbin@163.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3513-5362")),
  person("Guangchuang", "Yu", email = "guangchuangyu@gmail.com", role = c("aut", "ctb"), comment = c(ORCID = "0000-0002-6485-8781")))
Description: 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.
Depends:
  R (>= 4.3.0)
Imports:
  Rcpp,
  RcppParallel,
  methods,
  cli,
  dplyr,
  rlang,
  S4Vectors,
  SummarizedExperiment,
  SingleCellExperiment,
  SpatialExperiment,
  BiocGenerics,
  BiocParallel,
  fastmatch,
  pracma,
  stats,
  withr,
  Matrix,
  DelayedMatrixStats,
  deldir,
  utils,
  BiocNeighbors,
  ggplot2,
  ggstar,
  ggtree,
  ggfun
Suggests:
  rmarkdown,
  prettydoc,
  broman,
  RSpectra,
  BiasedUrn,
  knitr,
  ks,
  igraph,
  testthat (>= 3.0.0),
  scuttle,
  magrittr,
  DropletUtils,
  tibble,
  tidyr,
  harmony,
  aplot,
  scales,
  ggsc,
  scatterpie,
  scran
License: GPL-3
BugReports: https://github.com/YuLab-SMU/SVP/issues
URL: https://github.com/YuLab-SMU/SVP
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
biocViews: SingleCell, Software, Spatial, Transcriptomics, GeneTarget,
  GeneExpression, GeneSetEnrichment, Transcription, GO, KEGG
SystemRequirements: GNU make
ByteCompile: true
VignetteBuilder: knitr
LinkingTo: 
  Rcpp,
  RcppArmadillo (>= 14.0),
  RcppParallel,
  RcppEigen,
  dqrng
Config/testthat/edition: 3
LazyData: false