DESCRIPTION

Package: cola
Type: Package
Title: A Framework for Consensus Partitioning
Version: 2.9.1
Date: 2024-02-26
Authors@R: person("Zuguang", "Gu", email = "z.gu@dkfz.de", role = c("aut", "cre"),
                  comment = c('ORCID'="0000-0002-7395-8709"))
Depends: R (>= 4.0.0)
Imports: grDevices, 
         graphics, 
         grid, 
         stats, 
         utils,
         ComplexHeatmap (>= 2.5.4), 
         matrixStats, 
         GetoptLong, 
         circlize (>= 0.4.7), 
         GlobalOptions (>= 0.1.0),
         clue, 
         parallel, 
         RColorBrewer, 
         cluster, 
         skmeans, 
         png, 
         mclust, 
         crayon, 
         methods, 
         xml2,
         microbenchmark, 
         httr,
         knitr (>= 1.4.0),
         markdown (>= 1.6),
         digest,
         impute,
         brew,
         Rcpp (>= 0.11.0),
         BiocGenerics,
         eulerr,
         foreach,
         doParallel,
         doRNG,
         irlba
Suggests: genefilter,
          mvtnorm,
          testthat (>= 0.3),
          samr,
          pamr,
          kohonen,
          NMF,
          WGCNA,
          Rtsne,
          umap,
          clusterProfiler,
          ReactomePA,
          DOSE,
          AnnotationDbi,
          gplots,
          hu6800.db,
          BiocManager,
          data.tree,
          dendextend,
          Polychrome,
          rmarkdown,
          simplifyEnrichment,
          cowplot,
          flexclust,
          randomForest,
          e1071
Description: Subgroup classification is a basic task in
  genomic data analysis, especially for gene expression and DNA methylation data
  analysis. It can also be used to test the agreement to known clinical
  annotations, or to test whether there exist significant batch effects. The
  cola package provides a general framework for subgroup classification by
  consensus partitioning. It has the following features: 1. It modularizes the
  consensus partitioning processes that various methods can be easily
  integrated. 2. It provides rich visualizations for interpreting the results.
  3. It allows running multiple methods at the same time and provides
  functionalities to straightforward compare results. 4. It provides a new
  method to extract features which are more efficient to separate subgroups. 5.
  It automatically generates detailed reports for the complete analysis. 6. It allows
  applying consensus partitioning in a hierarchical manner.
URL: https://github.com/jokergoo/cola, 
     https://jokergoo.github.io/cola_collection/
VignetteBuilder: knitr
biocViews: Clustering, GeneExpression, Classification, Software
License: MIT + file LICENSE
LinkingTo: Rcpp