DESCRIPTION

Package: CrossICC
Type: Package
Title: An Interactive Consensus Clustering Framework for Multi-platform Data Analysis
Version: 0.99.27
Description: CrossICC utilizes an iterative strategy to derive the optimal gene set and cluster number from consensus similarity matrix generated by consensus clustering and it is able to deal with multiple cross platform datasets so that requires no between-dataset normalizations. This package also provides abundant functions for visualization and identifying subtypes of cancer. Specially, many cancer-related analysis methods are embedded to facilitate the clinical translation of the identified cancer subtypes.
Authors@R: c(
  person('Yu', 'Sun',   email = "suny226@mail2.sysu.edu.cn", role = c('aut', 'cre'), comment = c(ORCID = '0000-0003-4269-7187')),
  person('Qi', 'Zhao',  email = 'zhaoqi@sysucc.org.cn', role = 'aut', comment = c(ORCID = '0000-0002-8683-6145'))
  )
License: GPL-3 | file LICENSE
Encoding: UTF-8
Suggests: rmarkdown,
  testthat,
  knitr,
  shiny,
  shinydashboard,
  shinyWidgets,
  shinycssloaders,
  DT,
  ggthemes,
  ggplot2,
  pheatmap,
  RColorBrewer,
  tibble,
  ggalluvial
RoxygenNote: 6.1.1
Imports: data.table,
    methods,
    MergeMaid,
    ConsensusClusterPlus,
    limma,
    cluster,
    dplyr,
    Biobase,
    grDevices,
    stats,
    graphics,
    utils
Depends: R (>= 3.5),
  MASS
biocViews: Software, GeneExpression, DifferentialExpression, GUI, GeneSetEnrichment, Classification, Clustering,
  FeatureExtraction, Survival, Microarray, RNASeq, BatchEffect, Normalization, Preprocessing, Visualization
VignetteBuilder: knitr
Roxygen: list(markdown = TRUE)