IntClust: Integration of Multiple Data Sets with Clustering Techniques

Several integrative data methods in which information of objects from different data sources can be combined are included in the IntClust package. As a single data source is limited in its point of view, this provides more insight and the opportunity to investigate how the variables are interconnected. Clustering techniques are to be applied to the combined information. For now, only agglomerative hierarchical clustering is implemented. Further, differential gene expression and pathway analysis can be conducted on the clusters. Plotting functions are available to visualize and compare results of the different methods.

Version: 0.1.0
Depends: R (≥ 2.10)
Imports: ade4, a4Core, Biobase, cluster, plotrix, plyr, gplots, gridExtra, limma, gtools, e1071, pls, stats, utils, graphics, FactoMineR, analogue, lsa, SNFtool, grDevices, ggplot2, circlize, Rdpack, data.table, igraph
Suggests: MLP, biomaRt, org.Hs.eg.db, a4Base
Published: 2018-07-30
Author: Marijke Van Moerbeke
Maintainer: Marijke Van Moerbeke <marijke.vanmoerbeke at uhasselt.be>
License: GPL-3
NeedsCompilation: no
CRAN checks: IntClust results

Downloads:

Reference manual: IntClust.pdf
Vignettes: IntClustvignette
Package source: IntClust_0.1.0.tar.gz
Windows binaries: r-devel: IntClust_0.1.0.zip, r-release: IntClust_0.1.0.zip, r-oldrel: IntClust_0.1.0.zip
OS X binaries: r-release: IntClust_0.1.0.tgz, r-oldrel: IntClust_0.1.0.tgz
Old sources: IntClust archive

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