ICDS: Identification of Cancer Dysfunctional Subpathway with Omics Data

Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression. 2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.

Version:	0.1.2
Depends:	R (≥ 2.10)
Imports:	igraph, graphite, metap, methods, org.Hs.eg.db
Suggests:	knitr, rmarkdown, prettydoc
Published:	2021-07-15
Author:	Junwei Han [cre], Baotong Zheng [aut], Siyao Liu [ctb]
Maintainer:	Junwei Han <hanjunwei1981 at 163.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Citation:	ICDS citation info
Materials:	README
CRAN checks:	ICDS results

Documentation:

Reference manual:	ICDS.pdf
Vignettes:	ICDS User Guide

Downloads:

Package source:	ICDS_0.1.2.tar.gz
Windows binaries:	r-devel: ICDS_0.1.2.zip, r-release: ICDS_0.1.2.zip, r-oldrel: ICDS_0.1.2.zip
macOS binaries:	r-release (arm64): ICDS_0.1.2.tgz, r-oldrel (arm64): ICDS_0.1.2.tgz, r-release (x86_64): ICDS_0.1.2.tgz, r-oldrel (x86_64): ICDS_0.1.2.tgz
Old sources:	ICDS archive

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