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

Downloads:

Reference manual: ICDS.pdf
Vignettes: ICDS User Guide
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-release (x86_64): not available, r-oldrel: ICDS_0.1.2.tgz
Old sources: ICDS archive

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