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.
- Version0.1.3
- R version≥ 4.3.0
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?No
- ICDS citation info
- Last release08/01/2024
Documentation
Team
Junwei Han
Baotong Zheng
Show author detailsRolesAuthorSiyao Liu
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 204 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 6 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 3,132 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Aug 04, 2024 with 48 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports2 packages
- Suggests2 packages