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ccml  

Consensus Clustering for Different Sample Coverage Data
View on CRAN: Click here


Download and install ccml package within the R console
Install from CRAN:
install.packages("ccml")

Install from Github:
library("remotes")
install_github("cran/ccml")

Install by package version:
library("remotes")
install_version("ccml", "1.4.0")



Attach the package and use:
library("ccml")
Maintained by
Chuanxing Li
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-08-18
Latest Update: 2023-08-30
Description:
Consensus clustering, also called meta-clustering or cluster ensembles, has been increasingly used in clinical data. Current consensus clustering methods tend to ensemble a number of different clusters from mathematical replicates with similar sample coverage. As the fact of common variety of sample coverage in the real-world data, a new consensus clustering strategy dealing with such biological replicates is required. This is a two-step consensus clustering package, which is used to input multiple predictive labels with different sample coverage (missing labels).
How to cite:
Chuanxing Li (2022). ccml: Consensus Clustering for Different Sample Coverage Data. R package version 1.4.0, https://cran.r-project.org/web/packages/ccml. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.0.0 (2022-08-18 10:00), 1.1.0 (2022-08-29 10:30), 1.2.0 (2022-12-14 08:30), 1.3.0 (2023-08-23 15:40)
Other packages that cited ccml R package
View ccml citation profile
Other R packages that ccml depends, imports, suggests or enhances
Complete documentation for ccml
Functions, R codes and Examples using the ccml R package
Some associated functions: callNCW . ccml . example_data . plotCompareCW . randConsensusMatrix . spectralClusteringAffinity . 
Some associated R codes: callNCW.R . ccml.R . example_data.R . globals.R . plotCompareCW.R . randConsensusMatrix.R . spectralClusteringAffinity.R .  Full ccml package functions and examples
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