Identification Of Clinically Relevant Genomic Subtypes Using Outcome Weighted Learning

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Documentation for package ‘survClust’ version 1.2.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

survClust-package	perform supervised clustering for a particular 'k'
combineDist	Integrates weighted distance matrices
cv_survclust	performs cross validation on supervised clustering, 'survClust' for a particular 'k'. 'cv_survclust' runs
cv_voting	For a 'survClust' fit, return consolidated labels across rounds of cross validation for a specific 'k'. Note that cv.fit already has consolidated class labels across folds
getDist	Calculates weighted distance matrix of multiple genomic data types
getStats	Compute fit statistics after cross validation via 'cv_survclust'
plotStats	Plot the output from 'getStats'
simdat	Simulated dataset with 3-class solution
simsurvdat	Simulated survival dataset with accompanying 'simdat'
survClust	perform supervised clustering for a particular 'k'
uvm_dat	TCGA UVM Mutation and Copy Number datasets
uvm_survClust_cv.fit	survClust cv.survclust output of integrated TCGA UVM Mutation and Copy Number datasets.
uvm_survdat	TCGA UVM Clinical file