Cancer subtypes identification, validation and visualization based on multiple genomic data sets

Documentation for package ‘CancerSubtypes’ version 1.13.1

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data.checkDistribution	Data check distribution
data.imputation	Data imputation
data.normalization	Data normalization
DiffExp.limma	DiffExp.limma
drawHeatmap	Generate heatmaps
ExecuteCC	Execute Consensus Clustering
ExecuteCNMF	Execute Consensus NMF (Nonnegative matrix factorization)
ExecuteiCluster	Execute iCluster (Integrative clustering of multiple genomic data)
ExecuteSNF	Execute SNF(Similarity Network Fusion )
ExecuteSNF.CC	Execute the combined SNF (Similarity Network Fusion) and Consensus clustering
ExecuteWSNF	Execute the WSNF(Weighted Similarity Network Fusion)
FSbyCox	Biological feature (such as gene) selection based on Cox regression model.
FSbyMAD	Biological feature (such as gene) selection based on the most variant Median Absolute Deviation (MAD).
FSbyPCA	Biological feature (such as gene) dimension reduction and extraction based on Principal Component Analysis.
FSbyVar	Biological feature (such as gene) selection based on the most variance.
GeneExp	Dataset: Gene expression
miRNAExp	Dataset: miRNA expression
Ranking	Dataset: A default ranking of features for the fuction ExecuteWSNF()
saveFigure	This function save the figure in the current plot.
sigclustTest	A statistical method for testing the significance of clustering results.
silhouette_SimilarityMatrix	Compute or Extract Silhouette Information from Clustering based on similarity matrix.
spectralAlg	This is an internal function but need to be exported for the function ExecuteSNF.CC() call.
status	Dataset: Survival status
survAnalysis	Survival analysis(Survival curves, Log-rank test) and compute Silhouette information for cancer subtypes
time	Dataset: Survival time