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

## Documentation for package ‘CancerSubtypes’ version 1.10.0

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

## Help Pages

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