CCP: Consensus clustering
In syspremed/polyClustR: polyClustR: A pipeline to reconcile clustering solutions

Description Usage Arguments Details Value

Takes an expression matrix and finds clusters via hierarchical clustering, k-means or partitioning around medoids. Adapted from ConsensusClusterPlus of the ConsensusClusterPlus package, see ?ConsensusClusterPlus.

CCP(d = NULL, maxK = maxK, reps = reps, pItem = 0.8, pFeature = 1,
  clusterAlg = "hc", title = "untitled_consensus_cluster",
  innerLinkage = "average", finalLinkage = "average",
  distance = "euclidean", ml = NULL, tmyPal = NULL, seed = NULL,
  plot = "pdf", writeTable = TRUE, weightsItem = NULL,
  weightsFeature = NULL, verbose = F, corUse = "everything")

`d`	Numeric matrix. Data to be clustered, where columns=items/samples and rows are features
`maxK`	Integer. Maximum number of clusters to evaluate
`reps`	Integer. Number of subsamples so consensus can be evaluated
`pItem`	Numerical value. Proportion of items (columns) to sample in each subsampling
`pFeature`	Numerical value. Proportion of features (rows) to sample in each subsampling
`clusterAlg`	Character string. Cluster algorithm: 'hc' heirarchical (hclust), 'pam' for paritioning around medoids, 'km' for k-means
`title`	Character string. Name for output directory. Directory is created only if plot is not NULL or writeTable is TRUE. This title can be an abosulte or relative path.
`innerLinkage`	heirarchical linkage method for subsampling
`finalLinkage`	heirarchical linkage method for consensus matrix
`distance`	Character string. 'pearson': (1 - Pearson correlation), 'spearman' (1 - Spearman correlation), 'euclidean', 'binary', 'maximum', 'canberra', 'minkowski" or custom distance function.
`ml`	Optional. Prior result. If supplied then only do graphics and tables
`tmyPal`	Optional. Character vector. Colors for consensus matrix
`seed`	Optional. Numerical. Sets random seed for reproducible results
`plot`	Character string. NULL - print to screen, 'pdf', 'png', 'pngBMP' for bitmap png, helpful for large datasets
`writeTable`	Logical. TRUE - write ouput and log to csv
`weightsItem`	Optional. Numerical vector. Weights to be used for sampling items
`weightsFeature`	Optional. Numerical vector. Weights to be used for sampling features
`verbose`	Logical. If TRUE, print messages to the screen to indicate progress. This is useful for large datasets
`corUse`	Optional. Character string. Specifies how to handle missing data in correlation distances 'everything','pairwise.complete.obs', 'complete.obs'