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
.
1 2 3 4 5 6 | 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' |
Not intended for use outside of a call to polyCluster
.
Returns a list including a lot of information for each k, most importantly the consensus matrices and class assignments
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