Description Usage Arguments Details Value References See Also Examples
This function implements the consensus cluster algorithm.
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dat |
Probe by sample omic data matrix. Data should be filtered and normalized prior to analysis. |
max_k |
Integer specifying the maximum cluster number to evaluate.
Default is |
reps |
Number of subsamples to draw. |
distance |
Distance metric for clustering. Supports all methods
available in |
cluster_alg |
Clustering algorithm to implement. Currently supports
hierarchical ( |
hclust_method |
Method to use if |
p_item |
Proportion of items to include in each subsample. |
p_feature |
Proportion of features to include in each subsample. |
wts_item |
Optional vector of item weights. |
wts_feature |
Optional vector of feature weights. |
seed |
Optional seed for reproducibility. |
parallel |
If a parallel backend is loaded and available, should the function use it? Highly advisable if hardware permits. |
check |
Check for errors in function arguments? This is set to |
Consensus clustering is a resampling procedure to evaluate cluster stability.
A user-specified proportion of samples are held out on each run of the
algorithm to test how often the remaining samples do or do not cluster
together. The result is a square, symmetric consensus matrix for each value
of cluster numbers k. Each cell of the matrix mat[i, j]
represents the proportion of all runs including samples i
and j
in which the two were clustered together.
A list with max_k
elements, the first of which is NULL
.
Elements two through max_k
are consensus matrices corresponding to
cluster numbers k = 2 through max_k
.
Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003). Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning, 52: 91-118.
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