Proportional sample exposures will be used as input to perform clustering.
cluster_exposure( result, nclust, proportional = TRUE, method = "kmeans", dis.method = "euclidean", hc.method = "ward.D", clara.samples = 5, iter.max = 10, tol = 1e-15 )
result | A |
---|---|
nclust | Pre-defined number of clusters. |
proportional | Logical, indicating if proportional exposure (default) will be used for clustering. |
method | Clustering algorithms. Options are "kmeans" (K-means), "hkmeans" (hybrid of hierarchical K-means), "hclust" (hierarchical clustering), "pam" (PAM), and "clara" (Clara). |
dis.method | Methods to calculate dissimilarity matrix. Options are "euclidean" (default), "manhattan", "jaccard", "cosine", and "canberra". |
hc.method | Methods to perform hierarchical clustering. Options are "ward.D" (default), "ward.D2", "single", "complete", "average", "mcquitty", "median", and "centroid". |
clara.samples | Number of samples to be drawn from dataset. Only used when "clara" is selected. Default is 5. |
iter.max | Maximum number of iterations for k-means clustering. |
tol | Tolerance level for kmeans clustering level iterations |
A one-column data frame with sample IDs as row names and cluster number for each sample.
set.seed(123) data(res_annot) clust_out <- cluster_exposure(res_annot, nclust = 2) #> Metric: 'euclidean'; comparing: 7 vectors. #> Warning: FANNY algorithm has not converged in 'maxit' = 10 iterations