Consensus Clustering and Proportion of Ambiguously Clustered Pairs

Calculate consensus clustering and proportion of ambiguously clustered pairs (PAC) with hierarchical clustering.

Usage

consensus_cluster(
  x,
  k_min = 3,
  k_max = 100,
  n_reps = 100,
  p_sample = 0.8,
  p_feature = 1,
  p_minkowski = 2,
  dist_method = "euclidean",
  linkage = "complete",
  lower_lim = 0.1,
  upper_lim = 0.9,
  verbose = TRUE
)

Arguments

x

A samples x features normalized data matrix.

k_min

The minimum number of clusters calculated.

k_max

The maximum number of clusters calculated.

n_reps

The total number of subsamplings and reclusterings of the data; this value needs to be high enough to ensure PAC converges; convergence can be assessed with pac_convergence.

p_sample

The proportion of samples included in each subsample.

p_feature

The proportion of features included in each subsample.

p_minkowski

The power of the Minkowski distance.

dist_method

The distance measure for the distance matrix used in hclust; must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski".

linkage

The linkage method used in hclust; must be one of "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid"

lower_lim

The lower limit for determining whether a pair is clustered ambiguously; the lower this value, the higher the PAC.

upper_lim

The upper limit for determining whether a pair is clustered ambiguously; the higher this value, the higher the PAC.

verbose

Logical value used for choosing to display a progress bar or not.

Value

A data.frame with PAC values across iterations, as well as parameter values used when calling the method.

References

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(1), 91-118. https://doi.org/10.1023/A:1023949509487

Senbabaoglu, Y., Michailidis, G., & Li, J. Z. (2014). Critical limitations of consensus clustering in class discovery. Scientific reports, 4(1), 1-13. https://doi.org/10.1038/srep06207

Examples

pac.res <- consensus_cluster(iris[, 1:4], k_max = 20)
#> Calculating consensus clustering
pac_convergence(pac.res, k_plot = c(3, 5, 7, 9))