R: Consensus Clustering and Proportion of Ambiguously Clustered...

consensus_cluster {ClustAssess} | R Documentation

## Consensus Clustering and Proportion of Ambiguously Clustered Pairs

### Description

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)
    pac_convergence(pac.res, k_plot=c(3,5,7,9))

[Package ClustAssess version 0.3.0 Index]