Get estimation of optimal clustering number

This function provides two measurements (i.e., clustering prediction index and Gap-statistics) and aims to search the optimal number for multi-omics integrative clustering. In short, the peaks reach by the red (CPI) and blue (Gap-statistics) lines should be referred to determine N.clust. Function adapted from package MOVICS (see ref)

getClustNum(
  data = NULL,
  is.binary = rep(FALSE, length(data)),
  try.N.clust = 2:8,
  center = TRUE,
  scale = TRUE
)

Arguments

data

List of matrices.

is.binary

A logical vector to indicate if the subdata is binary matrix of 0 and 1 such as mutation.

try.N.clust

A integer vector to indicate possible choices of number of clusters.

center

A logical value to indicate if the variables should be centered. TRUE by default.

scale

A logical value to indicate if the variables should be scaled. FALSE by default.

Value

A figure that helps to choose the optimal clustering number (argument of N.clust), and a list containing the cluster number of identified by clustering prediction index (CPI) and Gap statistics

References

Chalise P, Fridley BL (2017). Integrative clustering of multi-level omic data based on non-negative matrix factorization algorithm. PLoS One, 12(5):e0176278. Tibshirani, R., Walther, G., Hastie, T. (2001). Estimating the number of data clusters via the Gap statistic. J R Stat Soc Series B Stat Methodol, 63(2):411-423. Lu, X., et al. (2020). MOVICS: an R package for multi-omics integration and visualization in cancer subtyping. Bioinformatics, 36(22-23), 5539–5541.

See also

Other Multi-omic integration: integrate_with_DIABLO(), integrate_with_MBPLS(), integrate_with_MEFISTO(), integrate_with_MOFA(), integrate_with_iCluster(), integrate_with_sMBPLS(), vertical_integration()

Examples

# There is no example and please refer to vignette.