Uses the SigClust K-Means algorithm to assess significance of clustering results.
sigclust(x, k, nsim, nrep = 1, labflag = 0, label = 0, icovest = 2)
| x | data matrix, samples are rows and features are columns |
|---|---|
| k | cluster size to test against |
| nsim | number of simulations |
| nrep | See |
| labflag | See |
| label | true class label. See |
| icovest | type of covariance matrix estimation |
An object of class sigclust. See sigclust::sigclust() for
details.
This function is a wrapper for the original sigclust::sigclust(), except
that an additional parameter k is allows testing against any number of
clusters. In addition, the default type of covariance estimation is also
different.
Liu, Yufeng, Hayes, David Neil, Nobel, Andrew and Marron, J. S, 2008, Statistical Significance of Clustering for High-Dimension, Low-Sample Size Data, Journal of the American Statistical Association 103(483) 1281--1293.
Hanwen Huang: hanwenh@email.unc.edu; Yufeng Liu: yfliu@email.unc.edu; J. S. Marron: marron@email.unc.edu
data(hgsc) dat <- hgsc[1:100, 1:50] nk <- 4 cc <- consensus_cluster(dat, nk = nk, reps = 5, algorithms = "pam", progress = FALSE) cl.mat <- consensus_combine(cc, element = "class") lab <- cl.mat$`4`[, 1] set.seed(1) str(sigclust(x = dat, k = nk, nsim = 50, labflag = 1, label = lab))#> Formal class 'sigclust' [package "sigclust"] with 10 slots #> ..@ raw.data : num [1:100, 1:50] -0.0107 -0.7107 0.8815 -1.0851 -0.9322 ... #> .. ..- attr(*, "dimnames")=List of 2 #> .. .. ..$ : chr [1:100] "TCGA.04.1331_PRO.C5" "TCGA.04.1332_MES.C1" "TCGA.04.1336_DIF.C4" "TCGA.04.1337_MES.C1" ... #> .. .. ..$ : chr [1:50] "ABAT" "ABHD2" "ACTB" "ACTR2" ... #> ..@ veigval : num [1:50] 11.81 4.51 2.66 2.29 1.84 ... #> ..@ vsimeigval: num [1:50] 11.81 4.51 2.66 2.29 1.84 ... #> ..@ simbackvar: num 0.42 #> ..@ icovest : num 2 #> ..@ nsim : num 50 #> ..@ simcindex : num [1:50] 0.647 0.673 0.65 0.652 0.59 ... #> ..@ pval : num 1 #> ..@ pvalnorm : num 1 #> ..@ xcindex : num 0.97