Uses the SigClust K-Means algorithm to assess significance of clustering results.
Arguments
- x
data matrix, samples are rows and features are columns
- k
cluster size to test against
- nsim
number of simulations
- nrep
See
sigclust::sigclust()for details.- labflag
See
sigclust::sigclust()for details.- label
true class label. See
sigclust::sigclust()for details.- icovest
type of covariance matrix estimation
Value
An object of class sigclust. See sigclust::sigclust() for
details.
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.
References
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.
Author
Hanwen Huang: hanwenh@email.unc.edu; Yufeng Liu: yfliu@email.unc.edu; J. S. Marron: marron@email.unc.edu
Examples
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 0.76
#> ..@ pvalnorm : num 0.776
#> ..@ xcindex : num 0.667