PermBiclust.sigclust_stop: 'SCBiclust' method for identifying means-based biclusters...

In SCBiclust: Identifies Mean, Variance, and Hierarchically Clustered Biclusters

'SCBiclust' method for identifying means-based biclusters with optional cluster significance testing

Description

'SCBiclust' method for identifying means-based biclusters with optional cluster significance testing

Usage

PermBiclust.sigclust_stop(
  x,
  nperms = 1000,
  silent = TRUE,
  maxnum.bicluster = 5,
  alpha = 0.05,
  icovest = 1,
  sc = TRUE
)

Arguments

x	a dataset with n rows and p columns, with observations in rows.
nperms	number of Beta(\frac{1}{2}, (p-1)/2) distributed variables generated for each feature (default=1000)
silent	should progress be printed? (default=TRUE)
maxnum.bicluster	The maximum number of biclusters returned
alpha	significance level for sigclust test.
icovest	Coviariance estimation type for sigclust test
sc	should the sigclust test be used? (default=TRUE)

Details

Observations in the bicluster are identified such that they maximize the feature-weighted between cluster sum of squares. Features in the bicluster are identified based on their contribution to the clustering of the observations. Feature weights are generated in a similar fashion as KMeansSparseCluster except with a modified objective function and no sparsity constraint. This algoritm uses a numerical approximation to E(√{B}) where B \sim Beta(\frac{1}{2}, (p-1)/2) as the expected null distribution for feature weights. Use of the sigclust algorithm to test the strength of the identified clusters is optional in this implementation of the algorithm.

Value

The function returns a S3-object with the following attributes:

• num.bicluster: The number of biclusters estimated by the procedure.

• x.residual: The data matrix x after removing the signals

• which.x: A list of length num.bicluster with each list entry containing a logical vector denoting if the data observation is in the given bicluster.

• which.y: A list of length num.bicluster with each list entry containing a logical vector denoting if the data feature is in the given bicluster.

Author(s)

Erika S. Helgeson, Qian Liu, Guanhua Chen, Michael R. Kosorok , and Eric Bair

Examples

test <- matrix(rnorm(60*180), nrow=60, ncol=180)
test[1:15,1:15] <- test[1:15,1:15]+rnorm(15*15, 2)
test[16:30,51:80] <- test[16:30,51:80]+rnorm(15*30, 3)
PermBiclust.sigclust_stop(test, silent=TRUE)

SCBiclust documentation built on June 10, 2022, 1:06 a.m.