View source: R/SCBiclust_beta_ks.R
PermBiclust.beta.ks | R Documentation |
'SCBiclust' method for identifying means-based biclusters with Kolmogorov-Smirnov test of feature weights
PermBiclust.beta.ks( x, nperms = 1000, silent = TRUE, maxnum.bicluster = 5, ks.alpha = 0.05 )
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 |
ks.alpha |
significance level for Kolmogorov-Smirnov test. |
Observations in the bicluster are identified such that they maximize the feature-weighted square-root of the 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. The Kolmogorov-Smirnov test is used to assess if feature weights deviate from the expected null distribution.
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.
Erika S. Helgeson, Qian Liu, Guanhua Chen, Michael R. Kosorok , and Eric Bair
test <- matrix(rnorm(100*200), nrow=100, ncol=200) test[1:20,1:20] <- test[1:20,1:20]+rnorm(20*20, 2) test[16:30,51:80] <- test[16:30,51:80]+rnorm(15*30, 3) PermBiclust.beta.ks(test, silent=TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.