View source: R/jackstraw_MiniBatchKmeans.R
jackstraw_MiniBatchKmeans  R Documentation 
Test the cluster membership for Kmeans clustering
jackstraw_MiniBatchKmeans(
dat,
MiniBatchKmeans.output = NULL,
s = NULL,
B = NULL,
center = TRUE,
covariate = NULL,
verbose = FALSE,
batch_size = floor(nrow(dat)/100),
initializer = "kmeans++",
pool = TRUE,
...
)
dat 
a data matrix with 
MiniBatchKmeans.output 
an output from applying 
s 
a number of “synthetic” null variables. Out of 
B 
a number of resampling iterations. 
center 
a logical specifying to center the rows. By default, 
covariate 
a model matrix of covariates with 
verbose 
a logical specifying to print the computational progress. By default, 
batch_size 
the size of the mini batches. 
initializer 
the method of initialization. By default, 
pool 
a logical specifying to pool the null statistics across all clusters. By default, 
... 
optional arguments to control the Mini Batch Kmeans clustering algorithm (refers to 
Kmeans clustering assign m
rows into K
clusters. This function enable statistical
evaluation if the cluster membership is correctly assigned. Each of m
pvalues refers to
the statistical test of that row with regard to its assigned cluster.
Its resampling strategy accounts for the overfitting characteristics due to direct computation of clusters from the observed data
and protects against an anticonservative bias.
jackstraw_MiniBatchKmeans
returns a list consisting of
F.obs 

F.null 
F null statistics between null variables and cluster centers, from the jackstraw method. 
p.F 

Neo Christopher Chung nchchung@gmail.com
Chung (2020) Statistical significance of cluster membership for unsupervised evaluation of cell identities. Bioinformatics, 36(10): 3107–3114 https://academic.oup.com/bioinformatics/article/36/10/3107/5788523
## Not run:
library(ClusterR)
dat = t(scale(t(Jurkat293T), center=TRUE, scale=FALSE))
MiniBatchKmeans.output < MiniBatchKmeans(data=dat, clusters = 2, batch_size = 300,
initializer = "kmeans++")
jackstraw.output < jackstraw_MiniBatchKmeans(dat,
MiniBatchKmeans.output = MiniBatchKmeans.output)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.