View source: R/jackstraw_kmeans.R
jackstraw_kmeans  R Documentation 
Test the cluster membership for Kmeans clustering
jackstraw_kmeans(
dat,
kmeans.dat,
s = NULL,
B = NULL,
center = FALSE,
covariate = NULL,
match = TRUE,
pool = TRUE,
verbose = FALSE,
...
)
dat 
a matrix with 
kmeans.dat 
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 of the null samples. By default, 
covariate 
a model matrix of covariates with 
match 
a logical specifying to match the observed clusters and jackstraw clusters using minimum Euclidean distances. 
pool 
a logical specifying to pool the null statistics across all clusters. By default, 
verbose 
a logical specifying to print the computational progress. By default, 
... 
optional arguments to control the 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.
The input data (dat
) must be of a class 'matrix'.
jackstraw_kmeans
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:
dat = t(scale(t(Jurkat293T), center=TRUE, scale=FALSE))
kmeans.dat < kmeans(dat, centers=2, nstart = 10, iter.max = 100)
jackstraw.out < jackstraw_kmeans(dat, kmeans.dat)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.