View source: R/jackstraw_cluster.R
jackstraw_cluster  R Documentation 
Test the cluster membership using a userdefined clustering algorithm
jackstraw_cluster(
dat,
k,
cluster,
centers,
algorithm = function(x, centers, ...) stats::kmeans(x, centers, ...),
s = 1,
B = 1000,
center = TRUE,
noise = NULL,
covariate = NULL,
pool = TRUE,
verbose = FALSE,
...
)
dat 
a data matrix with 
k 
a number of clusters. 
cluster 
a vector of cluster assignments. 
centers 
a matrix of all cluster centers. 
algorithm 
a clustering algorithm to use, where an output must include 'cluster' and 'centers'. For exact specification, see 
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, 
noise 
specify a parametric distribution to generate a noise term. If 
covariate 
a model matrix of covariates with 
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, 
... 
additional, optional arguments to 'algorithm'. 
The clustering algorithms 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 user is expected to explore the data with a given clustering algorithm and
determine the number of clusters k
.
Furthermore, provide cluster
and centers
as given by applying algorithm
onto dat
.
The rows of centers
correspond to k
clusters, as well as available levels in cluster
.
This function allows you to specify a parametric distribution of a noise term. It is an experimental feature.
jackstraw_cluster
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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.