View source: R/jackstraw_kmeanspp.R
jackstraw_kmeanspp | R Documentation |
Test the cluster membership for K-means clustering, using K-means++ initialization
jackstraw_kmeanspp( dat, kmeans.dat, s = NULL, B = NULL, center = TRUE, covariate = NULL, verbose = FALSE, pool = TRUE, ... )
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. By default, |
covariate |
a model matrix of covariates with |
verbose |
a logical specifying to print the computational progress. By default, |
pool |
a logical specifying to pool the null statistics across all clusters. By default, |
... |
optional arguments to control the k-means clustering algorithm (refers to |
K-means clustering assign m
rows into K
clusters. This function enable statistical
evaluation if the cluster membership is correctly assigned. Each of m
p-values refers to
the statistical test of that row with regard to its assigned cluster.
Its resampling strategy accounts for the over-fitting characteristics due to direct computation of clusters from the observed data
and protects against an anti-conservative bias.
Generally, it functions identical to jackstraw_kmeans
, but this uses ClusterR::KMeans_rcpp
instead of stats::kmeans
.
A speed improvement is gained by K-means++ initialization and RcppArmadillo
. If the input data is still too large,
consider using jackstraw_MiniBatchKmeans
.
The input data (dat
) must be of a class 'matrix'.
jackstraw_kmeanspp
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)) kmeans.dat <- KMeans_rcpp(dat, clusters = 10, num_init = 1, max_iters = 100, initializer = 'kmeans++') jackstraw.out <- jackstraw_kmeanspp(dat, kmeans.dat) ## End(Not run)
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