View source: R/jackstraw_pam.R
jackstraw_pam | R Documentation |
Test the cluster membership for Partitioning Around Medoids (PAM)
jackstraw_pam( dat, pam.dat, s = NULL, B = NULL, center = TRUE, covariate = NULL, verbose = FALSE, pool = TRUE, ... )
dat |
a matrix with |
pam.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 |
PAM assigns 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.
For a large dataset, PAM could be too slow. Consider using cluster::clara
and jackstraw::jackstraw_clara
.
The input data (dat
) must be of a class 'matrix'.
jackstraw_pam
returns a list consisting of
F.obs |
|
F.null |
F null statistics between null variables and cluster medoids, 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(cluster) dat = t(scale(t(Jurkat293T), center=TRUE, scale=FALSE)) pam.dat <- pam(dat, k=2) jackstraw.out <- jackstraw_pam(dat, pam.dat = pam.dat) ## End(Not run)
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