jackstraw_cluster: Jackstraw for the User-Defined Clustering Algorithm

In jackstraw: Statistical Inference for Unsupervised Learning

Jackstraw for the User-Defined Clustering Algorithm

Description

Test the cluster membership using a user-defined clustering algorithm

Usage

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,
  ...
)

Arguments

dat	a data matrix with m rows as variables and n columns as observations.
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 kmeans.
s	a number of “synthetic” null variables. Out of m variables, s variables are independently permuted.
B	a number of resampling iterations.
center	a logical specifying to center the rows. By default, TRUE.
noise	specify a parametric distribution to generate a noise term. If NULL, a non-parametric jackstraw test is performed.
covariate	a model matrix of covariates with n observations. Must include an intercept in the first column.
pool	a logical specifying to pool the null statistics across all clusters. By default, TRUE.
verbose	a logical specifying to print the computational progress. By default, FALSE.
...	additional, optional arguments to 'algorithm'.

Details

The clustering algorithms 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.

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.

Value

jackstraw_cluster returns a list consisting of

F.obs	m observed F statistics between variables and cluster centers.
F.null	F null statistics between null variables and cluster centers, from the jackstraw method.
p.F	m p-values of membership.

Author(s)

Neo Christopher Chung [e-mail]

References

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

jackstraw documentation built on Nov. 25, 2022, 5:06 p.m.