jackstraw_MiniBatchKmeans: Non-Parametric Jackstraw for Mini Batch K-means Clustering

In ncchung/jackstraw: Statistical Inference for Unsupervised Learning

Non-Parametric Jackstraw for Mini Batch K-means Clustering

Description

Test the cluster membership for K-means clustering

Usage

jackstraw_MiniBatchKmeans(
  dat,
  MiniBatchKmeans.output = NULL,
  s = NULL,
  B = NULL,
  center = TRUE,
  covariate = NULL,
  verbose = FALSE,
  batch_size = floor(nrow(dat)/100),
  initializer = "kmeans++",
  pool = TRUE,
  ...
)

Arguments

dat	a data matrix with m rows as variables and n columns as observations.
MiniBatchKmeans.output	an output from applying ClusterR::MiniBatchKmeans() onto dat. This provides more controls over the algorithm and subsequently the initial centroids used.
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.
covariate	a model matrix of covariates with n observations. Must include an intercept in the first column.
verbose	a logical specifying to print the computational progress. By default, FALSE.
batch_size	the size of the mini batches.
initializer	the method of initialization. By default, kmeans++.
pool	a logical specifying to pool the null statistics across all clusters. By default, TRUE.
...	optional arguments to control the Mini Batch K-means clustering algorithm (refers to ClusterR::MiniBatchKmeans).

Details

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.

Value

jackstraw_MiniBatchKmeans 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 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btaa087")}

Examples

## Not run: 
library(ClusterR)
dat = t(scale(t(Jurkat293T), center=TRUE, scale=FALSE))
MiniBatchKmeans.output <- MiniBatchKmeans(data=dat, clusters = 2, batch_size = 300,
initializer = "kmeans++")
jackstraw.output <- jackstraw_MiniBatchKmeans(dat,
MiniBatchKmeans.output = MiniBatchKmeans.output)

## End(Not run)

ncchung/jackstraw documentation built on Dec. 22, 2024, 8:33 p.m.