cluster: Cluster data points

In OMICsPCA: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples

Description

Clustering of data points using various algorithms

Usage

cluster(name, n = NULL, graph = NULL, choice, title = NULL, ...)

Arguments

name	name of the dataframe or matrix object containing the coordinates of data points. The output of "extract()" may be directly put here.
n	Number of clusters
graph	logical. Plots the clusterplot on first 2 dimensions if set TRUE
choice	Clustering algorithm to use. Available choices are: "density", "kmeans", "pam"
title	Title of the plot
...	additional non-conflicting arguments to cluster functions

Value

returns a list containing the cluster and plot information

Author(s)

Subhadeep Das

References

Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Institute for Computer Science, University of Munich. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96).

Forgy, E. W. (1965). Cluster analysis of multivariate data: efficiency vs interpretability of classifications. Biometrics, 21, 768<e2><80><93>769.

Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100<e2><80><93>108. doi: 10.2307/2346830.

Lloyd, S. P. (1957, 1982). Least squares quantization in PCM. Technical Note, Bell Laboratories. Published in 1982 in IEEE Transactions on Information Theory, 28, 128<e2><80><93>137.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, eds L. M. Le Cam & J. Neyman, 1, pp. 281<e2><80><93>297. Berkeley, CA: University of California Press.

Examples

exclude <- list(0,c(1,9))

int_PCA <- integrate_pca(Assays = c("H2az",
"H3k9ac"),
groupinfo = groupinfo,
name = multi_assay, mergetype = 2,
exclude = exclude, graph = FALSE)

name = int_PCA$int_PCA

data <- extract(name = name, PC = c(1:4),
groups = c("WE","RE"), integrated = TRUE,
rand = 300, groupinfo = groupinfo_ext)

clusters <- cluster(name = data, n = 2,
choice = "kmeans",
title = "kmeans on 2 clusters")

OMICsPCA documentation built on Nov. 8, 2020, 5:01 p.m.