cluster: Cluster data points

In subhadeep1024/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, 768–769.

Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100–108. 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, 128–137.

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. 281–297. 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")

subhadeep1024/OMICsPCA documentation built on March 17, 2020, 5:23 p.m.