cluster {OMICsPCA} | R Documentation

## Cluster data points

### 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")

[Package OMICsPCA version 1.4.0 Index]