cytofkit (version 1.4.8)

ClusterX: Fast clustering by automatic search and find of density peaks

Description

This package implement the clustering algorithm described by Alex Rodriguez and Alessandro Laio (2014) with improvements of automatic peak detection and parallel implementation

Usage

ClusterX(data, dimReduction = NULL, outDim = 2, dc, gaussian = TRUE, alpha = 0.001, detectHalos = FALSE, SVMhalos = FALSE, parallel = FALSE, nCore = 4)

Arguments

data

A data matrix for clustering.

dimReduction

Dimenionality reduciton method.

outDim

Number of dimensions will be used for clustering.

dc

Distance cutoff value.

gaussian

If apply gaussian to esitmate the density.

alpha

Signance level for peak detection.

detectHalos

If detect the halos.

SVMhalos

Run SVM on cores to assign halos.

parallel

If run the algorithm in parallel.

nCore

Number of cores umployed for parallel compution.

Value

a list

Details

ClusterX works on low dimensional data analysis (Dimensionality less than 5). If input data is of high diemnsional, t-SNE is conducted to reduce the dimensionality.

Examples

iris_unique <- unique(iris) # Remove duplicates
data <- as.matrix(iris_unique[,1:4])
ClusterXRes <- ClusterX(data)