ClusterX {cytofkit} | R Documentation

## Fast clustering by automatic search and find of density peaks

### Description

This package implements 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 reduction method.
`outDim` | Number of dimensions used for clustering.
`dc` | Distance cutoff value.
`gaussian` | If `TRUE`, apply gaussian to estimate the density.
`alpha` | Significance level for peak detection.
`detectHalos` | If `TRUE`, detect the halos.
`SVMhalos` | If `TRUE`, Run SVM on cores to assign halos.
`parallel` | If `TRUE`, run the algorithm in parallel.
`nCore` | Number of cores umployed for parallel compution.

### Details

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

### Value

A list

### Author(s)

Chen Hao

### Examples

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

[Package cytofkit version 1.12.0 Index]