ClusterX: Fast clustering by automatic search and find of density peaks
In cytofkit: cytofkit: an integrated mass cytometry data analysis pipeline

Description Usage Arguments Details Value Author(s) Examples

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

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

`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.

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.

A list

Chen Hao

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

Loading required package: ggplot2
Loading required package: plyr
Warning message:
no DISPLAY variable so Tk is not available 
    Calculate cutoff distance...0.29  
    Calculate local Density...DONE!
    Detect nearest neighbour with higher density...DONE!
    Peak detection...DONE!
    Cluster assigning...DONE!