ClusterX: Fast clustering by automaticly search and find of density...

In JinmiaoChenLab/ClusterX: Fast clustering by automaticly 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	If apply SVM model from cores to assign halos.
parallel	If 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 of high diemnsional, t-SNE is conducted to reduce the dimensionality.

Value

a object of ClusterX class

Author(s)

Chen Hao

Examples

dir <- system.file("extdata", package = "ClusterX")
r15 <- read.table(paste(dir, "R15.txt", sep = .Platform$file.sep), header = FALSE)
r15_c <- ClusterX(r15[,c(1,2)])
clusterPlot(r15_c)
densityPlot(r15_c)
peakPlot(r15_c)

d31 <- read.table(paste(dir, "D31.txt", sep = .Platform$file.sep), header = FALSE)
d31_c <- ClusterX(d31[,c(1,2)])
clusterPlot(d31_c)
densityPlot(d31_c)
peakPlot(d31_c)

JinmiaoChenLab/ClusterX documentation built on May 7, 2019, 10:52 a.m.