Description Usage Arguments Details Value Author(s) Examples
This package implement the clustering algorithm described by Alex Rodriguez and Alessandro Laio (2014) with improvements of automatic peak detection and parallel implementation
1 2 3 |
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. |
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.
a object of ClusterX
class
Chen Hao
1 2 3 4 5 6 7 8 9 10 11 12 | 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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.