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

Description Usage Arguments Details Value Author(s) Examples

View source: R/ClusterX.R

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

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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

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

raman91/cytofkit2 documentation built on Dec. 22, 2021, 12:54 p.m.