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Last data update: 2014.03.03

R: Model Based Clustering of Data for a pre-defined Number of...

cell.ClustData

R Documentation

Model Based Clustering of Data for a pre-defined Number of Clusters

Description

Performs EM-iteration on cell events, where an initial event cluster membership is obtained by hierarchical clustering on a sample subset given a number of clusters.

Usage

cell.ClustData(data, K, parameters=NULL, expName="immunoClust Experiment", 
                sample.seed=1, sample.number=1500, sample.standardize=TRUE,
                B=50, tol=1e-5, modelName="mvt")

Arguments

`data`	A numeric matrix, data frame of observations, or object of class flowFrame. Rows correspond to observations and columns correspond to measured parameters.
`K`	Given number of clusters for the final model.
`parameters`	A character vector specifying the parameters (columns) to be included in clustering. When it is left unspecified, all the parameters will be used.
`expName`	The name of the clustering experiment.
`sample.seed`	The seed integer for the random number generator.
`sample.number`	The maximum number of samples used for initial hierarchical clustering.
`sample.standardize`	A numeric indicating whether the samples for hierarchical clustering are standardized (mean=0, SD=1).
`B`	The maximum number of EM-iterations.
`tol`	The tolerance used to assess the convergence of the EM-algorithm.
`modelName`	Used mixture model; either `"mvt"` for a t-mixture model or `"mvn"` for a Gaussian Mixture model.

Details

Although this function provides the possiblity to cluster an abitrary set of observed data into a fixed number of clusters, this function is used in the immunoClust-pipeline only for the calculation of the initial model with one cluster.

Value

The fitted model cluster information in an object of class immunoClust.

Author(s)

Till SÃ¶rensen till-antoni.soerensen@charite.de

References

SÃ¶rensen, T., Baumgart, S., Durek, P., GrÃ¼tzkau, A. and HÃ¤upl, T. immunoClust - an automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A (accepted).

Examples

data(dat.fcs)
res <- cell.ClustData(dat.fcs, parameters=c("FSC-A", "SSC-A"), 5)
summary(res)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(immunoClust)
Loading required package: grid
Loading required package: lattice
Loading required package: flowCore
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/immunoClust/cell.ClustData.Rd_%03d_medium.png", width=480, height=480)
> ### Name: cell.ClustData
> ### Title: Model Based Clustering of Data for a pre-defined Number of
> ###   Clusters
> ### Aliases: cell.ClustData
> ### Keywords: cluster
> 
> ### ** Examples
> 
> data(dat.fcs)
> res <- cell.ClustData(dat.fcs, parameters=c("FSC-A", "SSC-A"), 5)
> summary(res)
** Experiment Information ** 
Experiment name: immunoClust Experiment 
Data Filename:    
Parameters:   FSC-A SSC-A 
Description:  

** Data Information ** 
Number of observations: 10000 
Number of parameters:   2 
Removed observations:   0 (0%)

** Transformation Information ** 
htrans-A:    
htrans-B:    
htrans-decade:   -1 

** Clustering Summary ** 
Number of clusters: 5 
Cluster     Proportion  Observations
       1      0.333431          2978
       2      0.189187          1934
       3      0.097432           995
       4      0.348150          3775
       5      0.031800           318

    Min.      0.031800           318
    Max.      0.348150          3775

** Information Criteria ** 
Log likelihood: -225553.5 -227573.7 -145333.3 
BIC: -225553.5 
ICL: -227573.7 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>