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Last data update: 2014.03.03

R: immunoClust EM-iteration on Cell-events given initial Cluster...

cell.ME

R Documentation

immunoClust EM-iteration on Cell-events given initial Cluster Membership Assignment

Description

Performs an EM-iteration on cell event observations given an initial cluster membership for the cell events and returns the fitted cluster information in an object of class immunoClust.

Usage

cell.ME(data, parameters=NULL, expName="immunoClust Experiment", 
        history=NULL, state=NULL, label, B=50, tol=1e-5, modelName="mvt")

Arguments

`data`	A numeric matrix, data frame of observations, or object of class flowFrame.
`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.
`history`	experimental; unused so far.
`state`	experimental: unused so far.
`label`	The N-dimensional vector containing the initial cluster membership. A label-number of 0 for an event indicates that this event is not initially assigned to a cluster.
`B`	The maximum number of EMt-iterations.
`tol`	The tolerance used to assess the convergence of the EMt-algorithms.
`modelName`	Used mixture model; either `"mvt"` or `"mvn"` for a t- or Gaussian mixture model respectively.

Value

The fitted clusters 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)
data(dat.exp)
## cell.clustering result for dat.fcs
r1 <- dat.exp[[1]]
summary(r1)
## apply model parameter to all (unfiltered) events
dat.trans <- trans.ApplyToData(r1, dat.fcs)
r2 <- cell.ME(dat.trans, parameters=r1@parameters, label=r1@label)
summary(r2)

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.ME.Rd_%03d_medium.png", width=480, height=480)
> ### Name: cell.ME
> ### Title: immunoClust EM-iteration on Cell-events given initial Cluster
> ###   Membership Assignment
> ### Aliases: cell.ME
> ### Keywords: cluster
> 
> ### ** Examples
> 
> data(dat.fcs)
> data(dat.exp)
> ## cell.clustering result for dat.fcs
> r1 <- dat.exp[[1]]
> summary(r1)
** Experiment Information ** 
Experiment name: 12543 
Data Filename:   fcs/12543.fcs 
Parameters:   FSC-A SSC-A FITC-A PE-A APC-A APC-Cy7-A Pacific Blue-A 
Description:  FCS SSC CD14 CD19 CD15 CD4 CD3 

** Data Information ** 
Number of observations: 10000 
Number of parameters:   7 
Removed from above:    318 (3.18%)
Removed from below:    0 (0%)

** Transformation Information ** 
htrans-A:   0.000000 0.000000 0.007202 0.004932 0.008136 0.015128 0.023041 
htrans-B:   0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 
htrans-decade:   -1 

** Clustering Summary ** 
Number of clusters: 13 
Cluster     Proportion  Observations
       1      0.037166           366
       2      0.054083           518
       3      0.001495            14
       4      0.005117            50
       5      0.040246           389
       6      0.035741           344
       7      0.015130           151
       8      0.007298            71
       9      0.114354          1107
      10      0.282377          2558
      11      0.007320            70
      12      0.014736           143
      13      0.384937          3901

    Min.      0.001495            14
    Max.      0.384937          3901

** Information Criteria ** 
Log likelihood: -254765.8 -256024.9 -174699.5 
BIC: -254765.8 
ICL: -256024.9 
> ## apply model parameter to all (unfiltered) events
> dat.trans <- trans.ApplyToData(r1, dat.fcs)
> r2 <- cell.ME(dat.trans, parameters=r1@parameters, label=r1@label)
> summary(r2)
** Experiment Information ** 
Experiment name: immunoClust Experiment 
Data Filename:    
Parameters:   FSC-A SSC-A FITC-A PE-A APC-A APC-Cy7-A Pacific Blue-A 
Description:  

** Data Information ** 
Number of observations: 10000 
Number of parameters:   7 
Removed observations:   318 (3.18%)

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

** Clustering Summary ** 
Number of clusters: 13 
Cluster     Proportion  Observations
       1      0.036992           367
       2      0.054499           518
       3      0.001380            13
       4      0.005126            50
       5      0.040249           389
       6      0.035694           345
       7      0.015258           151
       8      0.007058            70
       9      0.114449          1108
      10      0.270872          2433
      11      0.007317            70
      12      0.014696           143
      13      0.396410          4025

    Min.      0.001380            13
    Max.      0.396410          4025

** Information Criteria ** 
Log likelihood: -254768.3 -256012.1 -174686.7 
BIC: -254768.3 
ICL: -256012.1 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>