cell.EM: immunoClust EMt-iteration on Cell-events given initial Model...

In immunoClust: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry

Description

Performs EMt-iteration on cell event observations giving initial model parameters and returns the fitted clusters information in an object of class immunoClust.

Usage

cell.EM(data, parameters=NULL, expName="immunoClust Experiment", 
    history=NULL, state=NULL,
    K, w, m, s, B=50, tol=1e-5, bias=0.5, modelName="mvt")

cell.Estimation(data, parameters=NULL, expName="immunoClust Experiment",  
    history=NULL, state=NULL,
    K, w, m, s, 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.
K	The number of clusters.
w	The K-dimensional vector of the mixture proportions.
m	The K x P-dimensional matrix of the K estimated cluster means.
s	The K x P x P-dimensional matrix of the K estimated cluster covariance matrices.
B	The maximum number of EMt-iterations.
tol	The tolerance used to assess the convergence of the EMt-algorithms.
bias	The ICL-bias used in the EMt-algorithm.
modelName	Used mixture model; either "mvt" or "mvn" for a t- or Gaussian mixture model respectively.

Details

Whereas cell.EM performs a complete EMt-iteration, cell.Estimate only calculates the posterior probabilities and the Maximum-A-Posterior estimators of cluster membership for all events.

Value

The fitted clusters information in an object of class immunoClust.

Author(s)

Till Sörensen [e-mail]

References

Sörensen, T., Baumgart, S., Durek, P., Grützkau, A. and Häupl, T. immunoClust - an automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A (accepted).

See Also

cell.ME, cell.FitModel

Examples

data(dat.fcs)
data(dat.exp)
## cell.clustering result for dat.fcs
r <- dat.exp[[1]]
summary(r)
## apply model parameter to all (unfiltered) events
dat.trans <- trans.ApplyToData(r, dat.fcs)
r2 <- cell.EM(dat.trans, parameters(r), K=ncls(r), 
            w=weights(r),m=mu(r),s=sigma(r))
summary(r2)

immunoClust documentation built on Nov. 8, 2020, 5:19 p.m.