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

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

Description

The function fits initial model parameters to specific observed cell event data. The function returns the cluster information of the fitted model in an object of class immunoClust.

Usage

cell.FitModel(x, data, B=50, tol=1e-5, bias=0.5, modelName="mvt" )

cell.Classify(x, data, modelName="mvt" )

Arguments

x	An immunoClust object with the initial model parameter (parameters, K, w, mu, sigma).
data	A numeric matrix, data frame of observations, or object of class flowFrame.
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

These functions are wrapper of the functions cell.EM and cell.Estimation, when model cluster parameters are combined in an object of class immunoClust and are used in the iterative cell event clustering process cell.process of immunoClust and are not intended to be called directly.

Value

The fitted model cluster 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.process, cell.EM, cell.Estimation

Examples

data(dat.fcs)
data(dat.exp)
r1 <- dat.exp[[1]]
dat.trans <- trans.ApplyToData(r1, dat.fcs)
r2 <- cell.FitModel(r1, dat.trans)

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