cell.ME {immunoClust} | R Documentation |
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
.
cell.ME(data, parameters=NULL, expName="immunoClust Experiment", history=NULL, state=NULL, label, B=50, tol=1e-5, modelName="mvt")
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 |
The fitted clusters information in an object of class
immunoClust
.
Till Sörensen till-antoni.soerensen@charite.de
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).
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)