cell.EM {immunoClust} | R Documentation |
Performs EMt-iteration on cell event observations giving initial model
parameters and returns the fitted clusters information in an object of class
immunoClust
.
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")
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 |
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.
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 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@parameters, # K=r@K, w=r@w,m=r@mu,s=r@sigma) r2 <- cell.EM(dat.trans, parameters(r), K=ncls(r), w=weights(r),m=mu(r),s=sigma(r)) summary(r2)