cell.SubClustering: immunoClust Model Refinement Step in iterative Cell-events...
In immunoClust: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry

Description Usage Arguments Details Value Author(s) References See Also Examples

These function tests each cell-cluster of a model for refining it into more sub-clusters and returns the refined model parameter in an object of class immunoClust.

cell.SubClustering( x, dat, B=50, tol=1e-5, thres=0.1, bias=0.5,
                    sample.weights=1, sample.EM="MEt",
                    sample.number=1500, sample.standardize=TRUE,
                    extract.thres=0.8, modelName="mvt")

cell.TestSubCluster(x, y, t, cluster, J=8, B=500, tol=1e-5, bias=0.5,
                    sample.EM="MEt", sample.df=5, sample.number=1500, 
                    sample.standardize=TRUE, modelName="mvt")

`x`	An immunoClust object with the initial model parameter (K, w, mu, sigma).
`dat`	A numeric matrix, data frame of observations, or object of class flowFrame.
`B`	The maximum number of EM(t)-iterations in Sub-Clustering.
`tol`	The tolerance used to assess the convergence of the EM(t)-algorithms in Sub-Clustering.
`thres`	Defines the threshold, below which an ICL-increase is meaningless. The threshold is given as the multiple (or fraction) of the costs for a single cluster.
`bias`	The ICL-bias used in the EMt-algorithm.
`sample.weights`	Power of weights applied to hierarchical clustering, where the used weights are the probabilities of cluster membership.
`sample.EM`	Used EM-algorithm; either `"MEt"` for EMt-iteration or `"ME"` for EM-iteration without test step.
`sample.number`	The number of samples used for initial hierarchical clustering.
`sample.standardize`	A numeric indicating whether the samples for hierarchical clustering are standardized (mean=0, SD=1).
`extract.thres`	The threshold used for cluster data extraction.
`modelName`	Used mixture model; either `mvt` for a t-mixture model or `mvn` for a Gaussian Mixture model.
`y`	A numeric matrix of the observations beloning to the particular cluster.
`t`	A numeric vector with the probability weights for the observations belonining to the particular cluster.
`cluster`	An integer index of the particular cluster
`J`	The number of sub-models to be builded and tested for a particular cluster.
`sample.df`	Degree of freedom for the t-distibutions in a t-mixture model. Has to be 5 in immunoClust.

These function are used internally by the cell-clustering procedures of cell.process in immunoClust and are not intended to be used directly.

The cluster parameters of the refined model 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).

cell.process, cell.hclust

data(dat.fcs)
data(dat.exp)
dat.trans <- trans.ApplyToData(dat.exp[[1]], dat.fcs)
#need to re-calculate the cluster membership probabilities
# not stored in dat.exp
r1 <- cell.Classify(dat.exp[[1]], dat.trans)
summary(r1)
r2 <- cell.SubClustering(r1, dat.trans)
summary(r2)