cell.ClustData {immunoClust} | R Documentation |
Performs EM-iteration on cell events, where an initial event cluster membership is obtained by hierarchical clustering on a sample subset given a number of clusters.
cell.ClustData(data, K, parameters=NULL, expName="immunoClust Experiment", sample.number=1500, sample.standardize=TRUE, B=50, tol=1e-5, modelName="mvt")
data |
A numeric matrix, data frame of observations, or object of class flowFrame. Rows correspond to observations and columns correspond to measured parameters. |
K |
Given number of clusters for the final model. |
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. |
sample.number |
The maximum 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). |
B |
The maximum number of EM-iterations. |
tol |
The tolerance used to assess the convergence of the EM-algorithm. |
modelName |
Used mixture model; either |
Although this function provides the possiblity to cluster an abitrary set of observed data into a fixed number of clusters, this function is used in the immunoClust-pipeline only for the calculation of the initial model with one cluster.
The fitted model cluster 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).
immunoClust-object
, cell.hclust
data(dat.fcs) res <- cell.ClustData(dat.fcs, parameters=c("FSC-A", "SSC-A"), 5) summary(res)