meta.process {immunoClust}R Documentation

Meta-clustering of Cell-clusters in the immunoClust-pipeline

Description

This function performs iterative model based clustering on the clusters obtained by cell.process of several samples. Its input is a vector of the immunoClust-objects of the samples.

The function also performs in a secondary step an ordering of the meta-clusters according to their distribution in the scatter parameter and an automated gating process. These procedures are preliminary and not part of the presented algorithms of the reference.

Usage

meta.process(exp, dat.subset=c(), meta.iter=10, tol=1e-05, meta.bias=0.2,
            meta.alpha=.5, norm.method=0, norm.blur=2, norm.minG=10,
            scatter.subset=c(), scatter.bias=0.25, 
            scatter.prior=6)

Arguments

exp

A vector of list objects, each list contains the cell-clustering result of a sample in the res field. Addition fields are name and fsc containing the cell-sample name and fcs-filename, which are used for data output and plot routines.

dat.subset

A numeric vector defining the used observed parameters for the meta-clustering. If unset, all parameters in the cell-clustering results are used.

meta.iter

The number of major iterations.

tol

The tolerance used to assess the convergence of the EM(t)-algorithms.

meta.bias

The ICL-bias used in the EMt-iteration of the meta-clustering.

meta.alpha

A value between 0 and 1 used to balance the bhattacharrya probabilities calculated with either the full covariance matrices or using only the diagonal elements of it. When working with uncompensated FC data, very high correlations between parameters may be observed due to spill over. This leads to a very low bhattacharrya probability for two clusters even if they are located nearby. Using a mixture of the probabilities calculated with the complete covariance matrices and the variance information of each parameter avoids this problem. With a value of alpha=1, only the probabilities with complete covariance matrices are applied. A reasonable value for alpha is 0.5.

norm.method

A numeric selector for the normalization step to be performed during the major iteration.

norm.blur

The bluring constant by which the cell-clusters co-variance matrices are increased within the normalization step.

norm.minG

Minimum number of meta-clusters required before processing the normalization step.

scatter.subset

A numeric vector, giving the indices for the scatter parameter. If the scatter.subset is empty, scatter clustering was not performed.

scatter.bias

The ICL-bias used in EMt-iteration of scatter-clustering.

scatter.prior

experimental; gives the number of initial scatter regions for scatter clustering.

Value

The function returns a list-object with the following components:

dat.clusters A dat list-object of the cell event clusters used for meta-clustering.
res.clusters The immunoClust-object of the fitted meta-clustering mixture model.
dat.scatter A dat list-object of the scatter parameters for the cell event clusters used for scatter clustering.
res.scatter The immunoClust-object of the fitted scatter-clustering mixture model.
gating A list-object containing the hierarchical gating-tree.

The components of the dat list-objects are:

P The number of parameters for the cell event clusters.
N The number of cell-clustering experiments.
K The N-dimensional vector with the numbers of cell event clusters in each experiment. The total number of clusters is sum(K).
W The totK-dimensional vector with the mixture proportions of all clusters.
M The totK x P-dimensional matrix of all cluster means.
S The totK x P x P-dimensional matrix of all cluster covariance matrices.
expNames The N-dimensional character vector with the cell-clustering experiment names.
expEvents The N-dimensional vector with the numbers of events in each cell-clustering experiment.
clsEvents The totK-dimensional vector with the number of events in each cluster.
desc The P-dimensional character vector with the parameter description.

Author(s)

Till Sörensen till-antoni.soerensen@charite.de

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

immunoClust-object, meta.Clustering, meta.export, cell.process

Examples

data(dat.exp)
meta <- meta.process(dat.exp)
summary(meta$res.clusters)
tbl <- meta.numEvents(meta)

[Package immunoClust version 1.16.0 Index]