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]