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.

### 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)

### 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.

### Value

The function returns a `immunoMeta` 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.

### 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

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

### Examples

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

[Package immunoClust version 1.19.4 Index]