meta.regnorm {immunoClust} | R Documentation

## immunoClust normalization procedure

### Description

Performs a normalization via linear regression of the sample clusters in x to the clusters in y.

### Usage

    meta.RegNorm(y, x, method=1, alpha=0.5)

### Arguments

`y` | immunoClust-object with the destination clusters.
`x` | immunoClust-object woth the cluster to normalize.
`method` | Alternative methods used for the normalization routine. 1 = X = a x Y 2 = X = a x Y + b
`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.

### Value

Returns the normalized cell-clusters means and co-variance matrices in a list-object with the following slots:

P

  The number of observed 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 totK = sum_{i=1}^K K_i.

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.

### Author(s)

Till Sörensen till-antoni.soerensen@charite.de

### Examples

    data(dat.meta)
    data(dat.exp)
    dat.norm <- meta.RegNorm(dat.meta$res.clusters, dat.exp[[1]])

[Package immunoClust version 1.19.4 Index]