meta.normalize {immunoClust} | R Documentation

## immunoClust normalization step with the meta.clustering process

### Description

Performs a normalization via linear regression of the cell-cluster samples to the meta-clustering model.

### Usage

    meta.Normalize(P, N, K, W, M, S, G, Z, method=3)

### Arguments

`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.
`W` | The totK-dimensional vector with weights, i.e. number of events, 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.
`G` | The number of meta-clusters.
`Z` | The totK x G-dimensional matrix with the A-Posterior probabilities for a cell-cluster belonging to a meta-cluster.
`method` | Alternative methods used for the normalization routine. Let Y denote the consensus meta-model build from all cell-event clusters of all experiments using the A-posterior Z and X the cell-event clusters in each experiment. 0 = no normalization 1 = Y = a x X 2 = Y = a x X + b 3 = X = a x Y 4 = X = a x Y + b

### Details

The regression used the cell-cluster and meta-cluster means weighted by the probabilities for a cell-cluster belonging to the meta-cluster. It builds a consensus meta-model from all cell-clusters using the A-posterior probabilities Z.

### 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.

W

  The totK-dimensional vector with weights, i.e. number of events, 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.

### Author(s)

Till Sörensen till-antoni.soerensen@charite.de

### See Also

`meta.process`, `meta.Clustering`

### Examples

    data(dat.meta)
    dat <- dat.meta$dat.clusters
    res <- dat.meta$res.clusters
    dat.norm <- meta.Normalize(dat$P, dat$N, dat$K, dat$clsEvents,
        dat$M, dat$S, res@K, res@z)

[Package immunoClust version 1.12.0 Index]