meta.normalize: immunoClust normalization step with the meta.clustering...

In immunoClust: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry

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 [e-mail]

See Also

meta.process, meta.Clustering

Examples

data(dat.meta)
#dat <- dat.meta$dat.clusters
res <- dat.meta$res.clusters
dat.norm <- meta.Normalize(npar(dat.meta), nsam(dat.meta), 
    sam_ncls(dat.meta), sam_clsEvents(dat.meta), sam_clsMu(dat.meta), 
    sam_clsSigma(dat.meta), ncls(res), posterior(res))

immunoClust documentation built on Nov. 8, 2020, 5:19 p.m.