meta.regnorm: immunoClust normalization procedure

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

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 [e-mail]

Examples

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

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