meta.ME {immunoClust} | R Documentation |
Performs an EM(t)-iteration on cell-clusters given an initial meta-cluster
membership for the cell-clusters and returns the fitted meta-clusters
information in an object of class
immunoClust
.
meta.ME(P, N, K, W, M, S, label, B=100, tol=1e-5, method=20, bias=0.25, alpha=0.5, min.class=0)
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. |
label |
The totK-dimension integer vector with the initial cell-cluster to meta-cluster membership. |
B |
The totK x P x P-dimensional matrix of all cluster covariance matrices. |
tol |
The tolerance used to assess the convergence of the EM(t)-algorithms. |
method |
0 = KL-minimization not weighted 1 = BC-maximization not weighted 10 = BC-maximization weighted 2 = EMt-classification not weighted 20 = EMt-classification weighted |
bias |
The ICL-bias used in the EMt-iteration of the meta-clustering. |
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. |
min.class |
The minimum number of clusters for the final model. |
This function is used internally by the meta-clustering procedures
meta.process
and meta.Clustering
in
immunoClust.
The fitted meta-clusters information in an object of class
immunoClust
.
Till Sörensen till-antoni.soerensen@charite.de
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).
data(dat.exp) d <- meta.exprs(dat.exp) r <- meta.ME(d$P, d$N, d$K, d$clsEvents, d$M, d$S, label=rep(1,sum(d$K)))