immunoMeta.methods: Acessors and Methods for immunoMeta Objects

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

Description

Documentation of the accessors and methods for immunoMeta-objects

Arguments

object, immunoMeta	an object of class immunoMeta as return by meta.process.
cls	cluster subset for retrieved slot values.
par	parameter subset for retrieved slot values.
pos	Gives the position in the immunoMeta-hierarchy. pos is an array of indices which addresses the level of interest. Each level in the immunoMeta-hierarchy consists of a name (desc), meta-cluster subset (array of cluster indices) and a vector of sub-levels. pos is the sequence of indices into these sub-levels begining at root level.

Accessors

nsam

the number of immunoClust-objects (samples) which are co-clustered.

Usage:

nsam(immunoMeta)

sam_ncls

the number of cell event clusters in theimmunoClust-objects (samples) which are co-clustered.

Usage:

sam_ncls(immunoMeta, for.samples=seq_len(nsam(meta))

sam_clsWeights

the weigths of all cell event clusters which are collected for co-clustering.

Usage:

sam_clsWeights(immunoMeta)

sam_clsMu

the means of all cell event clusters which are collected for co-clustering.

Usage:

sam_clsMu(immunoMeta)

sam_clsSigma

the co-variance matrices of all cell event clusters which are collected for co-clustering.

Usage:

sam_clsSigma(immunoMeta)

sam_clsEvents

the event numbers of all cell event clusters which are collected for co-clustering.

Usage:

sam_clsEvents(immunoMeta)

nobj

the number of cell events clusters from sample cell-clustering which are co-clustered.

Usage:

nobj(immunoMeta)

ncls

the number of meta-clusters.

Usage:

ncls(immunoMeta)

npar

the number of parameters measured, cell-clustered and meta-clustered

Usage:

npar(immunoMeta)

parameters, parameters<-

extracts or replaces the names of measured, cell-clustered and meta-clustered parameters

Usage:

parameters(immunoMeta)

parameters(immunoMeta) <- value

label

the meta-clustering label, that is the assignment of the cell-clusters to the meta-clusters.

Usage:

label(immunoMeta, for.sample=NA)

If for.sample is specified, the label part for this sample only.

weights

the meta-clustering weights for the cluster selection (all meta-cluster by default)

Usage:

weights(immunoMets,cls=seq_len(ncls(immunoMeta)))

mu

the meta-cluster mean values for the cluster and parameter selection (all meta-cluster and all parameter by default)

Usage:

mu(immunoMeta, cls=seq_len(ncls(immunoMeta)), par=seq_len(npar(immunoMeta)))

sigma

the meta-cluster co-variance values for the cluster and parameter selection (all meta-cluster and all parameter by default)

Usage:

sigma(immunoMeta, cls=seq_len(ncls(immunoMeta)), par=seq_len(npar(immunoMeta)))

posterior

the posterior probabilities of cluster membership for each cell-cluster

Usage:

posterior(immunoMeta)

events

the cell-event numbers for each sample for the cluster selection (all meta-cluster by default)

Usage:

events(immunoMeta, ncls=seq_len(ncls(immunoMeta)))

prop, prop<-

get or a property value in the hierarchy level given by pos and named name

Usage:

prop(immunoMeta, name, pos=c())

prop(immunoMeta, name, pos, for.level=TRUE, for.sublevels=FALSE) <- value

If the option for.sublevels is set, the property value will by setted deep for all sub-levels of the by pos specified level.

The prop interface is very basic and no checks for meaningfull properties and values are performed. It could be used for everything at any time. Nevertheless, there are some property keys which are used internally mainly to control the plot routine for the levels.

desc the name of this level.

M the mean of all clusters in this level

S the co-variance meatirx of all clusters in this level

pscales a list of npar entries for the limits and ticksi information. Normaly, only set on root-level and then used for all sub-levels. But could set and altered at any level.

plot.subset an array of parameter indices used as default for the plot this level.

plot.color an index in the palette or other specified color used for plots of this level in its parent level.

plot.childs to be renamed in plot.levels.

plot.parent when set, additionally all cluster of the parent level are plotted in light gray.

desc, desc<-

Get or set the desc property in the by pos specified level.

Usage:

desc(immunoMeta, pos)

desc(immunoMeta, pos) <- value

descFull

Gives the full description path for the level given by pos, i.e. the concatinate desc values of this all parent levels.

Usage:

descFull(immunoMeta, pos)

level, level<-

Get or replace the level object at specified pos,

Usage:

value <- level(immunoMeta, pos)

level(immunoMeta, pos ) <- value

findLevel

Find the level pos value for a specific cluster cls

Usage:

pos <- findLevel(immunoMeta, cls)

clusters

Retrieves the cluster subset for the level at pos.

Usage:

cls <- clusters(immunoMeta, pos)

classified

Retrieves the cluster subset for the level at pos which are classified in sub-levels.

Usage:

cls <- classified(immunoMeta, pos)

unclassified

Retrieves the cluster subset for the level at pos which are not classified in sub-levels.

Usage:

cls <- unclassified(immunoMeta, pos)

Manipulators

addLevel<-

Adds a level at a specified hierarchy position pos. A level consists of a name (desc) and a cluster subset cls.

Usage:

addLevel(immunoMeta, pos, desc="new level") <- cls

move<-

Moves a cluster subset to a specific immunoMeta level. Clusters in cls are added to parent levels if nessesary and removed from other levels.

Usage:

move(immunoMeta, pos) <- cls

remove<-

removes a cluster subset from a specific immunoMeta level.

Usage:

remove(immunoMeta, pos) <- cls

parent<-

sets the parent for this level, or this level as parent for all its sub-levels

Usage:

parent(immunoMeta, pos) <- c()

parent(immunoMeta, pos) <- level

transfer<-

Overtakes the annotation of an immunoMeta-object to this immunoMeta-object

Usage:

transfer(immunoMeta) <- annotatedMeta

Methods

finalize

After manipulations of a immunoMeta-object finalize restructure all levels and returns the finalized object, where the parent relations are solved and the mean and co-variances of all levels are build.

Usage:

immunoMeta <- finalize(immunoMeta)

subset

Builds the immunoMeta-object for a cluster and/or parameter subset

Usage:

subsetMeta <- subset(immunoMeta, cls=seq_len(ncls(meta)), par=seq_len(npar(meta)))

transformParams

Scales and translates the cluster means of the immunoMeta-object in each parameter

Usage:

transformedMeta <- transformParams(immunoMeta, scale=c(), offset=c())

Author(s)

Till Sörensen [e-mail]

See Also

immunoMeta-object

Examples

###
data(dat.meta)
npar(dat.meta)
ncls(dat.meta)
cls <- clusters(dat.meta,c(1))
move(dat.meta,c(2)) <- cls

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