methods.immunoMeta {immunoClust} | R Documentation

## Acessors and Methods for immunoMeta Objects

### 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 the`immunoClust`-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())`

clusterCoeff

  Calculates the bhattacharrya coefficients of clusters `cls` for a level `lvl` in the `immunoMeta`-object

  Usage:

  `ret <- clustersCoeff(immunoMeta, cls, lvl, par=seq_len(npar(immunoMeta))`

clusterDist

  Calculates the bhattacharrya distances of clusters `cls` for a level `lvl` in the `immunoMeta`-object

  Usage:

  `ret <- clustersDist(immunoMeta, cls, lvl, par=seq_len(npar(immunoMeta))`

clusterProb

  Calculates the bhattacharrya probabilities of clusters `cls` for a level `lvl` in the `immunoMeta`-object

  Usage:

  `ret <- clustersProb(immunoMeta, cls, lvl, par=seq_len(npar(immunoMeta))`

### Author(s)

Till Sörensen till-antoni.soerensen@charite.de

### 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

[Package immunoClust version 1.24.0 Index]