ClusterExperiment-methods: Helper methods for the ClusterExperiment class

In epurdom/clusterExperiment: Compare Clusterings for Single-Cell Sequencing

Helper methods for the ClusterExperiment class

Description

This is a collection of helper methods for the ClusterExperiment class.

Usage

## S4 method for signature 'ClusterExperiment'
show(object)

## S4 method for signature 'ClusterExperiment'
transformation(x)

## S4 replacement method for signature 'ClusterExperiment,function'
transformation(object) <- value

## S4 method for signature 'ClusterExperiment'
nClusterings(x)

## S4 method for signature 'ClusterExperiment'
nClusters(x, ignoreUnassigned = TRUE)

## S4 method for signature 'ClusterExperiment'
nFeatures(x)

## S4 method for signature 'ClusterExperiment'
nSamples(x)

## S4 method for signature 'ClusterExperiment'
clusterMatrixNamed(x, whichClusters = "all")

## S4 method for signature 'ClusterExperiment'
clusterMatrixColors(x, whichClusters = "all")

## S4 method for signature 'ClusterExperiment'
clusterMatrix(x, whichClusters)

## S4 method for signature 'ClusterExperiment'
primaryCluster(x)

## S4 method for signature 'ClusterExperiment'
primaryClusterIndex(x)

## S4 method for signature 'ClusterExperiment'
primaryClusterLabel(x)

## S4 method for signature 'ClusterExperiment'
primaryClusterNamed(x)

## S4 method for signature 'ClusterExperiment'
primaryClusterType(x)

## S4 replacement method for signature 'ClusterExperiment,numeric'
primaryClusterIndex(object) <- value

## S4 method for signature 'ClusterExperiment'
dendroClusterIndex(x)

## S4 method for signature 'ClusterExperiment'
coClustering(x)

## S4 replacement method for signature 'ClusterExperiment,matrix'
coClustering(object) <- value

## S4 replacement method for signature 'ClusterExperiment,dsCMatrix'
coClustering(object) <- value

## S4 replacement method for signature 'ClusterExperiment,numeric'
coClustering(object) <- value

## S4 method for signature 'ClusterExperiment'
clusterTypes(x)

## S4 method for signature 'ClusterExperiment'
clusteringInfo(x)

## S4 method for signature 'ClusterExperiment'
clusterLabels(x)

## S4 replacement method for signature 'ClusterExperiment,character'
clusterLabels(object) <- value

## S4 method for signature 'ClusterExperiment'
clusterLegend(x)

## S4 replacement method for signature 'ClusterExperiment,list'
clusterLegend(object) <- value

## S4 method for signature 'ClusterExperiment'
orderSamples(x)

## S4 replacement method for signature 'ClusterExperiment,numeric'
orderSamples(object) <- value

## S4 replacement method for signature 'ClusterExperiment,character'
clusterTypes(object) <- value

## S4 method for signature 'ClusterExperiment'
addToColData(object, ...)

## S4 method for signature 'ClusterExperiment'
colDataClusters(
  object,
  whichClusters = "primary",
  useNames = TRUE,
  makeFactor = TRUE,
  ...
)

Arguments

x, object	a ClusterExperiment object.
value	The value to be substituted in the corresponding slot. See the slot descriptions in ClusterExperiment for details on what objects may be passed to these functions.
ignoreUnassigned	logical. If true, ignore the clusters with -1 or -2 assignments in calculating the number of clusters per clustering.
whichClusters	argument that can be either numeric or character vector indicating the clusterings to be used. See details of getClusterIndex.
...	For addToColData, arguments passed to colDataClusters.
useNames	for tableClusters, whether the output should be tabled with names (useNames=TRUE) or ids (useNames=FALSE)
makeFactor	logical for colDataClusters. If TRUE the clustering will be added to the colData slot as a factor. If FALSE, the clustering will be added to the colData slot as a character vector if useNames=TRUE and as a numeric vector if useNames=FALSE.

Details

Note that redefining the transformation function via transformation(x)<- will check the validity of the transformation on the data assay. If the assay is large, this may be time consuming. Consider using a call to ClusterExperiment, which has the option as to whether to check the validity of the transformation.

Value

transformation prints the function used to transform the data prior to clustering.

nClusterings returns the number of clusterings (i.e., ncol of clusterMatrix).

nClusters returns the number of clusters per clustering

nFeatures returns the number of features (same as 'nrow').

nSamples returns the number of samples (same as 'ncol').

clusterMatrixNamed returns a matrix with cluster labels.

clusterMatrixColors returns the matrix with all the clusterings, using the internally stored colors for each cluster

clusterMatrix returns the matrix with all the clusterings.

primaryCluster returns the primary clustering (as numeric).

primaryClusterIndex returns/sets the primary clustering index (i.e., which column of clusterMatrix corresponds to the primary clustering).

primaryClusterIndex returns/sets the primary clustering index (i.e., which column of clusterMatrix corresponds to the primary clustering).

primaryClusterNamed returns the primary cluster (using cluster labels).

primaryClusterIndex returns/sets the primary clustering index (i.e., which column of clusterMatrix corresponds to the primary clustering).

dendroClusterIndex returns/sets the clustering index of the clusters used to create dendrogram (i.e., which column of clusterMatrix corresponds to the clustering).

coClustering returns/sets the co-clustering matrix.

clusterTypes returns/sets the clusterTypes slot.

clusteringInfo returns the clusterInfo slot.

clusterLabels returns/sets the column names of the clusterMatrix slot.

clusterLegend returns/sets the clusterLegend slot.

orderSamples returns/sets the orderSamples slot.

addToColData returns a ClusterExperiment object with the clusterings in clusterMatrix slot added to the colData slot

colDataClusters returns a DataFrame object that has the clusterings in clusterMatrix slot added to the DataFrame in the colData slot

Examples

# load data:
data(rsecFluidigm)
show(rsecFluidigm)
#Number of clusterings
nClusterings(rsecFluidigm)
# Number of clusters per clustering
nClusters(rsecFluidigm)
# Number of features/samples
nSamples(rsecFluidigm)
nFeatures(rsecFluidigm)
# retrieve all clustering assignments
# (either as cluster ids, cluster names or cluster colors)
head(clusterMatrix(rsecFluidigm)[,1:5])
head(clusterMatrixNamed(rsecFluidigm)[,1:5])
head(clusterMatrixColors(rsecFluidigm)[,1:5])
# clustering Types/Labels
clusterTypes(rsecFluidigm)
clusterLabels(rsecFluidigm)
# Add a clustering assignment to the colData of the object
# (useful if working with function that relies on colData)
colData(rsecFluidigm)
test<-addToColData(rsecFluidigm,whichCluster="primary")
colData(test)

epurdom/clusterExperiment documentation built on April 28, 2024, 8:17 p.m.