SCE-accessors {CATALYST}

R Documentation

SingleCellExperiment accessors

Description

Various wrappers to conviniently access slots in a SingleCellExperiment created with prepData, and that are used frequently during differential analysis.

Usage

## S4 method for signature 'SingleCellExperiment'
ei(x)

## S4 method for signature 'SingleCellExperiment'
n_cells(x)

## S4 method for signature 'SingleCellExperiment'
channels(x)

## S4 method for signature 'SingleCellExperiment'
marker_classes(x)

## S4 method for signature 'SingleCellExperiment'
type_markers(x)

## S4 method for signature 'SingleCellExperiment'
state_markers(x)

## S4 method for signature 'SingleCellExperiment'
sample_ids(x)

## S4 method for signature 'SingleCellExperiment,missing'
cluster_ids(x, k = NULL)

## S4 method for signature 'SingleCellExperiment,character'
cluster_ids(x, k = NULL)

## S4 method for signature 'SingleCellExperiment'
cluster_codes(x)

## S4 method for signature 'SingleCellExperiment'
delta_area(x)

Arguments

x	a SingleCellExperiment.
k	character string specifying the clustering to extract. Valid values are names(cluster_codes(x)).

Value

ei

extracts the experimental design table.

n_cells

extracts the number of events measured per sample.

channels

extracts the original FCS file's channel names.

marker_classes

extracts marker class assignments ("type", "state", "none").

type_markers

extracts the antigens used for clustering.

state_markers

extracts antigens that were not used for clustering.

sample_ids

extracts the sample IDs as specified in the metadata-table.

cluster_ids

extracts the numeric vector of cluster IDs as inferred by FlowSOM.

cluster_codes

extracts a data.frame containing cluster codes for the FlowSOM clustering, the ConsensusClusterPlus metaclustering, and all mergings done through mergeClusters.

delta_area

extracts the delta area plot stored in the SCE's metadata by cluster

Author(s)

Helena L Crowell helena.crowell@uzh.ch

Examples

# construct SCE & run clustering
data(PBMC_fs, PBMC_panel, PBMC_md)
sce <- prepData(PBMC_fs, PBMC_panel, PBMC_md)
sce <- cluster(sce)

# view experimental design table
ei(sce)

# quick-access sample & cluster assignments
plot(table(sample_ids(sce)))
plot(table(cluster_ids(sce)))

# access specific clustering resolution
table(cluster_ids(sce, k = "meta8"))

# access marker information
channels(sce)
marker_classes(sce)
type_markers(sce)
state_markers(sce)

# get cluster ID correspondece between 2 clusterings
old_ids <- seq_len(20)
m <- match(old_ids, cluster_codes(sce)$`meta20`)
new_ids <- cluster_codes(sce)$`meta12`[m]
data.frame(old_ids, new_ids)

# view delta area plot (relative change in area 
# under CDF curve vs. the number of clusters 'k')
delta_area(sce)

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