SCE-accessors | R Documentation |
SingleCellExperiment
accessorsVarious wrappers to conviniently access slots
in a SingleCellExperiment
created with prepData
, and that are used
frequently during differential analysis.
## 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)
x |
a |
k |
character string specifying the clustering to extract.
Valid values are |
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
Helena L Crowell helena.crowell@uzh.ch
# 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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