n_cells {CATALYST} | R Documentation |
daFrame
Methods for accessing slots in a daFrame
.
n_cells(x) marker_classes(x) type_markers(x) state_markers(x) sample_ids(x) cluster_codes(x) cluster_ids(x) ## S4 method for signature 'daFrame' exprs(object) ## S4 method for signature 'daFrame' n_cells(x) ## S4 method for signature 'daFrame' marker_classes(x) ## S4 method for signature 'daFrame' type_markers(x) ## S4 method for signature 'daFrame' state_markers(x) ## S4 method for signature 'daFrame' sample_ids(x) ## S4 method for signature 'daFrame' cluster_codes(x) ## S4 method for signature 'daFrame' cluster_ids(x)
x, object |
a |
exprs
extracts the arcsinh-transformed expressions.
n_cells
extracts the number of events measured per sample.
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_codes
extracts a data.frame
containing cluster codes for the
FlowSOM
clustering, the ConsensusClusterPlus
metaclustering, and all mergings done through mergeClusters
.
cluster_ids
extracts the numeric vector of cluster IDs
as inferred by FlowSOM
.
Helena Lucia Crowell crowellh@student.ethz.ch
# construct daFrame data(PBMC_fs, PBMC_panel, PBMC_md) re <- daFrame(PBMC_fs, PBMC_panel, PBMC_md) # run clustering lineage <- c("CD3", "CD45", "CD4", "CD20", "CD33", "CD123", "CD14", "IgM", "HLA_DR", "CD7") re <- cluster(re, cols_to_use=lineage) # view data summary library(SummarizedExperiment) cbind(metadata(re)$experiment_info, cells=n_cells(re)) # access row / cell data head(rowData(re)) plot(table(cluster_ids(re))) # access marker information type_markers(re) state_markers(re) # get cluster ID correspondece between 2 clusterings old_ids <- seq_len(20) m <- match(old_ids, cluster_codes(re)$`20`) new_ids <- cluster_codes(re)$`12`[m] data.frame(old_ids, new_ids) # plot relative change in area under CDF curve vs. k metadata(re)$delta_area