indexCluster {scmap} | R Documentation |
Calculates centroids of each cell type and merge them into a single table.
indexCluster(object = NULL, cluster_col = "cell_type1") indexCluster.SingleCellExperiment(object, cluster_col) ## S4 method for signature 'SingleCellExperiment' indexCluster(object = NULL, cluster_col = "cell_type1")
object |
SingleCellExperiment object |
cluster_col |
column name in the 'colData' slot of the SingleCellExperiment object containing the cell classification information |
a 'data.frame' containing calculated centroids of the cell types of the Reference dataset
library(SingleCellExperiment) sce <- SingleCellExperiment(assays = list(normcounts = as.matrix(yan)), colData = ann) # this is needed to calculate dropout rate for feature selection # important: normcounts have the same zeros as raw counts (fpkm) counts(sce) <- normcounts(sce) logcounts(sce) <- log2(normcounts(sce) + 1) # use gene names as feature symbols rowData(sce)$feature_symbol <- rownames(sce) isSpike(sce, 'ERCC') <- grepl('^ERCC-', rownames(sce)) # remove features with duplicated names sce <- sce[!duplicated(rownames(sce)), ] sce <- selectFeatures(sce) sce <- indexCluster(sce[rowData(sce)$scmap_features, ])