buildCellTypeIndex {scfind} | R Documentation |
Calculates a fraction of expressed cells per gene per cell type
buildCellTypeIndex(object = NULL, cell_type_column = "cell_type1") buildCellTypeIndex.SCESet(object, cell_type_column) ## S4 method for signature 'SingleCellExperiment' buildCellTypeIndex(object = NULL, cell_type_column = "cell_type1")
object |
object of SingleCellExperiment class |
cell_type_column |
column name in the colData slot of the object SingleCellExperiment containing the cell classification information |
a 'data.frame' containing calculated gene index
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)), ] index <- buildCellTypeIndex(sce)