| findCellType {scfind} | R Documentation |
Calculates p-values of a log-likelihood of a list of genes to be associated with each cell type. Log-likelihood is based on gene expression values.
findCellType(gene_index = NULL, gene_list = NULL) findCellType.data.frame(gene_index, gene_list) ## S4 method for signature 'data.frame' findCellType(gene_index = NULL, gene_list = NULL)
gene_index |
a data.frame with cell types in columns and genes in rows |
gene_list |
genes that need to be searched in the gene_index |
a named numeric vector containing p-values
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)
res <- findCellType(index, gene_list = c('SOX6', 'SNAI3'))