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.
1 2 3 4 5 6
object of SingleCellExperiment class
column name in the colData slot of the object SingleCellExperiment containing the cell classification information
a 'list' containing calculated gene index
1 2 3 4 5 6 7 8 9 10 11 12 13
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 <- buildCellIndex(sce) res <- findCell(index, genelist = c('SOX6', 'SNAI3'))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.