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.
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a data.frame with cell types in columns and genes in rows
genes that need to be searched in the gene_index
a named numeric vector containing p-values
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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'))
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