## ----knitr-options, echo=FALSE, message=FALSE, warning=FALSE, cache=TRUE---- library(knitr) opts_chunk$set(fig.align = 'center', fig.width = 6, fig.height = 5, dev = 'png') op <- options(gvis.plot.tag='chart') ## ---- warning=FALSE, message=FALSE----------------------------------------- library(SingleCellExperiment) library(scfind) head(ann) yan[1:3, 1:3] ## -------------------------------------------------------------------------- sce <- SingleCellExperiment(assays = list(normcounts = as.matrix(yan)), colData = ann) # this is needed to calculate dropout rate for feature selection # important: normcounts have the same zeros as raw counts (fpkm) counts(sce) <- normcounts(sce) logcounts(sce) <- log2(normcounts(sce) + 1) # use gene names as feature symbols rowData(sce)$feature_symbol <- rownames(sce) isSpike(sce, "ERCC") <- grepl("^ERCC-", rownames(sce)) # remove features with duplicated names sce <- sce[!duplicated(rownames(sce)), ] sce ## -------------------------------------------------------------------------- geneIndex <- buildCellTypeIndex(sce) p_values <- -log10(findCellType(geneIndex, c("SOX6", "SNAI3"))) barplot(p_values, ylab = "-log10(pval)", las = 2) ## -------------------------------------------------------------------------- geneIndex <- buildCellIndex(sce) res <- findCell(geneIndex, c("SOX6", "SNAI3")) res$common_exprs_cells ## -------------------------------------------------------------------------- barplot(-log10(res$p_values), ylab = "-log10(pval)", las = 2) ## ----echo=FALSE------------------------------------------------------------ sessionInfo()