## ---- echo=FALSE, results="hide"---------------------------------------------- knitr::opts_chunk$set(error=FALSE, warning=FALSE, message=FALSE) library(BiocStyle) set.seed(10918) ## ----------------------------------------------------------------------------- library(scRNAseq) sce <- ZeiselBrainData() library(scuttle) sce <- quickPerCellQC(sce, subsets=list(Mito=grep("mt-", rownames(sce))), sub.fields=c("subsets_Mito_percent", "altexps_ERCC_percent")) sce ## ---- echo=FALSE-------------------------------------------------------------- # Make the damn thing sparse for speed. counts(sce) <- as(counts(sce), "dgCMatrix") ## ----------------------------------------------------------------------------- summary(librarySizeFactors(sce)) ## ----------------------------------------------------------------------------- summary(geometricSizeFactors(sce)) ## ----------------------------------------------------------------------------- summary(medianSizeFactors(sce)) ## ----------------------------------------------------------------------------- sizeFactors(sce) <- librarySizeFactors(sce) ## ----------------------------------------------------------------------------- sce <- computeLibraryFactors(sce) summary(sizeFactors(sce)) ## ----------------------------------------------------------------------------- library(scran) clusters <- quickCluster(sce) sce <- computePooledFactors(sce, clusters=clusters) summary(sizeFactors(sce)) ## ----------------------------------------------------------------------------- sce <- computePooledFactors(sce, clusters=sce$level1class) summary(sizeFactors(sce)) ## ----------------------------------------------------------------------------- sce2 <- computeSpikeFactors(sce, "ERCC") summary(sizeFactors(sce2)) ## ----------------------------------------------------------------------------- sce <- logNormCounts(sce) assayNames(sce) ## ----------------------------------------------------------------------------- assay(sce, "normed") <- normalizeCounts(sce, log=FALSE, size.factors=runif(ncol(sce)), pseudo.count=1.5) ## ----------------------------------------------------------------------------- assay(sce, "cpm") <- calculateCPM(sce) ## ----------------------------------------------------------------------------- sessionInfo()