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## ---- 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()
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