## ---- echo=FALSE, results="hide"---------------------------------------------- knitr::opts_chunk$set(error=FALSE, warning=FALSE, message=FALSE) library(BiocStyle) set.seed(10918) ## ----------------------------------------------------------------------------- library(scRNAseq) sce <- ZeiselBrainData() library(scuttle) sce <- quickPerCellQC(sce, subsets=list(Mito=grep("mt-", rownames(sce))), sub.fields=c("subsets_Mito_percent", "altexps_ERCC_percent")) sce ## ----------------------------------------------------------------------------- library(scuttle) agg.sce <- aggregateAcrossCells(sce, ids=sce$level1class) head(assay(agg.sce)) colData(agg.sce)[,c("ids", "ncells")] ## ----------------------------------------------------------------------------- agg.sce <- aggregateAcrossCells(sce, ids=colData(sce)[,c("level1class", "tissue")]) head(assay(agg.sce)) colData(agg.sce)[,c("level1class", "tissue", "ncells")] ## ----------------------------------------------------------------------------- sce <- logNormCounts(sce) agg.feat <- sumCountsAcrossFeatures(sce, ids=list(GeneSet1=1:10, GeneSet2=11:50, GeneSet3=1:100), average=TRUE, exprs_values="logcounts") agg.feat[,1:10] ## ----------------------------------------------------------------------------- agg.n <- summarizeAssayByGroup(sce, statistics="prop.detected", ids=colData(sce)[,c("level1class", "tissue")]) head(assay(agg.n)) ## ----------------------------------------------------------------------------- # Mocking up a dataset to demonstrate: outfile <- tempfile() write.table(counts(sce)[1:100,], file=outfile, sep="\t", quote=FALSE) # Reading it in as a sparse matrix: output <- readSparseCounts(outfile) class(output) ## ----------------------------------------------------------------------------- # Original row names are Ensembl IDs. sce.ens <- ZeiselBrainData(ensembl=TRUE) head(rownames(sce.ens)) # Replacing with guaranteed unique and non-missing symbols: rownames(sce.ens) <- uniquifyFeatureNames( rownames(sce.ens), rowData(sce.ens)$originalName ) head(rownames(sce.ens)) ## ----------------------------------------------------------------------------- out <- makePerCellDF(sce, features="Tspan12") colnames(out) ## ----------------------------------------------------------------------------- out2 <- makePerFeatureDF(sce, cells=c("1772063062_D05", "1772063061_D01", "1772060240_F02", "1772062114_F05")) colnames(out2) ## ----------------------------------------------------------------------------- sessionInfo()