## ----echo=FALSE, results="hide"----------------------------------------------- knitr::opts_chunk$set(error=FALSE, warning=FALSE, message=FALSE) library(BiocStyle) set.seed(10918) ## ----quickstart-load-data, message=FALSE, warning=FALSE----------------------- library(scRNAseq) example_sce <- ZeiselBrainData() example_sce ## ----------------------------------------------------------------------------- library(scater) example_sce <- addPerCellQC(example_sce, subsets=list(Mito=grep("mt-", rownames(example_sce)))) ## ----------------------------------------------------------------------------- plotColData(example_sce, x = "sum", y="detected", colour_by="tissue") ## ----plot-pdata-pct-exprs-controls-------------------------------------------- plotColData(example_sce, x = "sum", y="subsets_Mito_percent", other_fields="tissue") + facet_wrap(~tissue) ## ----plot-highest, fig.asp=1, fig.wide=TRUE, eval=.Platform$OS.type!="windows"---- plotHighestExprs(example_sce, exprs_values = "counts") ## ----------------------------------------------------------------------------- # Computing variance explained on the log-counts, # so that the statistics reflect changes in relative expression. example_sce <- logNormCounts(example_sce) vars <- getVarianceExplained(example_sce, variables=c("tissue", "total mRNA mol", "sex", "age")) head(vars) plotExplanatoryVariables(vars) ## ----plot-expression, fig.wide=TRUE------------------------------------------- plotExpression(example_sce, rownames(example_sce)[1:6], x = "level1class") ## ----plot-expression-scatter-------------------------------------------------- plotExpression(example_sce, rownames(example_sce)[1:6], x = rownames(example_sce)[10]) ## ----plot-expression-col------------------------------------------------------ plotExpression(example_sce, rownames(example_sce)[1:6], x = "level1class", colour_by="tissue") ## ----plot-expression-many----------------------------------------------------- plotExpression(example_sce, rownames(example_sce)[1:6]) ## ----------------------------------------------------------------------------- example_sce <- runPCA(example_sce) str(reducedDim(example_sce, "PCA")) ## ----------------------------------------------------------------------------- example_sce <- runPCA(example_sce, name="PCA2", subset_row=rownames(example_sce)[1:1000], ncomponents=25) str(reducedDim(example_sce, "PCA2")) ## ----plot-tsne-1comp-colby-sizeby-exprs--------------------------------------- # Perplexity of 10 just chosen here arbitrarily. set.seed(1000) example_sce <- runTSNE(example_sce, perplexity=10) head(reducedDim(example_sce, "TSNE")) ## ----plot-tsne-from-pca------------------------------------------------------- set.seed(1000) example_sce <- runTSNE(example_sce, perplexity=50, dimred="PCA", n_dimred=10) head(reducedDim(example_sce, "TSNE")) ## ----------------------------------------------------------------------------- example_sce <- runUMAP(example_sce) head(reducedDim(example_sce, "UMAP")) ## ----plot-reduceddim-4comp-colby-shapeby-------------------------------------- plotReducedDim(example_sce, dimred = "PCA", colour_by = "level1class") ## ----plot-pca-4comp-colby-sizeby-exprs---------------------------------------- plotTSNE(example_sce, colour_by = "Snap25") ## ----plot-pca-default--------------------------------------------------------- plotPCA(example_sce, colour_by="Mog") ## ----plot-pca-4comp-colby-shapeby--------------------------------------------- example_sce <- runPCA(example_sce, ncomponents=20) plotPCA(example_sce, ncomponents = 4, colour_by = "level1class") ## ----fig.wide=TRUE------------------------------------------------------------ ggcells(example_sce, mapping=aes(x=level1class, y=Snap25)) + geom_boxplot() + facet_wrap(~tissue) ## ----------------------------------------------------------------------------- ggcells(example_sce, mapping=aes(x=TSNE.1, y=TSNE.2, colour=Snap25)) + geom_point() + stat_density_2d() + facet_wrap(~tissue) + scale_colour_distiller(direction=1) ## ----------------------------------------------------------------------------- ggcells(example_sce, mapping=aes(x=sizeFactor, y=Actb)) + geom_point() + geom_smooth() ## ----------------------------------------------------------------------------- colnames(example_sce) <- make.names(colnames(example_sce)) ggfeatures(example_sce, mapping=aes(x=featureType, y=X1772062111_E06)) + geom_violin() ## ----------------------------------------------------------------------------- sessionInfo()