## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----load-packages, message=FALSE, warning=FALSE------------------------------ library(scTreeViz) library(Seurat) library(SC3) library(scran) library(scater) library(clustree) library(igraph) library(scRNAseq) ## ----results='hide', warning=FALSE, error=FALSE, message=FALSE---------------- # load dataset sce<- ZeiselBrainData() # Normalization sce <- logNormCounts(sce) # calculate umap and tsne sce <- runUMAP(sce) sce<- runTSNE(sce) sce<- runPCA(sce) ## ----warning=FALSE, error=FALSE, message=FALSE-------------------------------- treeViz <- createFromSCE(sce, reduced_dim = c("UMAP","PCA","TSNE")) plot(treeViz) ## ----eval=FALSE, warning=FALSE, error=FALSE, message=FALSE-------------------- # # Forming clusters # set.seed(1000) # for (i in seq(10)) { # clust.kmeans <- kmeans(reducedDim(sce, "TSNE"), centers = i) # sce[[paste0("clust", i)]] <- factor(clust.kmeans$cluster) # } # # treeViz<- createFromSCE(sce, check_coldata = TRUE, col_regex = "clust") # plot(treeViz) ## ----echo=TRUE, results='hide', warning=FALSE, error=FALSE, message=FALSE----- data(pbmc_small) pbmc <- pbmc_small ## ----echo=TRUE, results='hide', warning=FALSE, error=FALSE, message=FALSE----- pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-") pbmc <- NormalizeData(pbmc) all.genes <- rownames(pbmc) pbmc <- ScaleData(pbmc, vars.to.regress = "percent.mt") pbmc <- FindVariableFeatures(object = pbmc) pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc)) pbmc <- FindNeighbors(pbmc, dims = 1:10) pbmc <- FindClusters(pbmc, resolution = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0), print.output = 0, save.SNN = TRUE) pbmc ## ----echo=TRUE, results='hide', warning=FALSE, error=FALSE, message=FALSE----- # pbmc<- RunTSNE(pbmc) pbmc<- RunUMAP(pbmc, dims=1:3) Reductions(pbmc) ## ----echo=TRUE, warning=FALSE, error=FALSE, message=FALSE-------------------- treeViz<- createFromSeurat(pbmc, check_metadata = TRUE, reduced_dim = c("umap","pca","tsne")) plot(treeViz) ## ----results='hide', warning=FALSE, error=FALSE, message=FALSE---------------- n=64 # create a hierarchy df<- data.frame(cluster0=rep(1,n)) for(i in seq(1,5)){ df[[paste0("cluster",i)]]<- rep(seq(1:(2**i)),each=ceiling(n/(2**i)),len=n) } # generate a count matrix counts <- matrix(rpois(6400, lambda = 10), ncol=n, nrow=100) colnames(counts)<- seq(1:64) # create a `TreeViz` object treeViz <- createTreeViz(df, counts) plot(treeViz) ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # app <- startTreeviz(treeViz, top_genes = 500) ## ----eval=FALSE--------------------------------------------------------------- # app$plotGene(gene="AIF1") ## ----eval=FALSE, echo=TRUE---------------------------------------------------- # app$stop_app() ## ----------------------------------------------------------------------------- sessionInfo()