## ----echo=TRUE, message=FALSE, warning=FALSE, eval=FALSE---------------------- # if (!require("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # BiocManager::install("looking4clusters") ## ----echo=TRUE, message=FALSE, warning=FALSE, eval=TRUE----------------------- library("looking4clusters") ## ----echo=TRUE, message=FALSE, warning=FALSE---------------------------------- # Load sample data from scRNAseq package library(scRNAseq) sce <- ReprocessedAllenData("tophat_counts") counts <- assay(sce, "tophat_counts") # Perform dimensional reduction and an automatic cluster identification obj <- looking4clusters(t(counts), groups=colData(sce)[,'dissection_s']) # Output interactive visualization l4chtml(obj) ## ----echo=FALSE, out.width='100%'--------------------------------------------- knitr::include_graphics("auto.png") ## ----echo=TRUE, message=FALSE, warning=FALSE---------------------------------- # Create a new looking for cluster object obj <- looking4clusters(t(counts), running_all=FALSE) # Add a sample clasification from input data groups <- colData(sce)[,'dissection_s'] obj <- addcluster(obj, groups, myGroups=TRUE) # Perform a PCA and TSNE and add to the object as a dimensional reduction layout libsizes <- colSums(counts) size.factors <- libsizes/mean(libsizes) logcounts(sce) <- log2(t(t(counts)/size.factors) + 1) pca_data <- prcomp(t(logcounts(sce)), rank=50) obj <- addreduction(obj, pca_data$x[,1:2], "PCA") library(Rtsne) tsne_data <- Rtsne(pca_data$x[,1:50], pca = FALSE) obj <- addreduction(obj, tsne_data$Y, "TSNE") # Output interactive visualization l4chtml(obj) ## ----echo=FALSE, out.width='100%'--------------------------------------------- knitr::include_graphics("manual.png") ## ----echo=TRUE, message=FALSE, warning=FALSE---------------------------------- # Adding PCA and TSNE to the object reducedDims(sce) <- list(PCA=pca_data$x, TSNE=tsne_data$Y) # Create a looking4clusters object from a SingleCellExperiment object obj <- looking4clusters(sce, groups="dissection_s") # Output interactive visualization l4chtml(obj) ## ----echo=FALSE, out.width='100%'--------------------------------------------- knitr::include_graphics("sce.png") ## ----echo=TRUE, message=FALSE, warning=FALSE, eval=FALSE---------------------- # library(Seurat) # library(Matrix) # # # Load sample data from ZilionisLungData # lung <- ZilionisLungData() # immune <- lung$Used & lung$used_in_NSCLC_immune # lung <- lung[,immune] # lung <- lung[1:10000,1:1000] # # exp_mat <- Matrix(counts(lung),sparse = TRUE) # colnames(exp_mat) <- paste0(colnames(exp_mat), seq(1,ncol(exp_mat))) # # # Create a new Seurat object # seurat_object <- CreateSeuratObject(counts = exp_mat, # project = "Zilionis_immune") # # # Seurat data processing steps # seurat_object <- NormalizeData(seurat_object) # seurat_object <- ScaleData(seurat_object, features = rownames(seurat_object)) # # seurat_object <- FindVariableFeatures(seurat_object) # seurat_object <- RunPCA(seurat_object, # features = VariableFeatures(object = seurat_object)) # # # Create a looking4clusters object from a Seurat object # obj <- looking4clusters(seurat_object) # # # Output interactive visualization # l4chtml(obj) ## ----echo=FALSE, out.width='100%'--------------------------------------------- knitr::include_graphics("seurat.png") ## ----sessionInfo-------------------------------------------------------------- sessionInfo()