## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, dev = "png") ## ----load, eval=TRUE, message=FALSE------------------------------------------- library(celda) ## ----sce_import, eval = FALSE------------------------------------------------- # library(singleCellTK) # sce <- importCellRanger(sampleDirs = c("path/to/sample1/", "path/to/sample2/")) ## ----sce_import_raw, eval = FALSE--------------------------------------------- # sce.raw <- importCellRanger(sampleDirs = c("path/to/sample1/", "path/to/sample2/"), dataType = "raw") ## ----load_10X, eval=TRUE, message=FALSE--------------------------------------- # Load PBMC data library(TENxPBMCData) sce <- TENxPBMCData("pbmc4k") colnames(sce) <- paste(sce$Sample, sce$Barcode, sep = "_") rownames(sce) <- rowData(sce)$Symbol_TENx counts(sce) <- as(counts(sce), "dgCMatrix") ## ----decontX, eval=TRUE, message=FALSE---------------------------------------- sce <- decontX(sce) ## ----decontX_background, eval=FALSE, message=FALSE---------------------------- # sce <- decontX(sce, background = sce.raw) ## ----UMAP_Clusters------------------------------------------------------------ umap <- reducedDim(sce, "decontX_UMAP") plotDimReduceCluster(x = sce$decontX_clusters, dim1 = umap[, 1], dim2 = umap[, 2]) ## ----plot_decon--------------------------------------------------------------- plotDecontXContamination(sce) ## ----plot_feature, message=FALSE---------------------------------------------- library(scater) sce <- logNormCounts(sce) plotDimReduceFeature(as.matrix(logcounts(sce)), dim1 = umap[, 1], dim2 = umap[, 2], features = c("CD3D", "CD3E", "GNLY", "LYZ", "S100A8", "S100A9", "CD79A", "CD79B", "MS4A1"), exactMatch = TRUE) ## ----barplotCounts------------------------------------------------------------ markers <- list(Tcell_Markers = c("CD3E", "CD3D"), Bcell_Markers = c("CD79A", "CD79B", "MS4A1"), Monocyte_Markers = c("S100A8", "S100A9", "LYZ"), NKcell_Markers = "GNLY") cellTypeMappings <- list(Tcells = 2, Bcells = 5, Monocytes = 1, NKcells = 6) plotDecontXMarkerPercentage(sce, markers = markers, groupClusters = cellTypeMappings, assayName = "counts") ## ----barplotDecontCounts------------------------------------------------------ plotDecontXMarkerPercentage(sce, markers = markers, groupClusters = cellTypeMappings, assayName = "decontXcounts") ## ----barplotBoth-------------------------------------------------------------- plotDecontXMarkerPercentage(sce, markers = markers, groupClusters = cellTypeMappings, assayName = c("counts", "decontXcounts")) ## ----plotDecontXMarkerExpression---------------------------------------------- plotDecontXMarkerExpression(sce, markers = markers[["Monocyte_Markers"]], groupClusters = cellTypeMappings, ncol = 3) ## ----plot_norm_counts, eval = TRUE-------------------------------------------- library(scater) sce <- logNormCounts(sce, exprs_values = "decontXcounts", name = "decontXlogcounts") plotDecontXMarkerExpression(sce, markers = markers[["Monocyte_Markers"]], groupClusters = cellTypeMappings, ncol = 3, assayName = c("logcounts", "decontXlogcounts")) ## ----findDelta---------------------------------------------------------------- metadata(sce)$decontX$estimates$all_cells$delta ## ----newDecontX, eval=TRUE, message=FALSE------------------------------------- sce.delta <- decontX(sce, delta = c(9, 20), estimateDelta = FALSE) plot(sce$decontX_contamination, sce.delta$decontX_contamination, xlab = "DecontX estimated priors", ylab = "Setting priors to estimate higher contamination") abline(0, 1, col = "red", lwd = 2) ## ----seurat_create, eval = FALSE---------------------------------------------- # # Read counts from CellRanger output # library(Seurat) # counts <- Read10X("sample/outs/filtered_feature_bc_matrix/") # # # Create a SingleCellExperiment object and run decontX # sce <- SingleCellExperiment(list(counts = counts)) # sce <- decontX(sce) # # # Create a Seurat object from a SCE with decontX results # seuratObject <- CreateSeuratObject(round(decontXcounts(sce))) ## ----seurat_raw, eval = FALSE------------------------------------------------- # counts.raw <- Read10X("sample/outs/raw_feature_bc_matrix/") # sce.raw <- SingleCellExperiment(list(counts = counts.raw)) # sce <- decontX(sce, background = sce.raw) ## ----seurat_create2, eval = FALSE--------------------------------------------- # counts <- GetAssayData(object = seuratObject, slot = "counts") # sce <- SingleCellExperiment(list(counts = counts)) # sce <- decontX(sce) # seuratObj[["decontXcounts"]] <- CreateAssayObject(counts = decontXcounts(sce)) ## ----------------------------------------------------------------------------- sessionInfo()