## ----setup, include=FALSE----------------------------------------------------- knitr::opts_chunk$set(echo = TRUE, dev = "png") ## ----install, eval= FALSE----------------------------------------------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) { # install.packages("BiocManager") # } # BiocManager::install("celda") ## ----load, eval=TRUE, message=FALSE------------------------------------------- library(celda) ## ----load_10X, eval=TRUE, message=FALSE--------------------------------------- # Install TENxPBMCData if is it not already if (!requireNamespace("TENxPBMCData", quietly = TRUE)) { if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("TENxPBMCData") } # Load PBMC data library(TENxPBMCData) pbmc4k <- TENxPBMCData("pbmc4k") colnames(pbmc4k) <- paste(pbmc4k$Sample, pbmc4k$Barcode, sep = "_") rownames(pbmc4k) <- rowData(pbmc4k)$Symbol_TENx ## ----decontX_background, eval=FALSE, message=FALSE---------------------------- # pbmc4k <- decontX(x = pbmc4k, background = raw) ## ----decontX, eval=TRUE, message=FALSE---------------------------------------- pbmc4k <- decontX(x = pbmc4k) ## ----UMAP_Clusters------------------------------------------------------------ umap <- reducedDim(pbmc4k, "decontX_UMAP") plotDimReduceCluster(x = pbmc4k$decontX_clusters, dim1 = umap[, 1], dim2 = umap[, 2]) ## ----------------------------------------------------------------------------- plotDecontXContamination(pbmc4k) ## ---- message=FALSE----------------------------------------------------------- library(scater) pbmc4k <- logNormCounts(pbmc4k) plotDimReduceFeature(as.matrix(logcounts(pbmc4k)), 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(pbmc4k, markers = markers, groupClusters = cellTypeMappings, assayName = "counts") ## ----barplotDecontCounts------------------------------------------------------ plotDecontXMarkerPercentage(pbmc4k, markers = markers, groupClusters = cellTypeMappings, assayName = "decontXcounts") ## ----barplotBoth-------------------------------------------------------------- plotDecontXMarkerPercentage(pbmc4k, markers = markers, groupClusters = cellTypeMappings, assayName = c("counts", "decontXcounts")) ## ----plotDecontXMarkerExpression---------------------------------------------- plotDecontXMarkerExpression(pbmc4k, markers = markers[["Monocyte_Markers"]], groupClusters = cellTypeMappings, ncol = 3) ## ----plot_norm_counts, eval = FALSE------------------------------------------- # pbmc4k <- scater::logNormCounts(pbmc4k, # exprs_values = "decontXcounts", # name = "dlogcounts") # # plotDecontXMarkerExpression(pbmc4k, # markers = markers[["Monocyte_Markers"]], # groupClusters = cellTypeMappings, # ncol = 3, # assayName = c("logcounts", "dlogcounts")) ## ----findDelta---------------------------------------------------------------- metadata(pbmc4k)$decontX$estimates$all_cells$delta ## ----newDecontX, eval=TRUE, message=FALSE------------------------------------- pbmc4k.delta <- decontX(pbmc4k, delta = c(9, 20), estimateDelta = FALSE) plot(pbmc4k$decontX_contamination, pbmc4k.delta$decontX_contamination, xlab = "DecontX estimated priors", ylab = "Setting priors to estimate higher contamination") abline(0, 1, col = "red", lwd = 2) ## ----------------------------------------------------------------------------- sessionInfo()