## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(ggplot2) theme_set(theme_classic()) ## ----setup, message=FALSE, eval=FALSE----------------------------------------- # library(Seurat) # library(schex) ## ----load, eval=FALSE--------------------------------------------------------- # cbmc.rna <- as.sparse(read.csv(file = # "../new functions/data/GSE100866_CBMC_8K_13AB_10X-RNA_umi.csv.gz", # sep = ",", header = TRUE, row.names = 1)) # # cbmc.rna <- CollapseSpeciesExpressionMatrix(cbmc.rna) # # cbmc.adt <- as.sparse(read.csv(file = # "../new functions/data/GSE100866_CBMC_8K_13AB_10X-ADT_umi.csv.gz", # sep = ",", header = TRUE, row.names = 1)) # # cbmc.adt <- cbmc.adt[setdiff(rownames(x = cbmc.adt), # c("CCR5", "CCR7", "CD10")), ] ## ----preprocess-gene, eval=FALSE---------------------------------------------- # cbmc <- CreateSeuratObject(counts = cbmc.rna) # # cbmc <- NormalizeData(cbmc) # cbmc <- FindVariableFeatures(cbmc) # cbmc <- ScaleData(cbmc) ## ----cluster-gene, eval=FALSE------------------------------------------------- # cbmc <- RunPCA(cbmc, verbose = FALSE) # cbmc <- RunTSNE(cbmc, dims = 1:25, method = "FIt-SNE") ## ----preprocess-protein, eval=FALSE------------------------------------------- # cbmc[["ADT"]] <- CreateAssayObject(counts = cbmc.adt) # # cbmc <- NormalizeData(cbmc, assay = "ADT", normalization.method = "CLR") # cbmc <- ScaleData(cbmc, assay = "ADT") ## ----calc-hexbin, eval=FALSE-------------------------------------------------- # cbmc <- make_hexbin(cbmc, nbins = 25, # dimension_reduction = "tsne", use_dims=c(1,2)) ## ----plot-density, fig.height=7, fig.width=7, eval=FALSE---------------------- # plot_hexbin_density(cbmc) ## ----plot-feature, fig.height=7, fig.width=7, eval=FALSE---------------------- # plot_hexbin_feature(cbmc, mod="ADT", type="scale.data", feature="CD14", # action="mean", xlab="TSNE1", ylab="TSNE2", # title=paste0("Mean of protein expression of CD14")) ## ----plot-interact, fig.height=7, fig.width=7, message=FALSE, warning=FALSE, eval=FALSE---- # plot_hexbin_interact(cbmc, type=c("scale.data", "scale.data"), # mod=c("RNA", "ADT"), feature=c("CD14", "CD14"), interact="corr_spearman", # ylab="TSNE2", xlab="TSNE1", # title="Interaction protein and gene expression CD14") + # scale_fill_gradient2(midpoint=0, low="blue", mid="white", # high="red", space ="Lab") ## ----protein-pca, message=FALSE, warning=FALSE, eval=FALSE-------------------- # DefaultAssay(cbmc) <- "ADT" # cbmc <- RunPCA(cbmc, features = rownames(cbmc), reduction.name = "pca_adt", # reduction.key = "pca_adt_", verbose = FALSE) # cbmc <- make_hexbin(cbmc, nbins = 25, # dimension_reduction = "pca_adt", use_dims=c(1,2)) ## ----plot-feature-a, fig.height=7, fig.width=7, eval=FALSE-------------------- # plot_hexbin_feature(cbmc, mod="ADT", type="scale.data", feature="CD14", # action="mean", xlab="TSNE1", ylab="TSNE2", # title=paste0("Mean of protein expression of CD14"))