## ----options, include=FALSE, echo=FALSE--------------------------------------- library(BiocStyle) knitr::opts_chunk$set(crop=NULL) ## ----eval=FALSE--------------------------------------------------------------- # if (!require("BiocManager", quietly=TRUE)) # install.packages("BiocManager") # # BiocManager::install("SingleCellAlleleExperiment") ## ----message = FALSE---------------------------------------------------------- library(SingleCellAlleleExperiment) library(scaeData) library(scater) library(scran) library(scuttle) library(DropletUtils) library(ggplot2) library(patchwork) library(org.Hs.eg.db) library(AnnotationDbi) ## ----------------------------------------------------------------------------- example_data_10k <- scaeData::scaeDataGet(dataset="pbmc_10k") ## ----------------------------------------------------------------------------- example_data_10k ## ----------------------------------------------------------------------------- lookup_name <- "pbmc_10k_lookup_table.csv" lookup <- utils::read.csv(system.file("extdata", lookup_name, package="scaeData")) ## ----------------------------------------------------------------------------- head(lookup) ## ----eval=FALSE, warning=FALSE------------------------------------------------ # scae <- read_allele_counts(example_data_10k$dir, # sample_names="example_data", # filter_mode="no", # lookup_file=lookup, # barcode_file=example_data_10k$barcodes, # gene_file=example_data_10k$features, # matrix_file=example_data_10k$matrix, # filter_threshold=NULL, # verbose=FALSE) # ## ----warning=FALSE------------------------------------------------------------ scae <- read_allele_counts(example_data_10k$dir, sample_names="example_data", filter_mode="yes", lookup_file=lookup, barcode_file=example_data_10k$barcodes, gene_file=example_data_10k$features, matrix_file=example_data_10k$matrix, log=TRUE) scae ## ----------------------------------------------------------------------------- metadata(scae)[["knee_info"]] ## ----warning=FALSE, fig.height=5, fig.width=5--------------------------------- knee_df <- metadata(scae)[["knee_info"]]$knee_df knee_point <- metadata(scae)[["knee_info"]]$knee_point inflection_point <- metadata(scae)[["knee_info"]]$inflection_point ggplot(knee_df, aes(x=rank, y=total)) + geom_point() + geom_line(aes(y=fitted), color="red") + scale_x_log10() + scale_y_log10() + annotation_logticks() + labs(x="Barcode rank", y="Total UMI count") + geom_hline(yintercept=S4Vectors::metadata(knee_df)$knee, color="dodgerblue", linetype="dashed") + geom_hline(yintercept=S4Vectors::metadata(knee_df)$inflection, color="forestgreen", linetype="dashed") + annotate("text", x=2, y=S4Vectors::metadata(knee_df)$knee * 1.2, label="knee", color="dodgerblue") + annotate("text", x=2.25, y=S4Vectors::metadata(knee_df)$inflection * 1.2, label="inflection", color="forestgreen") + theme_bw() ## ----warning=FALSE, eval=FALSE------------------------------------------------ # #this is the object used in the further workflow # scae <- read_allele_counts(example_data_10k$dir, # sample_names="example_data", # filter_mode="custom", # lookup_file=lookup, # barcode_file=example_data_10k$barcodes, # gene_file=example_data_10k$features, # matrix_file=example_data_10k$matrix, # filter_threshold=282) # scae ## ----------------------------------------------------------------------------- rowData(scae) ## ----------------------------------------------------------------------------- head(colData(scae)) ## ----------------------------------------------------------------------------- metadata(scae)[["knee_info"]] ## ----------------------------------------------------------------------------- scae_nonimmune_subset <- scae_subset(scae, "nonimmune") head(rownames(scae_nonimmune_subset)) ## ----------------------------------------------------------------------------- scae_alleles_subset <- scae_subset(scae, "alleles") head(rownames(scae_alleles_subset)) ## ----------------------------------------------------------------------------- scae_immune_genes_subset <- scae_subset(scae, "immune_genes") head(rownames(scae_immune_genes_subset)) ## ----------------------------------------------------------------------------- scae_functional_groups_subset <- scae_subset(scae, "functional_groups") head(rownames(scae_functional_groups_subset)) ## ----------------------------------------------------------------------------- scae_immune_layers_subset <- c(rownames(scae_subset(scae, "alleles")), rownames(scae_subset(scae, "immune_genes")), rownames(scae_subset(scae, "functional_groups"))) scater::plotExpression(scae, scae_immune_layers_subset) ## ----------------------------------------------------------------------------- rn_scae_ni <- rownames(scae_subset(scae, "nonimmune")) rn_scae_alleles <- rownames(scae_subset(scae, "alleles")) scae_nonimmune__allels_subset <- scae[c(rn_scae_ni, rn_scae_alleles), ] scae_nonimmune__allels_subset ## ----------------------------------------------------------------------------- rn_scae_i <- rownames(scae_subset(scae, "immune_genes")) scae_nonimmune__immune <- scae[c(rn_scae_ni, rn_scae_i), ] scae_nonimmune__immune ## ----------------------------------------------------------------------------- rn_scae_functional <- rownames(scae_subset(scae, "functional_groups")) scae_nonimmune__functional <- scae[c(rn_scae_ni, rn_scae_functional), ] scae_nonimmune__functional ## ----------------------------------------------------------------------------- df_ni_a <- modelGeneVar(scae_nonimmune__allels_subset) top_ni_a <- getTopHVGs(df_ni_a, prop=0.1) ## ----------------------------------------------------------------------------- df_ni_g <- modelGeneVar(scae_nonimmune__immune) top_ni_g <- getTopHVGs(df_ni_g, prop=0.1) ## ----------------------------------------------------------------------------- df_ni_f <- modelGeneVar(scae_nonimmune__functional) top_ni_f <- getTopHVGs(df_ni_f, prop=0.1) ## ----------------------------------------------------------------------------- assay <- "logcounts" scae <- runPCA(scae, ncomponents=10, subset_row=top_ni_a, exprs_values=assay, name="PCA_a") scae <- runPCA(scae, ncomponents=10, subset_row=top_ni_g, exprs_values=assay, name="PCA_g") scae <- runPCA(scae, ncomponents=10, subset_row=top_ni_f, exprs_values=assay, name="PCA_f") ## ----------------------------------------------------------------------------- reducedDimNames(scae) ## ----------------------------------------------------------------------------- set.seed(18) scae <- runTSNE(scae, dimred="PCA_g", name="TSNE_g") ## ----eval=FALSE--------------------------------------------------------------- # set.seed(18) # scae <- runTSNE(scae, dimred="PCA_a", name="TSNE_a") ## ----eval=FALSE--------------------------------------------------------------- # set.seed(18) # scae <- runTSNE(scae, dimred="PCA_f", name="TSNE_f") ## ----------------------------------------------------------------------------- reducedDimNames(scae) ## ----fig3, fig.height = 4, fig.width = 12, fig.align = "center", warning = FALSE, message=FALSE---- tsne <- "TSNE_g" tsne_g_a <- plotReducedDim(scae, dimred=tsne, colour_by="HLA-DRB1") + ggtitle("HLA-DRB1 gene") tsne_g_a1 <- plotReducedDim(scae, dimred=tsne, colour_by="DRB1*07:01:01:01") + ggtitle("Allele DRB1*07:01:01:01") tsne_g_a2 <- plotReducedDim(scae, dimred=tsne, colour_by="DRB1*13:01:01") + ggtitle("Allele DRB1*13:01:01") p2 <- tsne_g_a + tsne_g_a1 + tsne_g_a2 p2 ## ----------------------------------------------------------------------------- sessionInfo()