## ----install_developter, eval=FALSE------------------------------------------- # # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # if (!requireNamespace("devtools", quietly = TRUE)) # install.packages("devtools") # # BiocManager::install("pcaMethods") # BiocManager::install("GSVA") # # devtools::install_github("wilsonlabgroup/scMappR") # # # ## ----install_cran, eval=FALSE------------------------------------------------- # # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager") # if (!requireNamespace("devtools", quietly = TRUE)) # install.packages("devtools") # # BiocManager::install("pcaMethods") # BiocManager::install("GSVA") # # install.packages("scMappR") # ## ----get_signatures, eval=FALSE----------------------------------------------- # # signatures <- get_signature_matrices(type = "all") #return a list of cell-type labels, p-values, and odds-ratios. # # ## ----scMappR_and_pathway_analysis, eval=FALSE--------------------------------- # # data(PBMC_scMappR) # load data example of PBMC bulk- and cell-sorted RNA-seq data # # bulk_DE_cors <- PBMC_example$bulk_DE_cors # 59 sex-specific DEGs in bulk PBMC (up-regulated = female-biased) # # bulk_normalized <- PBMC_example$bulk_normalized # log CPM normalized bulk RNA-seq data # # odds_ratio_in <- PBMC_example$odds_ratio_in # signature matrix developed from cell-sorted RNA-seq # # case_grep <- "_female" # flag for 'cases' (up-regulated), index is also acceptable # # control_grep <- "_male" # flag for 'control' (down-regulated), index is also acceptable # # max_proportion_change <- 10 # maximum cell-type proportion change -- this is good for cell-types that are uncomon in population and small absolute changes may yield large relative changes # # theSpecies <- "human" # these RNA-seq data have human gene symbols (and are also from human) # # # When running scMappR, it is strongly recommended to use scMappR_and_pathway analysis with the parameters below. # toOut <- scMappR_and_pathway_analysis(bulk_normalized, odds_ratio_in, # bulk_DE_cors, case_grep = case_grep, # control_grep = control_grep, rda_path = "", # max_proportion_change = 10, print_plots = TRUE, # plot_names = "scMappR_vignette_", theSpecies = "human", # output_directory = "scMappR_vignette_", # sig_matrix_size = 3000, up_and_downregulated = TRUE, # internet = TRUE, toSave = TRUE, path = tempdir()) # # ## ----two_method_pathway, eval=FALSE------------------------------------------- # # twoOutFiles <- two_method_pathway_enrichment(bulk_DE_cors, "human", # scMappR_vals = toOut$cellWeighted_Foldchange, background_genes = rownames(bulk_normalized), # output_directory = "newfun_test",plot_names = "nonreranked_", toSave = FALSE) # # # ## ----cwFoldChange_evaluate, eval=FALSE---------------------------------------- # # # evaluated <- cwFoldChange_evaluate(toOut$cellWeighted_Foldchange, toOut$cellType_Proportions, bulk_DE_cors) # # ## ----library_scMappR, warning=FALSE, echo = FALSE----------------------------- library(scMappR) ## ----scMappR_internal_example, eval = FALSE----------------------------------- # # data(POA_example) # region to preoptic area # # Signature <- POA_example$POA_Rank_signature # signature matrix # # rowname <- get_gene_symbol(Signature) # get signature # # rownames(Signature) <- rowname$rowname # # genes <- rownames(Signature)[1:60] # # rda_path1 = "" # data directory (if it exists) # # # Identify tissues available for tissue_scMappR_internal # data(scMappR_tissues) # # "Hypothalamus" %in% toupper(scMappR_tissues) # # internal <- tissue_scMappR_internal(genes, "mouse", output_directory = "scMappR_Test_Internal", # tissue = "hypothalamus", rda_path = rda_path1, toSave = TRUE, path = tempdir()) # # ## ----scMappR_custom_example, eval = FALSE------------------------------------- # # # Acquiring the gene list # data(POA_example) # # Signature <- POA_example$POA_Rank_signature # # rowname <- get_gene_symbol(Signature) # # rownames(Signature) <- rowname$rowname # # genes <- rownames(Signature)[1:200] # # #running tisue_scMappR_custom # internal <- tissue_scMappR_custom(genes,Signature,output_directory = "scMappR_Test_custom", toSave = F) # # ## ----tissue_ct_enrichment_example, fig.show='hide', eval=FALSE---------------- # # data(POA_example) # POA_generes <- POA_example$POA_generes # POA_OR_signature <- POA_example$POA_OR_signature # POA_Rank_signature <- POA_example$POA_Rank_signature # Signature <- POA_Rank_signature # rowname <- get_gene_symbol(Signature) # rownames(Signature) <- rowname$rowname # genes <- rownames(Signature)[1:100] # # enriched <- tissue_by_celltype_enrichment(gene_list = genes, # species = "mouse",p_thresh = 0.05, isect_size = 3) # # # # ## ----process_scRNAseq_count, eval = FALSE------------------------------------- # # data(sm) # # toProcess <- list(example = sm) # # tst1 <- process_dgTMatrix_lists(toProcess, name = "testProcess", species_name = "mouse", # naming_preference = "eye", rda_path = "", # toSave = TRUE, saveSCObject = TRUE, path = tempdir()) # # # ## ----make_multi_scRNAseq, eval = FALSE---------------------------------------- # # # generating scRNA-seq data with multiple runs. # data(sm) # # sm1 <- sm2 <- sm # colnames(sm1) <- paste0(colnames(sm1), ".1") # colnames(sm2) <- paste0(colnames(sm2),".2") # combined_counts <- cbind(sm1,sm2) # ## ----combine_int_anchors, eval=FALSE------------------------------------------ # toProcess <- list() # for(i in 1:2) { # toProcess[[paste0("example",i)]] <- combined_counts[,grep(paste0(".",i), colnames(combined_counts))] # } # tst1 <- process_dgTMatrix_lists(toProcess, name = "testProcess", species_name = "mouse", # naming_preference = "eye", rda_path = "", # toSave = TRUE, saveSCObject = TRUE, path = tempdir()) # # ## ----combine_nobatch, eval=FALSE---------------------------------------------- # # tst1 <- process_dgTMatrix_lists(combined_counts, name = "testProcess", species_name = "mouse", # naming_preference = "eye", rda_path = "", # toSave = TRUE, saveSCObject = TRUE, path = tempdir()) # # ## ----Seurat_Object_Generation, eval = FALSE----------------------------------- # # # data(sm) # # toProcess <- list(sm = sm) # # seurat_example <- process_from_count(toProcess, "test_vignette",theSpecies = "mouse") # # levels(seurat_example@active.ident) <- c("Myoblast", "Neutrophil", "cardiomyoblast", "Mesothelial") # ## ----from_seurat_object, eval = FALSE----------------------------------------- # # generes <- seurat_to_generes(pbmc = seurat_example, test = "wilcox") # # gene_out <- generes_to_heatmap(generes, make_names = FALSE) # ## ----from_count_and_genes, eval = FALSE--------------------------------------- # # #Create the cell-type ids and matrix # Cell_type_id <- seurat_example@active.ident # # count_file <- sm # # rownames_example <- get_gene_symbol(count_file) # # rownames(count_file) <- rownames_example$rowname # # # make seurat object # seurat_example <- process_from_count(count_file, "test_vignette",theSpecies = "mouse") # # # Intersect column names (cell-types) with labelled CTs # # inters <- intersect(colnames(seurat_example), names(Cell_type_id)) # # seurat_example_inter <- seurat_example[,inters] # # Cell_type_id_inter <- Cell_type_id[inters] # # seurat_example_inter@active.ident <- Cell_type_id_inter # # # Making signature matrices # # generes <- seurat_to_generes(pbmc = seurat_example_inter, test = "wilcox") # # gene_out <- generes_to_heatmap(generes, make_names = FALSE) # ## ----plot_barplot, eval=FALSE------------------------------------------------- # # # making an example matrix # term_name <- c("one", "two", "three") # log10 <- c(1.5, 4, 2.1) # # ordered_back_all <- as.data.frame(cbind(term_name,log10)) # # #plotting # g <- ggplot2::ggplot(ordered_back_all, ggplot2::aes(x = stats::reorder(term_name, # log10), y = log10)) + ggplot2::geom_bar(stat = "identity", # fill = "turquoise") + ggplot2::coord_flip() + ggplot2::labs(y = "-log10(Padj)", # x = "Gene Ontology") # y <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(face = NULL, # color = "black", size = 12, angle = 35), axis.text.y = ggplot2::element_text(face = NULL, # color = "black", size = 12, angle = 35), axis.title = ggplot2::element_text(size = 16, # color = "black")) # # print(y) # ## ----heatmap_identification, eval=FALSE--------------------------------------- # # # Generating a heatmap # # # Acquiring the gene list # data(POA_example) # # Signature <- POA_example$POA_Rank_signature # # rowname <- get_gene_symbol(Signature) # # rownames(Signature) <- rowname$rowname # # genes <- rownames(Signature)[1:200] # # #running tisue_scMappR_custom # internal <- tissue_scMappR_custom(genes,Signature,output_directory = "scMappR_Test_custom", toSave = F) # # toPlot <- internal$gene_list_heatmap$geneHeat # # # #Plotting the heatmap # # cex = 0.2 # size of genes # # myheatcol <- grDevices::colorRampPalette(c("lightblue", "white", "orange"))(256) # pheatmap::pheatmap(as.matrix(toPlot), color = myheatcol, scale = "row", fontsize_row = cex, fontsize_col = 10) # # #