Nothing
## ----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)
#
#
#
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