## ----Loading datasets, message=FALSE------------------------------------- library(fcoex, quietly = TRUE) library(SingleCellExperiment, quietly = TRUE) data("mini_pbmc3k") cat("This is the single cell object we will explore:") mini_pbmc3k ## ----Creating fcoex object, message=FALSE-------------------------------- target <- colData(mini_pbmc3k) target <- target$clusters exprs <- as.data.frame(assay(mini_pbmc3k, 'logcounts')) fc <- new_fcoex(data.frame(exprs),target) ## ----Discretizing dataset, message=FALSE--------------------------------- fc <- discretize(fc, number_of_bins = 8) ## ----Finding cbf modules, message=FALSE---------------------------------- fc <- find_cbf_modules(fc,n_genes = 200, verbose = FALSE, is_parallel = FALSE) ## ----Plotting module networks, message=FALSE----------------------------- fc <- get_nets(fc) # Taking a look at the first two networks: show_net(fc)[["CD79A"]] show_net(fc)[["HLA-DRB1"]] ## ----Saving plots, eval= FALSE, message=FALSE, results='hide'------------ # save_plots(name = "fcoex_vignette", fc,force = TRUE, , directory = "./Plots") ## ----Running ORA analysis, warning=FALSE--------------------------------- gmt_fname <- system.file("extdata", "pathways.gmt", package = "CEMiTool") gmt_in <- pathwayPCA::read_gmt(gmt_fname) fc <- mod_ora(fc, gmt_in) fc <- plot_ora(fc) ## ----Saving plots again, eval= FALSE, message=FALSE, results='hide'----- # save_plots(name = "fcoex_vignette", fc, force = TRUE, directory = "./Plots") ## ----Reclustering , message=FALSE---------------------------------------- fc <- recluster(fc) ## ----Visualizing--------------------------------------------------------- colData(mini_pbmc3k) <- cbind(colData(mini_pbmc3k), `mod_HLA-DRB1` = idents(fc)$`HLA-DRB1`) colData(mini_pbmc3k) <- cbind(colData(mini_pbmc3k), mod_CD79A = idents(fc)$CD79A) library(scater) # Let's see the original clusters plotReducedDim(mini_pbmc3k, dimred="UMAP", colour_by="clusters") library(gridExtra) p1 <- plotReducedDim(mini_pbmc3k, dimred="UMAP", colour_by="mod_HLA-DRB1") p2 <- plotReducedDim(mini_pbmc3k, dimred="UMAP", colour_by="HLA-DRB1") p3 <- plotReducedDim(mini_pbmc3k, dimred="UMAP", colour_by="mod_CD79A") p4 <- plotReducedDim(mini_pbmc3k, dimred="UMAP", colour_by="CD79A") grid.arrange(p1, p2, p3, p4, nrow=2) ## ----Running Seurat pipeline, warning=FALSE------------------------------ library(Seurat) library(fcoex) library(ggplot2) data(pbmc_small) exprs <- data.frame(GetAssayData(pbmc_small)) target <- Idents(pbmc_small) fc <- new_fcoex(data.frame(exprs),target) fc <- discretize(fc) fc <- find_cbf_modules(fc,n_genes = 70, verbose = FALSE, is_parallel = FALSE) fc <- get_nets(fc) gmt_fname <- system.file("extdata", "pathways.gmt", package = "CEMiTool") gmt_in <- pathwayPCA::read_gmt(gmt_fname) fc <- mod_ora(fc, gmt_in) # In Seurat's sample data, pbmc small, no enrichments are found. # That is way plot_ora is commented out. #fc <- plot_ora(fc) ## ----Saving Seurat plots, eval = FALSE----------------------------------- # save_plots(name = "fcoex_vignette_Seurat", fc, force = TRUE, directory = "./Plots") ## ----Plotting and saving reclusters, eval = FALSE----------------------- # # fc <- recluster(fc) # # file_name <- "pbmc3k_recluster_plots.pdf" # directory <- "./Plots/" # # pbmc_small <- RunUMAP(pbmc_small, dims = 1:10) # # pdf(paste0(directory,file_name), width = 3, height = 3) # # print(DimPlot(pbmc_small)) # # for (i in names(module_genes(fc))){ # Idents(pbmc_small ) <- idents(fc)[[i]] # mod_name <- paste0("M", which(names(idents(fc)) == i), " (", i,")") # # plot2 <- DimPlot(pbmc_small, reduction = 'umap', cols = c("darkgreen", "dodgerblue3")) + # ggtitle(mod_name) # print(plot2) # } # dev.off() #