## ----包括=假------------------------------------------------- knitr :: opts_chunk $ set（collass = true，注释=“＃>”）## ----安装--------------------------------------------------------------------------------如果（！quickenamespace（“biocmanager”，squally = true））install.packages（“biocmanager”）##这将安装biocmanager包biocmanager ::安装（“lrcell”）## -- 设置 - - - - - - - - - - - - - - - - - - - - - - - ----------------------图书馆（lrcell）## ----------------------------------------------------------------------------------------- ##用于安装实验室＃biocmanager ::安装（“实验室”）##查询数据库（实验室）eh < - 实验室::实验室（）eh < - annotationhub ::查询（eh，lrcelltypemarkers“）##查询lrcelltypemarkers包eh ##这将列出eh编号来访问计算的基因浓缩分数##获得鼠标脑额相皮质富集的基因浓缩.g < - eh [[eh4548“] marker.g < - get_markergenes（富集，见过hod =“lr”，topn = 100）## ---- eval = false ------------------------------------------------------- ##---- Lrcell_gene_enriched_scores（expr，annot，power = 1，parallel =真，n.cores = 4）## ----示例-----------------------------------------------------------------＃加载示例批量数据数据（“example_gene_pvals”）head（example_gene_pvals，n = 5）## ---- LRCell ------------------------------------------------------------- res <- LRcell(gene.p = example_gene_pvals, marker.g = NULL, species = "mouse", region = "FC", method = "LiR") FC_res <- res$FC # exclude leading genes for a better view sub_FC_res <- subset(FC_res, select=-lead_genes) head(sub_FC_res) ## ----plot_LRcell, fig.width=8, fig.height=6, dpi=120-------------------------- plot_manhattan_enrich(FC_res, sig.cutoff = .05, label.topn = 5) ## ----download_marker---------------------------------------------------------- library(ExperimentHub) eh <- ExperimentHub::ExperimentHub() ## use ExperimentHub to download data eh <- query(eh, "LRcellTypeMarkers") enriched_genes <- eh[["EH4548"]] # use title ID which indicates FC region # get marker genes for LRcell in logistic regression FC_marker_genes <- get_markergenes(enriched_genes, method="LR", topn=100) # to have a glance of the marker gene list head(lapply(FC_marker_genes, head)) ## ----LRcellCore--------------------------------------------------------------- res <- LRcellCore(gene.p = example_gene_pvals, marker.g = FC_marker_genes, method = "LR", min.size = 5, sig.cutoff = 0.05) ## curate cell types res$cell_type <- unlist(lapply(strsplit(res$ID, '\\.'), '[', 2)) head(subset(res, select=-lead_genes)) ## ----plot_LRcellCore, fig.width=10, fig.height=6, dpi=120--------------------- plot_manhattan_enrich(res, sig.cutoff = .05, label.topn = 5) ## ----echo=FALSE--------------------------------------------------------------- # generate a simulated gene*cell read counts matrix n.row <- 3; n.col <- 10 sim.expr <- matrix(0, nrow=n.row, ncol=n.col) rownames(sim.expr) <- paste0("gene", 1:n.row) colnames(sim.expr) <- paste0("cell", 1:n.col) # generate a simulated annotation for cells sim.annot <- c(rep("celltype1", 3), rep("celltype2", 3), rep("celltype3", 4)) names(sim.annot) <- colnames(sim.expr) sim.expr['gene1', ] <- c(3, 0, 2, 8, 10, 6, 1, 0, 0, 2) # marker gene for celltype2 sim.expr['gene2', ] <- c(7, 5, 8, 1, 0, 5, 2, 3, 2, 1) # marker gene for celltype1 sim.expr['gene3', ] <- c(8, 10, 6, 7, 8, 9, 5, 8, 6, 8) # house keeping ## ----example_expr------------------------------------------------------------- # print out the generated expression matrix print(sim.expr) # print out the cell-type annotation print(sim.annot) ## ----marker_gene_selection---------------------------------------------------- # generating the enrichment score enriched_res <- LRcell_gene_enriched_scores(expr = sim.expr, annot = sim.annot, parallel = FALSE) enriched_res ## ----------------------------------------------------------------------------- marker_res <- get_markergenes(enriched.g = enriched_res, method = "LR", topn=1) marker_res ## ----session_info------------------------------------------------------------- sessionInfo()