# #——包括= FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - knitr:: opts_chunk美元集(崩溃= TRUE,评论= " # > ",fig.align =“中心”,fig.show =“黑名单”,eval = TRUE,整洁。选择=列表(空白= FALSE,宽度。截止= 60),整齐= TRUE,消息= FALSE,警告= FALSE) # #——install-pkg-bioconductor, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # #安装PFP从github,需要预装biocondutor依赖包#如果!requireNamespace (“BiocManager”,悄悄地= TRUE)) # install.packages (BiocManager) # BiocManager::安装(“项目”)# #——install-pkg-github, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # #安装PFP从github,需要预装biocondutor依赖包#如果需要(devtools)) (! # install.packages (devtools) # devtools:: install_github (“aib-group /项目”)# #——install-database-bioconductor, eval = FALSE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - # # #安装PFP从github,需要预装biocondutor依赖包#如果!requireNamespace (“BiocManager”,悄悄地= TRUE)) # install.packages (BiocManager) # BiocManager::安装(“org.Hs.eg.db”) # #——load-pkg, eval = TRUE,包括= TRUE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -库(PFP) # #——general-pipline, eval = TRUE,包括= TRUE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #加载数据——人类的基因列表;人类的PFPRefnet对象;亲民党#对象测试;不同的基因的列表。数据(“gene_list_hsa”) (“PFPRefnet_hsa”)数据(“PFP_test1”)的数据(“data_std”) #步骤1:计算网络的相似性得分。PFP_test < calc_PFP_score(基因= gene_list_hsa PFPRefnet = PFPRefnet_hsa) #步骤2:排名亲民党分数的途径。rank1 < - rank_PFP(对象= PFP_test total_rank = TRUE, thresh_value = 0.5) # #——the_target_gene, eval = TRUE,包括= TRUE - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - #步骤1:选择路径的最大得分。pathway_select < - refnet_info (rank1) [1, " id "] gene_test <——pathways_score (rank1)美元genes_score [[pathway_select]] $ ENTREZID #步骤2:相关系数得分的优势。 edges_coexp <- get_exp_cor_edges(gene_test,data_std) # Step3: Find the difference genes that are of focus. gene_list2 <- unique(c(edges_coexp$source,edges_coexp$target)) # Step4: Find the edge to focus on. edges_kegg <- get_bg_related_kegg(gene_list2,PFPRefnet=PFPRefnet_hsa, rm_duplicated = TRUE) # Step5: Find the associated network require(org.Hs.eg.db) net_test <- get_asso_net(edges_coexp = edges_coexp, edges_kegg = edges_kegg, if_symbol = TRUE, gene_info_db = org.Hs.eg.db) ## ----a PFP example, fig.height=6, fig.width=7.2, warning=FALSE---------------- plot_PFP(PFP_test) ## ----a rank PFP, fig.height=6, fig.width=7.2, warning=FALSE------------------- plot_PFP(rank1) ## ----echo=FALSE--------------------------------------------------------------- sessionInfo()