## ----包括= false ---------------------------------------------------------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ---- eval=FALSE------------------------------------------------------------------------------------------------＃if（！sireseenamespace（“ biocmanager”，悄悄= true））＃install.packages（“ biocmanager”）＃＃＃＃biocmanager :: install（gseamining'gseamining'）## ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------＃install.packages（“ devtools”）＃如果您尚未安装“ devtools” package＃liberdy（devtools）＃devtools :: install_github（“ oriolarques/gseamining”）## ----评估= false -------------------------------------------------------------------------------------------------------------------------------------------------------------------＃＃一个基因派包含三个功能：＃＃1.＃＃vector：折叠更改或其他类型的数值变量＃＃＃2。姓名向量：每个数字有一个名称，相应的基因ID＃＃3。分类向量：应该以减少顺序排序＃tabletop_p30 < - as.data.frame（tabletop_p30）＃genelist = genelist = tabletop_p30 [，2]＃names（genelist）（genelist）= as.Character（tabletop_p30 [，1]）## --------------------------------------------------------------------------------------------------------------------------------------------------------------------＃库（clusterProfiler）＃＃从msigdb＃gmtc2 < - 读取.gmt文件read.gmt（“ gmt files/c2.all.v7.1.symbols.gmt”）＃gmtc5 < - read.gmt（'gmt files/c5.all.v7.1.symbols.gmts.gmt'）＃gmthall <---read.gmt（'gmt files/h.all.v7.1.symbols.gmt'）＃＃＃合并所有基因集＃gmt_all <-rbind（gmtc2，gmtc2，gmtc5，gmthall，gmthall）## ----------------------------------------------------------------------------------------------------------------------------------------- # GSEA_p30<-GSEA(geneList, TERM2GENE = gmt_all, nPerm = 1000, pvalueCutoff = 0.5) # # # Selection of gene sets with a specific thershold in terms of NES and p.adjust # genesets_sel <- GSEA_p30@result ## ----------------------------------------------------------------------------- # Structure of the data included in the package data('genesets_sel', package = 'GSEAmining') tibble::glimpse(genesets_sel) ## ----------------------------------------------------------------------------- library(GSEAmining) data("genesets_sel", package = 'GSEAmining') gs.filt <- gm_filter(genesets_sel, p.adj = 0.05, neg_NES = 2.6, pos_NES = 2) ## ----setup-------------------------------------------------------------------- # Create an object that will contain the cluster of gene sets. gs.cl <- gm_clust(gs.filt) ## ---- fig.height = 7, fig.width = 7------------------------------------------- gm_dendplot(gs.filt, gs.cl) ## ---- fig.height = 7, fig.width = 7------------------------------------------- gm_dendplot(gs.filt, gs.cl, col_pos = 'orange', col_neg = 'black', rect = TRUE, dend_len = 20, rect_len = 2) ## ---- message = FALSE, fig.height = 7, fig.width = 7-------------------------- gm_enrichterms(gs.filt, gs.cl) ## ---- message = FALSE, fig.height = 7, fig.width = 7-------------------------- gm_enrichterms(gs.filt, gs.cl, clust = FALSE, col_pos = 'chocolate3', col_neg = 'skyblue3') ## ---- message = FALSE, fig.height = 12, fig.width = 7.2----------------------- gm_enrichcores(gs.filt, gs.cl) ## ---- eval=FALSE-------------------------------------------------------------- # gm_enrichreport(gs.filt, gs.cl, output = 'gm_report') ## ----------------------------------------------------------------------------- sessionInfo()