通常我们进行功能富集分析,对基因集进行功能注释的时候,往往会得到大量显著富集的功能,今天小编给大家介绍的simplifyEnrichment包,就可以通过binary cut的方法,将GOSemSim得到的GO相似度矩阵进行划分,从而将GO划分为几个类,通过注释就可以知道每个类对应的功能是什么。 R包安装 ###通过github安装simplifyEnrichment包library(...
##GO:0033993GO:0033993response to lipid50/344909/18866##GO:0019725GO:0019725cellular homeostasis47/344971/18866##GO:0023061GO:0023061signal release33/344564/18866##GO:0055082GO:0055082cellular chemical homeostasis41/344827/18866##GO:0051046GO:0051046regulationofsecretion37/344702/18866##GO:0009725GO:000...
0009725~response to hormone stimulus GOTERM_BP_FAT GO:0015718~monocarboxylic acid transport GOTERM_BP_FAT GO:0032102~negative regulation of response to external stimulus GOTERM_CC_FAT GO:0034702~ion channel complex GOTERM_BP_FAT GO:0042127~regulation of cell proliferation GOTERM_MF_FAT GO:0005184...
使用TBtools做GO富集,会得到以下三个文件 使用Excel打开其中一个,比如比较经常使用的Biological Process,复制p_adjust小于等于0.05的GO term对应的p_adjust值和GO号 黏贴到revigo网站,http://revigo./ 点击START REVIGO,并等待运行结果 在结果窗口中,基于个人生物学知识,调整展示和不展示GO term信息 以上即可输出可用图...
A few GO terms were not in the top 1% and they were filtered out by multi-test adjustments, like the GO term ‘response to hormone stimulus’ (GO:0009725) (H). Open in new tabDownload slideFigure 3. SEACOMPARE analysis based on multiple hypothesis tests (FDRs) and P-values of the ...