大家好，元旦过得还好吗？之前我们聊过如果富集分析结果不理想，如何选择富集分析的terms，如果不记得，可以看看这三个推文和视频。
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富集分析结果不理想：如何从上千个term中找到自己想要所有term？
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直播五：单细胞GO、KEGG富集分析
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手动选择富集分析结果，真的不合理吗？
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今天我们聊聊富集分析的图如何美化
我们手动选择tems之后，画图如下
ggplot(xx.all %>%dplyr::filter(stringr::str_detect(pattern = "apopto",Description)) %>%# dplyr::mutate(Description = factor(Description, levels = unique(.$Description))) %>%dplyr::filter(stringr::str_detect(negate = TRUE,pattern = "regulation of lymphocyte apop",Description) ) %>%group_by(Description) %>%add_count() %>%dplyr::arrange(dplyr::desc(n),dplyr::desc(Description)) %>%mutate(Description =forcats:: fct_inorder(Description)), #fibri|matrix|collaaes(Cluster, Description)) +geom_point(aes(fill=p.adjust, size=Count), shape=21)+theme_bw()+theme(axis.text.x=element_text(angle=90,hjust = 1,vjust=0.5),axis.text.y=element_text(size = 12),axis.text = element_text(color = 'black', size = 12))+scale_fill_gradient(low="red",high="blue")+labs(x=NULL,y=NULL)# coord_flip()
美化富集分析结果
有人说这图不好看，有点乱。那我稍微更改一下，富集分析的结果会更好看点。主要是Description列变成因子就行啦，这样看上去确实清爽很多：
ggplot(xx.all %>%dplyr::filter(stringr::str_detect(pattern = "apopto",Description)) %>%dplyr::mutate(Description = factor(Description, levels = unique(.$Description))) %>%dplyr::filter(stringr::str_detect(negate = TRUE,pattern = "regulation of lymphocyte apop",Description) ) %>%group_by(Description) %>%add_count() %>%dplyr::arrange(dplyr::desc(n),dplyr::desc(Description)) %>%mutate(Description =forcats:: fct_inorder(Description)), #fibri|matrix|collaaes(Cluster, Description)) +geom_point(aes(fill=p.adjust, size=Count), shape=21)+theme_bw()+theme(axis.text.x=element_text(angle=90,hjust = 1,vjust=0.5),axis.text.y=element_text(size = 12),axis.text = element_text(color = 'black', size = 12))+scale_fill_gradient(low="red",high="blue")+labs(x=NULL,y=NULL)# coord_flip()
