library(AnnotationHub)
library(clusterProfiler)
hub <- AnnotationHub()
display(hub)
##查找物种hub号
orgdb <- hub[["AH96879"]]
install.packages('org.HS.eg.db')
##go注释格式改写
library(dplyr)
library(tidyr)
library(reshape2)
library(stringr)
go <- read.csv("D:/go.csv",header = F,sep = ",",na.strings = "--")
View(go)
names(go) <- c("gene_id","GO_term")
go1 <- go%>%separate(col = GO_term,into = paste("new_col",1:50,sep = "_"),sep = ",")%>%
  melt(id.vars="gene_id",value.name = "GO_term")%>%arrange(gene_id)%>%select(c("gene_id","GO_term")) %>% na.omit() %>% 
  write.table(file = "D:/go.csv",quote = F,row.names = F,sep = "/t")
##go富集

go_anno <- read.table("D:/白千层系列/转录组注释/EGG-GO.txt",header = F)##GO注释文件
names(go_anno) <- c('gene_id', 'ID')
library(GO.db)##GO数据库
k <- keys(GO.db,keytype = "GOID")
go_class <- select(GO.db,
       keys = k,
       columns = c("TERM","ONTOLOGY"),
       keytype="GOID")
names(go_class) <- c("ID","Description","Ontology")
go_anno1 <- merge(go_anno,go_class,by = "ID",all.x = T)
go_rich <- enricher(gene = df,
                    TERM2GENE = go_anno1[c('ID', 'gene_id')], 
                    TERM2NAME = go_anno1[c('ID', 'Description')], 
                    pvalueCutoff = 0.05, 
                    pAdjustMethod = 'BH', 
                    qvalueCutoff = 0.2, 
                    maxGSSize = 500)
p <- dotplot(go_rich)
p + scale_color_continuous(low='#f9766d', high='#00bfc4')##改变气泡颜色
cnetplot(egoall,showCategory=10,foldChange=FClist,circular=TRUE,colorEdge=TRUE)write.csv(go_rich,"34_down.csv")
df <- go_rich@result
##GOplot
library(GOplot)
df <- go_rich@result
GO <- df[1:9,c(1,2,8,6)]
GO$geneID <- str_replace_all(GO$geneID,"/",",") ### 修改geneID这一列的分隔符号
names(GO)=c("ID","Term","Genes","adj_pval")
GO$Category = "BP"
genedata = data.frame(ID=union_all,logFC=rnorm(length(union_all),mean = 0,sd=2)) 
circ <- circle_dat(GO,genedata)
##kegg富集
test1 <- read.table("clipboard",header = F,check.names = F,fill=TRUE)

go_rich <- enricher(gene = df,
                    TERM2GENE = kegg2[c('ID', 'gene_id')], 
                    TERM2NAME = kegg2[c('ID', 'Description')], 
                    pvalueCutoff = 0.05, 
                    pAdjustMethod = 'BH', 
                    qvalueCutoff = 0.2, 
                    maxGSSize = 500)
##解析json文件
library(rjson)
library(jsonlite)
library(tidyverse)
KO <- fromJSON("<https://www.kegg.jp/kegg-bin/download_htext?htext=ko00001&format=json&filedir=>")#下载并解析JSON文件
KO$name <- NULL
KO <- as.data.frame(KO) %>% 
  unnest(cols = c("children.name","children.children"),names_repair = tidyr_legacy) %>%#重要函数
  unnest(cols = c("children.name","name","children"),names_repair = tidyr_legacy) %>%
  unnest(cols = c("children.name","name","name1","children"),names_repair = tidyr_legacy)
colnames(KO) <- c("L1","L2","L3","KO") 

emapplot(ora_pt
         ,showCategory=30
         ,color="p.adjust"#映射给条目颜色’pvalue’,’p.adjust’or’qvalue’
         ,shadowtext=TRUE#显示标签阴影
         ,repel=FALSE#解决标签重叠问题
         ,node_label="category"#显示谁的标签’category’,’group’,’all',’none’
         
         #形状控制参数
         ,layout.params=list(layout=NULL#和cnetplot的形状参数一样，后面也是，就不多说了
                               ,coords=NULL#控制位置，需要含2列的data.frame,x是x轴坐标,y是y轴坐标
         )
         
         #控制连线
         ,edge.params=list(show=TRUE#是否显示连线
                             ,min=0.2)#判断两个节点是否相似的阈值
         
         #大小控制参数
         ,cex.params=list(category_node=1#节点大小
                            ,category_label=1#节点标签大小
                            ,line=1#线的粗细
                            #,pie2axis#饼图大小
                            #,label_group#分组标签大小
         )
         
         #控制高亮，和cnetplot同，不再多说
         ,hilight.params=list(category=NULL
                                ,alpha_hilight=1
                                ,alpha_no_hilight=0.3)
         
         #控制聚类
         ,cluster.params=list(cluster=FALSE#对条目聚类
                                ,method=stats::kmeans#聚类方法
                                ,n=NULL#聚类个数
                                ,legend=FALSE#显示聚类图例
                                ,label_style="shadowtext"
                                ,label_words_n=4#聚类标签个数
                                ,label_format=30#最长的字符数，控制折叠
         )
)