miRNA和mRNA预测

# mirna和mrna互相预测
# BiocManager::install("multiMiR")
library(multiMiR)
# 开始计算，全程联网，mirna,target决定预测方向，空值时作为目标
example3 <- get_multimir(org = "hsa",
                        mirna = "hsa-miR-xxx-5p",          # 你的miRNA  
                        # target = "XXXX",                      #  你的mRNA
                        table = "predicted",
                        predicted.cutoff = 35,
                        predicted.cutoff.type = "p",
                        predicted.site = "all")
# 提取结果
example3_result <- example3@data
example3_result

# 查看各个数据库结果数
example3@data$database %>% table()
# 画图
library(VennDiagram)
library(tidyverse)
db <- unique(example3_result$database)
db
# 代码优化-------------------------------------------
# 1、每个数据库得结果分开，放入一个list
res_single_db <- db %>% lapply(function(x){example3_result %>% filter(database == x)})
names(res_single_db) <- db
res_single_db
# 2、每个数据库的预测目标分开，生成一个list
to_venn <- res_single_db %>% lapply(function(x){
 unique(x$target_symbol)
})
names(to_venn) <- db
to_venn
# 3、提取想要使用的目标向量，此步不能省略
venn_list <-list(
 diana_microt = to_venn$diana_microt,
 elmmo        = to_venn$elmmo,
 # microcosm  = to_venn$microcosm,
 miranda      = to_venn$miranda,
 # mirdb      = to_venn$mirdb,
 # pictar     = to_venn$pictar,
 pita         = to_venn$pita,
 targetscan   = to_venn$targetscan
)

# 4、画图，不能每个库的结果都用，可能没有结果
veenplot1 <- venn.diagram(
 x = veen_list,
 filename = ".\\plots\\venn1.pdf",
 # disable.logging = TRUE,
 ext.text = TRUE,
 ext.line.lwd = 2,
 ext.dist = -0.15,
 ext.length = 0.9,
 ext.pos = -4,
 # inverted = TRUE,
 cex = 1,
 cat.cex = 1,
 # rotation.degree = 45,
 main = "Complex Venn Diagram",
 # sub = "Featuring: rotation and external lines",
 main.cex = 1,
 sub.cex = 0.9
)


# 5、用循环求交集，代码来自知乎，解螺旋
for (i in 1:length(veen_list)){
 if(i==1){
   intergenes <- veen_list[[1]]
 }
 else{
   intergenes <- intersect(intergenes,veen_list[[i]])
 }
}
# 查看结果
intergenes