#BiocManager::install('clusterProfiler')
#BiocManager::install("pathview")
#install.packages('tidyverse')
library(clusterProfiler)
library(tidyverse)
data1<-read.table("K2.txt",header = TRUE,row.names = 1)
#head(data1)

				

					
				
				

					
				
				

					
				
				

					
				
			

data1.2<-data1 %>% select_if(is.numeric)
data1.2$Group <- factor(data1$Group,levels=c("CCFM16","Placebo"))
#write.csv(data1.2,"data1.2.csv")
#summary(data1.2)
'''
shapiro.test的零假设是变量符合正态分布，
当结果的p-value > 0.05时，不能拒绝原假设，表明变量服从正态分布
dim(data1)
diff_test <- data1 %>% 
    select_if(is.numeric) 
shapiro.test(diff_test[,1])
Shapiro-Wilk normality test
data:  diff_test[, 1]
W = 0.97972, p-value = 0.297
'''
##  符合正态分布，则进行t-test
diff <- data1.2 %>% 
  select_if(is.numeric) %>%
  map_df(~ broom::tidy(t.test(. ~ Group,data = data1.2)), .id = 'var')
#整合数据
diff$q.value <- p.adjust(diff$p.value,"fdr")
diff$FC<-diff$estimate1/diff$estimate2
diff$logFC<-log2(diff$FC)
p1<-diff[,c(1,14,6)]
newdata1<-na.omit(p1)
#newdata2  <- filter(newdata1, newdata$logFC!= Inf)
#newdata3  <- filter(newdata2, newdata$logFC = Inf)
p2 <- p1[p1$logFC!= "-Inf",]
p3 <- p2[p2$logFC!= "Inf",]
write.csv(newdata3,"p1.csv")
#diff <- diff %>% filter(p.value < 0.05)
##################UP###############
flt <- p3[p3$logFC > 1.1 & p3$p.value < 0.05,]
up_gene<-na.omit(flt)

				

					
				
				

					
				
				

					
				
				

					
				
			

library("pathview")
library(clusterProfiler)
ekegg <- enrichKEGG(gene =up_gene$var,
                    organism = "ko",
                    keyType = "kegg")
View(as.data.frame(ekegg))              
dotplot(ekegg)

				

					
				
				

					
				
				

					
				
				

					
				
			

#browseKEGG(ekegg, "rno05146")
barplot(ekegg, showCategory = 20)

				

					
				
				

					
				
				

					
				
				

					
				
			

dotplot(ekegg, x = "GeneRatio",
        color = "p.adjust", 
        showCategory = 30, 
        split = NULL, font.size = 12,)

				

					
				
				

					
				
				

					
				
				

					
				
			

browseKEGG(ekegg, "ko02025")
#################down##########
flt <- p3[p3$logFC < 1.1 & p3$p.value < 0.05,]
down_gene<-na.omit(flt)
library("pathview")
library(clusterProfiler)
ekegg <- enrichKEGG(gene =down_gene$var,
                    organism = "ko",
                    keyType = "kegg")
View(as.data.frame(ekegg))              
dotplot(ekegg)

				

					
				
				

					
				
				

					
				
				

					
				
			

dotplot(ekegg, x = "GeneRatio",
        color = "p.adjust", 
        showCategory = 30, 
        split = NULL, font.size = 12,)

				

					
				
				

					
				
				

					
				
				

					
				
			

browseKEGG(ekegg, "ko00720")