rm(list=ls())
setwd("C:\\Users\\TIAN\\Desktop\\sike")

# Packages ----------------------------------------------------------------
library(limma)
library(edgeR)
library(tidyverse)

# Data Import -------------------------------------------------------------
counts <- read.delim("data.txt", row.names = "ID_REF")
targets <- read_tsv("GPL10558-50081.txt", comment = "#") %>% 
  select(ID, ILMN_Gene)

# Data Cleaning -----------------------------------------------------------

d0 <- DGEList(counts)
d0 <- calcNormFactors(d0)
d0$samples$group <- substr(colnames(counts), 8, 8) 
group <- substr(colnames(counts), 8, 8)
# group <- c(rep("A",23),rep("B",14),rep("CDE", 134-23-14))

# Modelling ---------------------------------------------------------------
model_matrix <- model.matrix(~0 + group)
y <- voom(d0, model_matrix, plot = T)
fit <- lmFit(y, model_matrix)

# Comparison --------------------------------------------------------------
contr <- makeContrasts(groupA - groupB, levels = colnames(coef(fit)))
tmp <- contrasts.fit(fit, contr)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
top.table <- top.table %>% 
  rownames_to_column("ID") %>% 
  left_join(targets, by = "ID") %>% 
  mutate(ID = ILMN_Gene) %>% 
  select(-ILMN_Gene)
diffgene <- top.table[with(top.table, (abs(logFC)>=2 & adj.P.Val < 0.05 )),]
write_tsv(top.table,"comparison_A_vs_B.tsv")
write_tsv(diffgene,"DEG_A_vs_B.tsv")

contr <- makeContrasts(groupA - groupC, levels = colnames(coef(fit)))
tmp <- contrasts.fit(fit, contr)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
top.table <- top.table %>% 
  rownames_to_column("ID") %>% 
  left_join(targets, by = "ID") %>% 
  mutate(ID = ILMN_Gene) %>% 
  select(-ILMN_Gene)
diffgene <- top.table[with(top.table, (abs(logFC)>=2 & adj.P.Val < 0.05 )),]
write_tsv(top.table,"comparison_A_vs_C.tsv")
write_tsv(diffgene,"DEG_A_vs_C.tsv")


contr <- makeContrasts(groupB - groupC, levels = colnames(coef(fit)))
tmp <- contrasts.fit(fit, contr)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
top.table <- top.table %>% 
  rownames_to_column("ID") %>% 
  left_join(targets, by = "ID") %>% 
  mutate(ID = ILMN_Gene) %>% 
  select(-ILMN_Gene)
diffgene <- top.table[with(top.table, (abs(logFC)>=2 & adj.P.Val < 0.05 )),]
write_tsv(top.table,"comparison_B_vs_C.tsv")
write_tsv(diffgene,"DEG_B_vs_C.tsv")

contr <- makeContrasts(groupC - groupD, levels = colnames(coef(fit)))
tmp <- contrasts.fit(fit, contr)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
top.table <- top.table %>% 
  rownames_to_column("ID") %>% 
  left_join(targets, by = "ID") %>% 
  mutate(ID = ILMN_Gene) %>% 
  select(-ILMN_Gene)
diffgene <- top.table[with(top.table, (abs(logFC)>=1 & adj.P.Val < 0.05 )),]
write_tsv(top.table,"comparison_C_vs_D.tsv")
write_tsv(diffgene,"DEG_C_vs_D.tsv")

group <- c(rep("A",23),rep("B",14),rep("CDE", 134-23-14))
model_matrix <- model.matrix(~0 + group)
y <- voom(d0, model_matrix, plot = T)
fit <- lmFit(y, model_matrix)
contr <- makeContrasts(groupA - groupCDE, levels = colnames(coef(fit)))
tmp <- contrasts.fit(fit, contr)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
top.table <- top.table %>% 
  rownames_to_column("ID") %>% 
  left_join(targets, by = "ID") %>% 
  mutate(ID = ILMN_Gene) %>% 
  select(-ILMN_Gene)
diffgene <- top.table[with(top.table, (abs(logFC)>=2 & adj.P.Val < 0.05 )),]
write_tsv(top.table,"comparison_A_vs_CDE.tsv")
write_tsv(diffgene,"DEG_A_vs_CDE.tsv")

group <- c(rep("A",23),rep("B",14),rep("CDE", 134-23-14))
model_matrix <- model.matrix(~0 + group)
y <- voom(d0, model_matrix, plot = T)
fit <- lmFit(y, model_matrix)
contr <- makeContrasts(groupB - groupCDE, levels = colnames(coef(fit)))
tmp <- contrasts.fit(fit, contr)
tmp <- eBayes(tmp)
top.table <- topTable(tmp, sort.by = "P", n = Inf)
top.table <- top.table %>% 
  rownames_to_column("ID") %>% 
  left_join(targets, by = "ID") %>% 
  mutate(ID = ILMN_Gene) %>% 
  select(-ILMN_Gene)
diffgene <- top.table[with(top.table, (abs(logFC)>=2 & adj.P.Val < 0.05 )),]
write_tsv(top.table,"comparison_B_vs_CDE.tsv")
write_tsv(diffgene,"DEG_B_vs_CDE.tsv")

# The original intention of this study was to divide the samples into five groups, including normal artery (group A), AAA neck (group B), small AAA with aortic diameter ≤ 55 mm (group C), large AAA with aortic diameter > 55 mm (group D) and ruptured AAA with unknown aortic diameter (group E). Key genes of the AAA progression were analyzed by intersecting the DEGs of each group. However, there were very few DEGs between group C and group D. It seems to indicate that the progression of AAA may only be caused by mechanical stress rather than a phased change at the molecular biological level. Therefore, we finally performed the bioinformatic analysis on the circRNA-miRNA-mRNA network by comparing NOR and AAA.