跟小新老师学转录组的第四天

rm(list = ls())
library(stringr)

## ====================1.读取数据
# 读取raw count表达矩阵
rawcount <- read.table("data/raw_counts.txt",row.names = 1, 
                       sep = "\t", header = T)
colnames(rawcount)

# 查看表达谱
rawcount[1:4,1:4]

# 去除前的基因表达矩阵情况
dim(rawcount)

# 获取分组信息
group <- read.table("data/filereport_read_run_PRJNA229998_tsv.txt",
                    header = T,sep = "\t", quote = "\"")
colnames(group)

# 提取表达矩阵对应的样本表型信息
group <- group[match(colnames(rawcount), group$run_accession),
               c("run_accession","sample_title")]
group

# 差异分析方案为：Dex vs untreated
group$sample_title <- str_split_fixed(group$sample_title,pattern  = "_", n=2)[,2]
group
write.table(group,file = "data/group.txt",row.names = F,sep = "\t",quote = F)


## =================== 2.表达矩阵预处理
# 过滤低表达基因
keep <- rowSums(rawcount>0) >= floor(0.75*ncol(rawcount))
table(keep)

filter_count <- rawcount[keep,]
filter_count[1:4,1:4]
dim(filter_count)

# 加载edgeR包计算counts per millio(cpm) 表达矩阵
library(edgeR)
express_cpm <- cpm(filter_count)
express_cpm[1:6,1:6]

# 保存表达矩阵和分组结果
save(filter_count, express_cpm, group, file = "data/Step01-airwayData.Rdata")

rm(list = ls())
options(stringsAsFactors = F)

# 加载包，设置绘图参数
library(ggplot2)
library(ggsci)

mythe <- theme_bw() + theme(panel.grid.major=element_blank(),
                            panel.grid.minor=element_blank())


# 加载原始表达的数据
lname <- load(file = "data/Step01-airwayData.Rdata")
lname
exprSet <- log10(as.matrix(express_cpm)+1)
exprSet[1:6,1:6]



## 1.样本表达总体分布-箱式图
# 构造绘图数据
data <- data.frame(expression=c(exprSet),
                   sample=rep(colnames(exprSet),each=nrow(exprSet)))
head(data)

p <- ggplot(data = data, aes(x=sample,y=expression,fill=sample))
p1 <- p + geom_boxplot() + 
  mythe + theme(axis.text.x = element_text(angle = 90)) + 
  xlab(NULL) + ylab("log10(CPM+1)") + scale_fill_nejm()

p1

# 保存图片
png(file = "result/1.sample_boxplot.png",width = 800, height = 900,res=150)
print(p1)
dev.off()

## 2.样本表达总体分布-小提琴图
p2 <- p + geom_violin() +  mythe +
  theme(axis.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90)) + 
  xlab(NULL) + ylab("log10(CPM+1)")+scale_fill_lancet()
p2

# 保存图片
png(file = "result/1.sample_violin.png",width = 800, height = 900,res=150)
print(p2)
dev.off()

## 3.样本表达总体分布-概率密度分布图
m <- ggplot(data=data, aes(x=expression)) 
p3 <- m +  geom_density(aes(fill=sample, colour=sample),alpha = 0.1) + 
  xlab("log10(CPM+1)") + mythe +scale_fill_npg()
p3

# 保存图片
png(file = "result/1.sample_density.png",width = 800, height = 700, res=150)
print(p3)
dev.off()

# 魔幻操作，一键清空
rm(list = ls()) 
options(stringsAsFactors = F)

library(FactoMineR)
library(factoextra)
library(corrplot)
library(pheatmap)
library(tidyverse)

# 加载数据并检查
lname <- load(file = 'data/Step01-airwayData.Rdata')
lname



## 1.样本之间的相关性-层次聚类树----
dat <- log10(express_cpm+1)
dat[1:4,1:4]
dim(dat)
sampleTree <- hclust(dist(t(dat)), method = "average")
plot(sampleTree)

# 提取样本聚类信息
temp <- as.data.frame(cutree(sampleTree,k = 2)) %>% 
  rownames_to_column(var="sample")
temp1 <- merge(temp,group,by.x = "sample",by.y="run_accession")
table(temp1$`cutree(sampleTree, k = 2)`,temp1$sample_title)

# 保存结果
pdf(file = "result/2.sample_Treeplot.pdf",width = 7,height = 6)
plot(sampleTree)
dev.off()

## 2.样本之间的相关性-PCA----
# 第一步，数据预处理
dat <- log10(express_cpm+1)
dat[1:4,1:4]

dat <- as.data.frame(t(dat))
dat_pca <- PCA(dat, graph = FALSE)

group_list <- group[match(group$run_accession,rownames(dat)), 2]
group_list

# geom.ind: point显示点，text显示文字
# palette: 用不同颜色表示分组
# addEllipses: 是否圈起来
mythe <- theme_bw() + 
  theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank()) +
  theme(plot.title = element_text(hjust = 0.5))

p <- fviz_pca_ind(dat_pca,
                  geom.ind = "text",  #point
                  col.ind = group_list, 
                  palette = c("#00AFBB", "#E7B800"), 
                  addEllipses = T,  
                  legend.title = "Groups") + mythe
p

# 保存结果
pdf(file = "result/2.sample_PCA.pdf",width = 6.5,height = 6)
plot(p)
dev.off()

## 3.样本之间的相关性-cor----
# 选择差异变化大的基因算样本相关性
exprSet <- express_cpm
exprSet = exprSet[names(sort(apply(exprSet, 1, mad),decreasing = T)[1:800]),]
dim(exprSet)

# 计算相关性
M <- cor(log10(exprSet+1),method = "spearman")
M

# 构造注释条
anno <- data.frame(group=group$sample_title,row.names = group$run_accession )

# 保存结果
pheatmap(M, display_numbers = T,
         annotation_col = anno,
         fontsize = 10,cellheight = 30,
         cellwidth = 30,cluster_rows = T,
         cluster_cols = T,
         width = 7.5,height = 7)

rm(list = ls())
options(stringsAsFactors = F)

# 加载包
library(edgeR)
library(ggplot2)

# 读取基因表达矩阵信息并查看分组信息和表达矩阵数据
lname <- load(file = "data/Step01-airwayData.Rdata")
lname

# 表达谱
filter_count[1:4,1:4]

# 分组信息
group_list <- group[match(colnames(filter_count),group$run_accession),2]
group_list

# treat vs control
comp <- unlist(strsplit("Dex_vs_untreated",split = "_vs_"))
group_list <- factor(group_list,levels = comp)
group_list
table(group_list)


# 构建线性模型。0代表x线性模型的截距为0
design <- model.matrix(~0+group_list)
rownames(design) <- colnames(filter_count)
colnames(design) <- levels(factor(group_list))
design

# 构建edgeR的DGEList对象
DEG <- DGEList(counts=filter_count, 
               group=factor(group_list))

# 归一化基因表达分布
DEG <- calcNormFactors(DEG)

# 计算线性模型的参数
DEG <- estimateGLMCommonDisp(DEG,design)
DEG <- estimateGLMTrendedDisp(DEG, design)
DEG <- estimateGLMTagwiseDisp(DEG, design)

# 拟合线性模型
fit <- glmFit(DEG, design)

# 进行差异分析
lrt <- glmLRT(fit, contrast=c(1,-1)) 

# 提取过滤差异分析结果
DEG_edgeR <- as.data.frame(topTags(lrt, n=nrow(DEG), adjust.method = "BH"))
head(DEG_edgeR)

# 筛选上下调，设定阈值
fc_cutoff <- 2
fdr <- 0.05

DEG_edgeR$regulated <- "normal"

loc_up <- intersect(which( DEG_edgeR$logFC > log2(fc_cutoff) ),
                    which( DEG_edgeR$FDR < fdr) )

loc_down <- intersect(which(DEG_edgeR$logFC < (-log2(fc_cutoff))),
                      which(DEG_edgeR$FDR < fdr))

DEG_edgeR$regulated[loc_up] <- "up"
DEG_edgeR$regulated[loc_down] <- "down"

table(DEG_edgeR$regulated)


## 添加一列gene symbol
# 方法1：使用包
library(org.Hs.eg.db)
keytypes(org.Hs.eg.db)
columns(org.Hs.eg.db)

library(clusterProfiler)
id2symbol <- bitr(rownames(DEG_edgeR), 
                  fromType = "ENSEMBL", 
                  toType = "SYMBOL", 
                  OrgDb = org.Hs.eg.db)
head(id2symbol)

DEG_edgeR <- cbind(GeneID=rownames(DEG_edgeR),DEG_edgeR)
DEG_edgeR_symbol <- merge(id2symbol,DEG_edgeR,
                          by.x="ENSEMBL",by.y="GeneID",all.y=T)
head(DEG_edgeR_symbol)


# 方法2：gtf文件中得到的id与name关系
# Assembly: GRCh37(hg19) Release: ？
# 使用上课测试得到的count做



# 选择显著差异表达的结果
library(tidyverse)
DEG_edgeR_symbol_Sig <- filter(DEG_edgeR_symbol,regulated!="normal")

# 保存
write.csv(DEG_edgeR_symbol,"result/4.DEG_edgeR_all.csv", row.names = F)
write.csv(DEG_edgeR_symbol_Sig,"result/4.DEG_edgeR_Sig.csv", row.names = F)
save(DEG_edgeR_symbol,file = "data/Step03-edgeR_nrDEG.Rdata")



##====== 检查是否上下调设置错了
# 挑选一个差异表达基因
head(DEG_edgeR_symbol_Sig)

exp <- c(t(express_cpm[match("ENSG00000003402",rownames(express_cpm)),]))
test <- data.frame(value=exp, group=group_list)

ggplot(data=test,aes(x=group,y=value,fill=group)) + geom_boxplot()