原本还有第四个部分，小洁老师讲了另一个R包下载表达矩阵和临床信息的，
TCGA-4.使用RTCGA包获取数据
但是这个包有个缺点就是数据更新不及时，因此当时看到时候我就没有跟学了。直接跳到第五步TCGA-5.（转录组）差异分析三大R包及其结果对比
但是呢，由于没跟学第四步这一步获取数据并做数据清洗的时候出了问题，一直没能完成，后来昨天花了点时间学了冰糖在菜鸟团的推文也是小洁老师的第四步教程相关的内容，对比来看一步步调试，再加上从技能树推文得到的小洁老师的画图函数后，终于完成了第五步的学习。
还是很有收获的。

1.提前准备安装和加载R包

rm(list = ls())
options(stringsAsFactors = F)
if(!require(stringr))install.packages('stringr')
if(!require(ggplotify))install.packages("ggplotify")
if(!require(patchwork))install.packages("patchwork")
if(!require(cowplot))install.packages("cowplot")
if(!require(DESeq2))install.packages('DESeq2')
if(!require(edgeR))install.packages('edgeR')
if(!require(limma))install.packages('limma')

2.准备数据

本示例的数据是TCGA-KIRC的表达矩阵。tcga样本编号14-15位是隐藏分组信息的,详见：
TCGA的样本id里藏着分组信息

TCGA样本id，分组信息是在这个id的第14-15位，01-09是tumor，10-29是normal。

#TCGA-KIRC
library(TCGAbiolinks)
#可以查看所有支持的癌症种类的缩写
#TCGAbiolinks:::getGDCprojects()$project_id
#还是选择之前的例子
cancer_type="TCGA-KIRC"
clinical <- GDCquery_clinic(project = cancer_type, type = "clinical")
clinical[1:4,1:4]
dim(clinical)

query <- GDCquery(project = cancer_type, 
                  data.category = "Transcriptome Profiling", 
                  data.type = "miRNA Expression Quantification", 
                  workflow.type = "BCGSC miRNA Profiling")
GDCdownload(query, method = "api", files.per.chunk = 50)
expdat <- GDCprepare(query = query)
expdat[1:3,1:3]
library(tibble)
rownames(expdat) <- NULL
expdat <- column_to_rownames(expdat,var = "miRNA_ID")
expdat[1:3,1:3]
exp = t(expdat[,seq(1,ncol(expdat),3)])
exp[1:4,1:4]
expr=exp
rowName <- str_split(rownames(exp),'_',simplify = T)[,3]
expr<- apply(expr,2,as.numeric) 
expr<- na.omit(expr)
dim(expr)
expr <- expr[,apply(expr, 2,function(x){sum(x>1)>10})]
rownames(expr) <- rowName
dim(expr)
expr[1:4,1:4]
save(expr,clinical,file = "tcga-kirc-download.Rdata")
rm(list = ls())
load("tcga-kirc-download.Rdata") #获取初步下载数据。
meta <- clinical
colnames(meta)
meta <- meta[,c("submitter_id","vital_status",
                "days_to_death","days_to_last_follow_up",
                "race",
                "age_at_diagnosis",
                "gender" ,
                "ajcc_pathologic_stage")]
expr=t(expr)
expr[1:4,1:4]
group_list <- ifelse(as.numeric(str_sub(colnames(expr),14,15))<10,"tumor","normal")
group_list <- factor(group_list,levels = c("normal","tumor"))

table(group_list)
# normal  tumor 
# 71    545
save(expr,group_list,file = "tcga-kirc-raw.Rdata")

由于不知道小洁老师做了怎样的过滤，我得到的结果不同
我觉得应该是在mata这个代码步骤后面选择一个指标过滤掉一些数据。
先放着，这个代码在这个步骤中没有用到。以后应该会用到。
由于不会自己写代码，后面的分析基本上就是走的小洁老师教程的内容。

3.三大R包的差异分析

#Deseq2
library(DESeq2)
colData <- data.frame(row.names =colnames(expr), 
                      condition=group_list)
dds <- DESeqDataSetFromMatrix(
  countData = expr,
  colData = colData,
  design = ~ condition)
#参考因子应该是对照组 dds$condition <- relevel(dds$condition, ref = "untrt")

dds <- DESeq(dds)
# 两两比较
res <- results(dds, contrast = c("condition",rev(levels(group_list))))
resOrdered <- res[order(res$pvalue),] # 按照P值排序
DEG <- as.data.frame(resOrdered)
head(DEG)
# 去除NA值
DEG <- na.omit(DEG)

#添加change列标记基因上调下调
#logFC_cutoff <- with(DEG,mean(abs(log2FoldChange)) + 2*sd(abs(log2FoldChange)) )
logFC_cutoff <- 1
DEG$change = as.factor(
  ifelse(DEG$pvalue < 0.05 & abs(DEG$log2FoldChange) > logFC_cutoff,
         ifelse(DEG$log2FoldChange > logFC_cutoff ,'UP','DOWN'),'NOT')
)
head(DEG)

DESeq2_DEG <- DEG

#edgeR
library(edgeR)

dge <- DGEList(counts=expr,group=group_list)
dge$samples$lib.size <- colSums(dge$counts)
dge <- calcNormFactors(dge) 

design <- model.matrix(~0+group_list)
rownames(design)<-colnames(dge)
colnames(design)<-levels(group_list)

dge <- estimateGLMCommonDisp(dge,design)
dge <- estimateGLMTrendedDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)

fit <- glmFit(dge, design)
fit2 <- glmLRT(fit, contrast=c(-1,1)) 

DEG=topTags(fit2, n=nrow(expr))
DEG=as.data.frame(DEG)
logFC_cutoff <- with(DEG,mean(abs(logFC)) + 2*sd(abs(logFC)) )
logFC_cutoff <- 1
DEG$change = as.factor(
  ifelse(DEG$PValue < 0.05 & abs(DEG$logFC) > logFC_cutoff,
         ifelse(DEG$logFC > logFC_cutoff ,'UP','DOWN'),'NOT')
)
head(DEG)
table(DEG$change)
edgeR_DEG <- DEG

#limma-voom
library(limma)

design <- model.matrix(~0+group_list)
colnames(design)=levels(group_list)
rownames(design)=colnames(expr)

dge <- DGEList(counts=expr)
dge <- calcNormFactors(dge)
logCPM <- cpm(dge, log=TRUE, prior.count=3)

v <- voom(dge,design, normalize="quantile")
fit <- lmFit(v, design)

constrasts = paste(rev(levels(group_list)),collapse = "-")
cont.matrix <- makeContrasts(contrasts=constrasts,levels = design) 
fit2=contrasts.fit(fit,cont.matrix)
fit2=eBayes(fit2)

DEG = topTable(fit2, coef=constrasts, n=Inf)
DEG = na.omit(DEG)
#logFC_cutoff <- with(DEG,mean(abs(logFC)) + 2*sd(abs(logFC)) )
logFC_cutoff <- 1
DEG$change = as.factor(
  ifelse(DEG$P.Value < 0.05 & abs(DEG$logFC) > logFC_cutoff,
         ifelse(DEG$logFC > logFC_cutoff ,'UP','DOWN'),'NOT')
)
head(DEG)
limma_voom_DEG <- DEG
save(DESeq2_DEG,edgeR_DEG,limma_voom_DEG,group_list,file = "DEG.Rdata")

#差异分析结果的可视化
rm(list = ls())
load("tcga-kirc-raw.Rdata")
load("DEG.Rdata")
source("3-plotfunction.R")
logFC_cutoff <- 1
expr[1:4,1:4]
dat = log(expr+1)
pca.plot = draw_pca(dat,group_list)

cg1 = rownames(DESeq2_DEG)[DESeq2_DEG$change !="NOT"]
cg2 = rownames(edgeR_DEG)[edgeR_DEG$change !="NOT"]
cg3 = rownames(limma_voom_DEG)[limma_voom_DEG$change !="NOT"]

h1 = draw_heatmap(expr[cg1,],group_list)
h2 = draw_heatmap(expr[cg2,],group_list)
h3 = draw_heatmap(expr[cg3,],group_list)

v1 = draw_volcano(test = DESeq2_DEG[,c(2,5,7)],pkg = 1)
v2 = draw_volcano(test = edgeR_DEG[,c(1,4,6)],pkg = 2)
v3 = draw_volcano(test = limma_voom_DEG[,c(1,4,7)],pkg = 3)

library(patchwork)
(h1 + h2 + h3) / (v1 + v2 + v3) +plot_layout(guides = 'collect')

#(v1 + v2 + v3) +plot_layout(guides = 'collect')
ggsave("heat_volcano.png",width = 21,height = 9)
#三大R包差异基因对比
# 三大R包差异基因交集
UP=function(df){
  rownames(df)[df$change=="UP"]
}
DOWN=function(df){
  rownames(df)[df$change=="DOWN"]
}

up = intersect(intersect(UP(DESeq2_DEG),UP(edgeR_DEG)),UP(limma_voom_DEG))
down = intersect(intersect(DOWN(DESeq2_DEG),DOWN(edgeR_DEG)),DOWN(limma_voom_DEG))

hp = draw_heatmap(expr[c(up,down),],group_list)

#上调、下调基因分别画维恩图

up.plot <- venn(UP(DESeq2_DEG),UP(edgeR_DEG),UP(limma_voom_DEG),
                "UPgene"
)
down.plot <- venn(DOWN(DESeq2_DEG),DOWN(edgeR_DEG),DOWN(limma_voom_DEG),
                  "DOWNgene"
)

library(cowplot)
library(ggplotify)
up.plot = as.ggplot(as_grob(up.plot))
down.plot = as.ggplot(as_grob(down.plot))
library(patchwork)
#up.plot + down.plot

pca.plot + hp+up.plot +down.plot
ggsave("deg.png",height = 10,width = 10)

整个流程走完得到的结果如下：

热图火山图

PCA，热图，韦恩图