生信技能树GEO数据挖掘直播配套笔记

if(!require("BiocManager")) install.packages("BiocManager",update = F,ask = F)
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")
cran_packages <- c('tidyr',
                   'tibble',
                   'dplyr',
                   'stringr',
                   'ggplot2',
                   'ggpubr',
                   'factoextra',
                   'FactoMineR',
                   'devtools',
                   'cowplot',
                   'patchwork',
                   'basetheme',
                   'paletteer',
                   'AnnoProbe',
                   'ggthemes',
                   'VennDiagram',
                   'tinyarray') 
Biocductor_packages <- c('GEOquery',
                         'hgu133plus2.db',
                         'ggnewscale',
                         "limma",
                         "impute",
                         "GSEABase",
                         "GSVA",
                         "clusterProfiler",
                         "org.Hs.eg.db",
                         "preprocessCore",
                         "enrichplot",
                         "ggplotify")

for (pkg in cran_packages){
  if (! require(pkg,character.only=T) ) {
    install.packages(pkg,ask = F,update = F)
    require(pkg,character.only=T) 
  }
}


for (pkg in Biocductor_packages){
  if (! require(pkg,character.only=T) ) {
    BiocManager::install(pkg,ask = F,update = F)
    require(pkg,character.only=T) 
  }
}

#前面的所有提示和报错都先不要管。主要看这里
for (pkg in c(Biocductor_packages,cran_packages)){
  require(pkg,character.only=T) 
}
#没有任何提示就是成功了，如果有warningxx包不存在，用library检查一下。
#library报错，就单独安装。

#数据下载
rm(list = ls())
library(GEOquery)
#先去网页确定是否是表达芯片数据，不是的话不能用本流程。
gse_number = "GSE56649"
eSet <- getGEO(gse_number, destdir = '.', getGPL = F)
class(eSet)
length(eSet)
eSet = eSet[[1]]
#(1)提取表达矩阵exp
exp <- exprs(eSet)
dim(exp)
exp[1:4,1:4]
#检查矩阵是否正常，如果是空的就会报错，空的和有负值的、有异常值的矩阵需要处理原始数据。
#如果表达矩阵为空，大多数是转录组数据，不能用这个流程(后面另讲)。
#自行判断是否需要log
exp = log2(exp+1)
boxplot(exp)
# 将原始数据标准化（针对画出的图水平不一致）
exp =normalizeBetweenArrays(exp)
boxplot(exp)

#(2)提取临床信息
pd <- pData(eSet)
#(3)让exp列名与pd的行名顺序完全一致
p = identical(rownames(pd),colnames(exp));p
if(!p) exp = exp[,match(rownames(pd),colnames(exp))]
#(4)提取芯片平台编号
gpl_number <- eSet@annotation;gpl_number
save(gse_number,pd,exp,gpl_number,file = "step1output.Rdata")

# Group(实验分组)和ids(探针注释)
rm(list = ls())  
load(file = "step1output.Rdata")
library(stringr)
# 标准流程代码是二分组，多分组数据的分析后面另讲
# 生成Group向量的三种常规方法，三选一，选谁就把第几个逻辑值写成T，另外两个为F。如果三种办法都不适用，可以继续往后写else if
if(F){
  # 1.Group----
  # 第一种方法，有现成的可以用来分组的列
  Group = pd$`disease state:ch1` 
}else if(F){
  # 第二种方法，自己生成
  Group = c(rep("RA",times=13),
            rep("control",times=9))
  Group = rep(c("RA","control"),times = c(13,9))
}else if(T){
  # 第三种方法，使用字符串出理的函数获取分组
  Group=ifelse(str_detect(pd$source_name_ch1,"control"),
               "control",
               "RA")
}

# 需要把Group转换成因子，并设置参考水平，指定levels，对照组在前，处理组在后
Group = factor(Group,levels = c("control","RA"))
Group

#2.探针注释的获取-----------------
#捷径
library(tinyarray) # 这个包为生信技能树老师所写
find_anno(gpl_number)
ids <- AnnoProbe::idmap('GPL570')
#四种方法，方法1里找不到就从方法2找，以此类推。
#方法1 BioconductorR包(最常用)
gpl_number 
#http://www.bio-info-trainee.com/1399.html
if(!require(hgu133plus2.db))BiocManager::install("hgu133plus2.db")
library(hgu133plus2.db)
ls("package:hgu133plus2.db")
ids <- toTable(hgu133plus2SYMBOL)
head(ids)
# 方法2 读取GPL网页的表格文件，按列取子集
##https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL570
if(F){
  #注：表格读取参数、文件列名不统一，活学活用，有的表格里没有symbol列，也有的GPL平台没有提供注释表格
  b = read.table("GPL570-55999.txt",header = T,
                 quote = "\"",sep = "\t",check.names = F)
  colnames(b)
  ids2 = b[,c("ID","Gene Symbol")]
  colnames(ids2) = c("probe_id","symbol")
  ids2 = ids2[ids2$symbol!="" & !str_detect(ids2$symbol,"///"),]
}

# 方法3 官网下载注释文件并读取
##http://www.affymetrix.com/support/technical/byproduct.affx?product=hg-u133-plus
# 方法4 自主注释 
#https://mp.weixin.qq.com/s/mrtjpN8yDKUdCSvSUuUwcA
save(exp,Group,ids,gse_number,file = "step2output.Rdata")

rm(list = ls())  
load(file = "step1output.Rdata")
load(file = "step2output.Rdata")
#输入数据：exp和Group
#Principal Component Analysis
#http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/112-pca-principal-component-analysis-essentials

# 1.PCA 图----
dat=as.data.frame(t(exp))
library(FactoMineR)
library(factoextra) 
dat.pca <- PCA(dat, graph = FALSE)
pca_plot <- fviz_pca_ind(dat.pca,
                         geom.ind = "point", # show points only (nbut not "text")
                         col.ind = Group, # color by groups
                         palette = c("#00AFBB", "#E7B800"),
                         addEllipses = TRUE, # Concentration ellipses
                         legend.title = "Groups"
)
pca_plot
save(pca_plot,file = "pca_plot.Rdata")

# 2.top 1000 sd 热图---- 
cg=names(tail(sort(apply(exp,1,sd)),1000))
n=exp[cg,]

# 直接画热图，对比不鲜明
library(pheatmap)
annotation_col=data.frame(group=Group)
rownames(annotation_col)=colnames(n) 
pheatmap(n,
         show_colnames =F,
         show_rownames = F,
         annotation_col=annotation_col
)

# 按行标准化
pheatmap(n,
         show_colnames =F,
         show_rownames = F,
         annotation_col=annotation_col,
         scale = "row",
         breaks = seq(-3,3,length.out = 100)
         ) 
dev.off()

# 关于scale的进一步学习：zz.scale.R

require(stats)
# 1.示例数据
x <- matrix(sample(1:30,30), ncol = 6)
rownames(x) = paste0("gene",1:5)
colnames(x) = paste0("sample",1:6)

# 2.标准化
scale(x) #函数只能按列标准化，但是我们需要按行
x
y = t(scale(t(x)))

# 3.标准化前后，某gene的表达量点图比较，大小趋势不变。

par(mfrow = c(2,2))
plot(x[1,])
plot(y[1,])
plot(x[2,])
plot(y[2,])

rm(list = ls())
load("step2output.Rdata")
#保留最大值
exp2 = exp[ids$probe_id,]
identical(ids$probe_id,rownames(exp2))
ids = ids[order(rowSums(exp2),decreasing = T),]
ids = ids[!duplicated(ids$symbol),];nrow(ids)
# 拿这个ids去inner_join

#求平均值
rm(list = ls())
load("step2output.Rdata")
exp3 = exp[ids$probe_id,]
rownames(exp3) = ids$symbol
exp3[1:4,1:4]
exp4 = limma::avereps(exp3)

# 此时拿到的exp4已经是一个基因为行名的表达矩阵，直接差异分析，不再需要inner_join 

rm(list = ls()) 
load(file = "step2output.Rdata")
#差异分析，用limma包来做
#需要表达矩阵和Group，不需要改
library(limma)
design=model.matrix(~Group)
fit=lmFit(exp,design)
fit=eBayes(fit)
deg=topTable(fit,coef=2,number = Inf)

#为deg数据框添加几列
#1.加probe_id列，把行名变成一列
library(dplyr)
deg <- mutate(deg,probe_id=rownames(deg))
#2.加上探针注释
ids = ids[!duplicated(ids$symbol),]
#其他去重方式在zz.去重方式.R
deg <- inner_join(deg,ids,by="probe_id")
nrow(deg)

#3.加change列,标记上下调基因
logFC_t=1
P.Value_t = 0.05
k1 = (deg$P.Value < P.Value_t)&(deg$logFC < -logFC_t)
k2 = (deg$P.Value < P.Value_t)&(deg$logFC > logFC_t)
deg <- mutate(deg,change = ifelse(k1,"down",ifelse(k2,"up","stable")))
table(deg$change)
#4.加ENTREZID列，用于富集分析（symbol转entrezid，然后inner_join）
library(clusterProfiler)
library(org.Hs.eg.db)
s2e <- bitr(deg$symbol, 
            fromType = "SYMBOL",
            toType = "ENTREZID",
            OrgDb = org.Hs.eg.db)#人类
#其他物种http://bioconductor.org/packages/release/BiocViews.html#___OrgDb
deg <- inner_join(deg,s2e,by=c("symbol"="SYMBOL"))
save(Group,deg,logFC_t,P.Value_t,gse_number,file = "step4output.Rdata")

rm(list = ls()) 
load(file = "step1output.Rdata")
load(file = "step4output.Rdata")
#1.火山图----
library(dplyr)
library(ggplot2)
dat  = deg[!duplicated(deg$symbol),]

p <- ggplot(data = dat, 
            aes(x = logFC, 
                y = -log10(P.Value))) +
  geom_point(alpha=0.4, size=3.5, 
             aes(color=change)) +
  ylab("-log10(Pvalue)")+
  scale_color_manual(values=c("blue", "grey","red"))+
  geom_vline(xintercept=c(-logFC_t,logFC_t),lty=4,col="black",lwd=0.8) +
  geom_hline(yintercept = -log10(P.Value_t),lty=4,col="black",lwd=0.8) +
  theme_bw()
p

for_label <- dat%>% 
  filter(symbol %in% c("HADHA","LRRFIP1"))

volcano_plot <- p +
  geom_point(size = 3, shape = 1, data = for_label) +
  ggrepel::geom_label_repel(
    aes(label = symbol),
    data = for_label,
    color="black"
  )
volcano_plot

#2.差异基因热图----

load(file = 'step2output.Rdata')
# 表达矩阵行名替换
exp = exp[dat$probe_id,]
rownames(exp) = dat$symbol
if(F){
  #全部差异基因
  cg = dat$symbol[dat$change !="stable"]
  length(cg)
}else{
  #取前10上调和前10下调
  library(dplyr)
  dat2 = dat %>%
    filter(change!="stable") %>%
    arrange(logFC)
  cg = c(head(dat2$symbol,10),
         tail(dat2$symbol,10))
  }
n=exp[cg,]
dim(n)

#差异基因热图
library(pheatmap)
annotation_col=data.frame(group=Group)
rownames(annotation_col)=colnames(n) 
heatmap_plot <- pheatmap(n,show_colnames =F,
                         scale = "row",
                         #cluster_cols = F, 
                         annotation_col=annotation_col,
                         breaks = seq(-3,3,length.out = 100)
) 
heatmap_plot
#拼图
library(patchwork)
library(ggplotify)
volcano_plot +as.ggplot(heatmap_plot)

# f <- volcano_plot +as.ggplot(heatmap_plot)
# ggsave(f,filename = "./pin.pdf",width = 15,height = 15)

# 3.感兴趣基因的相关性----
library(corrplot)
g = deg$symbol[1:10] # 换成自己感兴趣的基因
g

M = cor(t(exp[g,]))
pheatmap(M)

library(paletteer)
my_color = rev(paletteer_d("RColorBrewer::RdYlBu"))
my_color = colorRampPalette(my_color)(10)
corrplot(M, type="upper",
         method="pie",
         order="hclust", 
         col=my_color,
         tl.col="black", 
         tl.srt=45)
library(cowplot)
cor_plot <- recordPlot() 

# 拼图
load("pca_plot.Rdata")
plot_grid(pca_plot,cor_plot,
          volcano_plot,heatmap_plot$gtable)
dev.off()
#保存
pdf("deg.pdf")
plot_grid(pca_plot,cor_plot,
          volcano_plot,heatmap_plot$gtable)
dev.off()

rm(list = ls())  
load(file = 'step4output.Rdata')
library(clusterProfiler)
library(ggthemes)
library(org.Hs.eg.db)
library(dplyr)
library(ggplot2)
library(stringr)
library(enrichplot)

# 1.GO 富集分析----

#(1)输入数据
gene_up = deg$ENTREZID[deg$change == 'up'] 
gene_down = deg$ENTREZID[deg$change == 'down'] 
gene_diff = c(gene_up,gene_down)

#(2)富集
#以下步骤耗时很长，设置了存在即跳过
if(!file.exists(paste0(gse_number,"_GO.Rdata"))){
  ego <- enrichGO(gene = gene_diff,
                  OrgDb= org.Hs.eg.db,
                  ont = "ALL",
                  readable = TRUE)
  ego_BP <- enrichGO(gene = gene_diff,
                  OrgDb= org.Hs.eg.db,
                  ont = "BP",
                  readable = TRUE)
  #ont参数：One of "BP", "MF", and "CC" subontologies, or "ALL" for all three.
  save(ego,ego_BP,file = paste0(gse_number,"_GO.Rdata"))
}
load(paste0(gse_number,"_GO.Rdata"))  ## 这个很有用

#(3)可视化
#条带图
barplot(ego)
#气泡图
dotplot(ego)

dotplot(ego, split = "ONTOLOGY", font.size = 10, 
        showCategory = 5) + 
  facet_grid(ONTOLOGY ~ ., space = "free_y",scales = "free_y") + 
  scale_y_discrete(labels = function(x) str_wrap(x, width = 45))

#geneList 用于设置下面图的颜色
geneList = deg$logFC
names(geneList)=deg$ENTREZID

#(3)展示top通路的共同基因，要放大看。
#Gene-Concept Network
cnetplot(ego,categorySize="pvalue", foldChange=geneList,colorEdge = TRUE)
cnetplot(ego, showCategory = 3,foldChange=geneList, circular = TRUE, colorEdge = TRUE)
#Enrichment Map,这个函数最近更新过，版本不同代码会不同

Biobase::package.version("enrichplot")

if(T){
  emapplot(pairwise_termsim(ego)) #新版本
}else{
  emapplot(ego)#旧版本
}
#(4)展示通路关系 https://zhuanlan.zhihu.com/p/99789859
#goplot(ego)
goplot(ego_BP)

#(5)Heatmap-like functional classification
heatplot(ego,foldChange = geneList,showCategory = 8)

# 2.KEGG pathway analysis----
#上调、下调、差异、所有基因
#（1）输入数据
gene_up = deg[deg$change == 'up','ENTREZID'] 
gene_down = deg[deg$change == 'down','ENTREZID'] 
gene_diff = c(gene_up,gene_down)

#（2）对上调/下调/所有差异基因进行富集分析

if(!file.exists(paste0(gse_number,"_KEGG.Rdata"))){
  kk.up <- enrichKEGG(gene         = gene_up,
                      organism     = 'hsa')
  kk.down <- enrichKEGG(gene         =  gene_down,
                        organism     = 'hsa')
  kk.diff <- enrichKEGG(gene         = gene_diff,
                        organism     = 'hsa')
  save(kk.diff,kk.down,kk.up,file = paste0(gse_number,"_KEGG.Rdata"))
}
load(paste0(gse_number,"_KEGG.Rdata"))

#(3)看看富集到了吗？https://mp.weixin.qq.com/s/NglawJgVgrMJ0QfD-YRBQg
table(kk.diff@result$p.adjust<0.05)
table(kk.up@result$p.adjust<0.05)
table(kk.down@result$p.adjust<0.05)

#(4)双向图
# 富集分析所有图表默认都是用p.adjust,富集不到可以退而求其次用p值，在文中说明即可
down_kegg <- kk.down@result %>%
  filter(pvalue<0.05) %>% #筛选行
  mutate(group=-1) #新增列

up_kegg <- kk.up@result %>%
  filter(pvalue<0.05) %>%
  mutate(group=1)

source("kegg_plot_function.R")
g_kegg <- kegg_plot(up_kegg,down_kegg)
g_kegg
#g_kegg +scale_y_continuous(labels = c(2,0,2,4,6))

# 3.GSEA作kegg和GO富集分析----
#https://www.yuque.com/xiaojiewanglezenmofenshen/dbwkg1/ytawgg

#(1)查看示例数据
data(geneList, package="DOSE")
#(2)将我们的数据转换成示例数据的格式
geneList=deg$logFC
names(geneList)=deg$ENTREZID
geneList=sort(geneList,decreasing = T)
#(3)GSEA富集
kk_gse <- gseKEGG(geneList     = geneList,
                  organism     = 'hsa',
                  verbose      = FALSE)
down_kegg<-kk_gse[kk_gse$pvalue<0.05 & kk_gse$enrichmentScore < 0,];down_kegg$group=-1
up_kegg<-kk_gse[kk_gse$pvalue<0.05 & kk_gse$enrichmentScore > 0,];up_kegg$group=1
#(4)可视化
g2 = kegg_plot(up_kegg,down_kegg)
g2
# 4.能看懂的资料越来越多----
# GSEA学习更多：https://www.yuque.com/docs/share/a67a180f-dd2b-4f6f-96c2-68a4b86fe862?#
# 富集分析学习更多：http://yulab-smu.top/clusterProfiler-book/index.html
# 弦图：https://www.yuque.com/xiaojiewanglezenmofenshen/dbwkg1/dgs65p
# GOplot：https://mp.weixin.qq.com/s/LonwdDhDn8iFUfxqSJ2Wew
# 网上的资料和宝藏无穷无尽，学好R语言慢慢发掘~

# 弦图
ego1 <- data.frame(ego_BP) 
colnames(ego1)
ego1 <- ego1[1:10,c(1,2,8,6)] 
ego1$geneID <- str_replace_all(ego1$geneID,"/",",") 
names(ego1)=c("ID","Term","Genes","adj_pval")
ego1$Category = "BP"
head(ego1)
genes = data.frame(ID=deg$symbol,
                   logFC=deg$logFC)
head(genes)

# 开始画弦图
library(GOplot)
circ <- circle_dat(ego1,genes)
chord <- chord_dat(data=circ, genes=genes,process = ego1$Term) # 

f2 <- GOChord(chord, space = 0.01, gene.order = 'logFC', gene.space = 0.15, gene.size = 3)
ggsave(f2,filename = "./xiantu.pdf",width = 17,height = 18)

### ---------------
###
### Create: Jianming Zeng
### Date: 2018-07-09 20:11:07
### Email: jmzeng1314@163.com
### Blog: http://www.bio-info-trainee.com/
### Forum:  http://www.biotrainee.com/thread-1376-1-1.html
### CAFS/SUSTC/Eli Lilly/University of Macau
### Update Log: 2018-07-09  First version
###

### Update: Xiaojie Liang
### Date: 2021-07-12 23:38:07
### Email: liangxiaojiecom@163.com
### ---------------
library(ggthemes)
library(ggplot2)
kegg_plot <- function(up.data,down.data){
  dat=rbind(up.data,down.data)
  colnames(dat)
  dat$pvalue = -log10(dat$pvalue)
  dat$pvalue=dat$pvalue*dat$group 
  
  dat=dat[order(dat$pvalue,decreasing = F),]
  
  gk_plot <- ggplot(dat,aes(reorder(Description, pvalue), y=pvalue)) +
    geom_bar(aes(fill=factor((pvalue>0)+1)),stat="identity", width=0.7, position=position_dodge(0.7)) +
    coord_flip() +
    scale_fill_manual(values=c("#0072B2", "#B20072"), guide="none") +
    labs(x="", y="" ) +
    theme_pander()  +
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          #axis.ticks.x = element_blank(),
          axis.line.x = element_line(size = 0.3, colour = "black"),#x轴连线
          axis.ticks.length.x = unit(-0.20, "cm"),#修改x轴刻度的高度，负号表示向上
          axis.text.x = element_text(margin = margin(t = 0.3, unit = "cm")),##线与数字不要重叠
          axis.ticks.x = element_line(colour = "black",size = 0.3) ,#修改x轴刻度的线                         
          axis.ticks.y = element_blank(),
          axis.text.y  = element_text(hjust=0),
          panel.background = element_rect(fill=NULL, colour = 'white')
    )
}

rm(list = ls())
load("step4output.Rdata")
gene_up= deg[deg$change == 'up','symbol'] 
gene_down=deg[deg$change == 'down','symbol']
gene_diff = c(gene_up,gene_down)

# 1.制作string的输入数据
write.table(gene_diff,
            file="diffgene.txt",
            row.names = F,
            col.names = F,
            quote = F)
# 从string网页获得string_interactions.tsv
# 2.准备cytoscape的输入文件
p = deg[deg$change != "stable",
        c("symbol","logFC")]
head(p)
write.table(p,
            file = "deg.txt",
            sep = "\t",
            quote = F,
            row.names = F)
# string_interactions.tsv是网络文件
# deg.txt是属性表格