geoChina的用法
代码语言：javascript复制#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 = "GSE28345"
library(AnnoProbe)
eSet <- geoChina(gse_number, destdir = '.')
class(eSet)
length(eSet)
eSet = eSet[[1]]批量安装R包
代码语言：javascript复制options("repos"="https://mirrors.ustc.edu.cn/CRAN/")
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")
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warning xx包不存在，用library检查一下。
#library报错，就单独安装。Group(实验分组)和ids(探针注释)
代码语言：javascript复制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$`disease state:ch1`
}else if(F){
# 第二种方法，自己生成
Group = c(rep("RA",times=13),
rep("control",times=9))
Group = rep(c("RA","control"),times = c(13,9))
}else if(T){
# 第三种方法，使用字符串处理的函数获取分组
Group=ifelse(str_detect(pd$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探针注释的获取
代码语言：javascript复制#捷径
library(tinyarray)
find_anno(gpl_number) #打出找注释的代码
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)
代码语言：javascript复制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
代码语言：javascript复制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.delim("GPL570-55999.txt",
check.names = F,
comment.char = "#")
colnames(b)
ids2 = b[,c("ID","Gene Symbol")]
colnames(ids2) = c("probe_id","symbol")
k1 = ids2$symbol!="";table(k1)
k2 = !str_detect(ids2$symbol,"///");table(k2)
ids2 = ids2[ k1 & k2,]
# ids = ids2
}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
代码语言：javascript复制save(exp,Group,ids,gse_number,file = "step2output.Rdata")如何画PCA图
代码语言：javascript复制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-essentials1.PCA 图----
代码语言：javascript复制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----
代码语言：javascript复制cg=names(tail(sort(apply(exp,1,sd)),1000))
n=exp[cg,]#把方差最大的基因挑选出来
# 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，放大了行内部的差别
pheatmap(n,
show_colnames =F,
show_rownames = F,
annotation_col=annotation_col,
scale = "row",
breaks = seq(-3,3,length.out = 100)
) #breaks参数：设置色带分配范围，100种数字就是100种颜色
dev.off()差异分析
代码语言：javascript复制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)#提取结果为deg数据框添加几列
1.加probe_id列，把行名变成一列
代码语言：javascript复制library(dplyr)
deg <- mutate(deg,probe_id=rownames(deg))2.加上探针注释
代码语言：javascript复制ids = ids[!duplicated(ids$symbol),]
#其他去重方式
rm(list = ls())
load("step2output.Rdata")
#1.保留最大值
exp2 = exp[ids$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
#2.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
deg <- inner_join(deg,ids,by="probe_id")
nrow(deg)3.加change列,标记上下调基因
代码语言：javascript复制logFC_t=1
p_t = 0.05
k1 = (deg$P.Value < p_t)&(deg$logFC < -logFC_t)
k2 = (deg$P.Value < p_t)&(deg$logFC > logFC_t)
deg <- mutate(deg,change = ifelse(k1,"down",ifelse(k2,"up","stable")))
table(deg$change)4.加ENTREZID列，用于富集分析（symbol转entrezid，然后inner_join）
代码语言：javascript复制library(clusterProfiler)
library(org.Hs.eg.db)
s2e <- bitr(deg$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_t,gse_number,file = "step4output.Rdata")
