下面是（ 数据挖掘 ）直播配套笔记
因为是癌症方面，自己不研究这一方面，所以不常用，但是GEO的转录组数据，是根据这个文件改写的
0.安装包
代码语言：javascript复制options("repos" = c(CRAN="http://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
if(!require("BiocManager")) install.packages("BiocManager",update = F,ask = F)
options(BioC_mirror="http://mirrors.tuna.tsinghua.edu.cn/bioconductor/")
cran_packages <- c('tidyr',
'tibble',
'dplyr',
'stringr',
'ggplot2',
'ggpubr',
'factoextra',
'FactoMineR',
'pheatmap',
"survival",
"survminer",
"patchwork",
"ggstatsplot",
"ggplotify",
"cowplot",
"glmnet",
"ROCR",
"caret",
"randomForest",
"survminer",
"Hmisc",
"e1071",
"deconstructSigs",
"AnnoProbe",
"timeROC",
"circlize",
"VennDiagram",
"tinyarray"
)
Biocductor_packages <- c("limma",
"clusterProfiler",
"org.Hs.eg.db",
"SummarizedExperiment",
"DESeq2",
"edgeR",
"ggpubr",
"rtracklayer",
"genefilter",
"maftools",
"ComplexHeatmap",
"GDCRNATools"
)
for (pkg in cran_packages){
if (! require(pkg,character.only=T) ) {
install.packages(pkg,ask = F,update = F)
require(pkg,character.only=T)
}
}
# use BiocManager to install
for (pkg in Biocductor_packages){
if (! require(pkg,character.only=T) ) {
BiocManager::install(pkg,ask = F,update = F)
require(pkg,character.only=T)
}
}
#前面的任何信息都先不要管。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。
#报错就回去重新安装。如果你没有安装xx包，却提示你xx包不存在，这也正常，是因为依赖关系，缺啥补啥。
1.数据下载
最方便的是xena。可以网页下载，也可以用代码下载。
代码语言：javascript复制proj = "TCGA-CHOL"
if(F){
download.file(url = paste0("https://gdc.xenahubs.net/download/",proj, ".htseq_counts.tsv.gz"),destfile = paste0(proj,".htseq_counts.tsv.gz"))
download.file(url = paste0("https://gdc.xenahubs.net/download/",proj, ".GDC_phenotype.tsv.gz"),destfile = paste0(proj,".GDC_phenotype.tsv.gz"))
download.file(url = paste0("https://gdc.xenahubs.net/download/",proj, ".survival.tsv"),destfile = paste0(proj,".survival.tsv"))
}
2.生存信息与临床信息
这里仅仅是查看一下，到生存信息部分再整理。
代码语言：javascript复制clinical = read.delim(paste0(proj,".GDC_phenotype.tsv.gz"),fill = T,header = T,sep = "t")
surv = read.delim(paste0(proj,".survival.tsv"),header = T)
clinical[1:4,1:4]
head(surv)
3.表达矩阵行名ID转换
代码语言：javascript复制dat = read.table(paste0(proj,".htseq_counts.tsv.gz"),check.names = F,row.names = 1,header = T)
#逆转log
dat = as.matrix(2^dat - 1)
dat[1:4,1:4]
# 深坑一个
dat[97,9]
as.character(dat[97,9]) #眼见不一定为实吧。
# 用apply转换为整数矩阵
exp = apply(dat, 2, as.integer)
exp[1:4,1:4] #行名消失
rownames(exp) = rownames(dat)
exp[1:4,1:4]
gdc下载的数据从此处开始衔接
代码语言：javascript复制library(stringr)
# 行名ID转换：方法1(推荐)
head(rownames(exp))
library(AnnoProbe)
annoGene(rownames(exp),ID_type = "ENSEMBL") # 转换不了
rownames(exp) = str_split(rownames(exp),"\.",simplify = T)[,1];head(rownames(exp))
re = annoGene(rownames(exp),ID_type = "ENSEMBL");head(re)
library(tinyarray)
exp = trans_array(exp,ids = re,from = "ENSEMBL",to = "SYMBOL")
exp[1:4,1:4]
# 方法2：zz_gtf_ID转换.Rmd
4.基因过滤
需要过滤一下那些在很多样本里表达量都为0或者表达量很低的基因。过滤标准不唯一。
过滤之前基因数量：
代码语言：javascript复制nrow(exp)
常用过滤标准1：
仅去除在所有样本里表达量都为零的基因
代码语言：javascript复制exp1 = exp[rowSums(exp)>0,]
nrow(exp1)
常用过滤标准2(推荐)：
仅保留在一半以上样本里表达的基因
代码语言：javascript复制exp = exp[apply(exp, 1, function(x) sum(x > 0) > 0.5*ncol(exp)), ]
nrow(exp)
5.分组信息获取
根据样本ID的第14-15位，给样本分组（tumor和normal）
代码语言：javascript复制table(str_sub(colnames(exp),14,15))
Group = ifelse(as.numeric(str_sub(colnames(exp),14,15)) < 10,'tumor','normal')
Group = factor(Group,levels = c("normal","tumor"))
table(Group)
6.保存数据
TCGA以外的数据没有clinical,surv，从下面代码里去掉。
代码语言：javascript复制save(exp,Group,proj,clinical,surv,file = paste0(proj,".Rdata"))
其他数据下载方法
gdc-client
是从官方网站下载，代码在gdc文件夹，难度较大，结果与xena整理的一致，作为补充学习浏览一下即可。
GDCRNAtools
http://bioconductor.org/packages/devel/bioc/vignettes/GDCRNATools/inst/doc/GDCRNATools.html
1.三大R包差异分析
代码语言：javascript复制rm(list = ls())
load("TCGA-CHOL.Rdata")
table(Group)
#deseq2----
library(DESeq2)
colData <- data.frame(row.names =colnames(exp),
condition=Group)
if(!file.exists(paste0(proj,"_dd.Rdata"))){
dds <- DESeqDataSetFromMatrix(
countData = exp,
colData = colData,
design = ~ condition)
dds <- DESeq(dds)
save(dds,file = paste0(proj,"_dd.Rdata"))
}
load(file = paste0(proj,"_dd.Rdata"))
class(dds)
res <- results(dds, contrast = c("condition",rev(levels(Group))))
#constrast
c("condition",rev(levels(Group)))
class(res)
DEG1 <- as.data.frame(res)
DEG1 <- DEG1[order(DEG1$pvalue),]
DEG1 = na.omit(DEG1)
head(DEG1)
#添加change列标记基因上调下调
logFC_t = 2
pvalue_t = 0.05
k1 = (DEG1$pvalue < pvalue_t)&(DEG1$log2FoldChange < -logFC_t);table(k1)
k2 = (DEG1$pvalue < pvalue_t)&(DEG1$log2FoldChange > logFC_t);table(k2)
DEG1$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))
table(DEG1$change)
head(DEG1)
#edgeR----
library(edgeR)
dge <- DGEList(counts=exp,group=Group)
dge$samples$lib.size <- colSums(dge$counts)
dge <- calcNormFactors(dge)
design <- model.matrix(~Group)
dge <- estimateGLMCommonDisp(dge, design)
dge <- estimateGLMTrendedDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)
fit <- glmFit(dge, design)
fit <- glmLRT(fit)
DEG2=topTags(fit, n=Inf)
class(DEG2)
DEG2=as.data.frame(DEG2)
head(DEG2)
k1 = (DEG2$PValue < pvalue_t)&(DEG2$logFC < -logFC_t);table(k1)
k2 = (DEG2$PValue < pvalue_t)&(DEG2$logFC > logFC_t);table(k2)
DEG2$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))
head(DEG2)
table(DEG2$change)
###limma----
library(limma)
dge <- edgeR::DGEList(counts=exp)
dge <- edgeR::calcNormFactors(dge)
design <- model.matrix(~Group)
v <- voom(dge,design, normalize="quantile")
design <- model.matrix(~Group)
fit <- lmFit(v, design)
fit= eBayes(fit)
DEG3 = topTable(fit, coef=2, n=Inf)
DEG3 = na.omit(DEG3)
k1 = (DEG3$P.Value < pvalue_t)&(DEG3$logFC < -logFC_t);table(k1)
k2 = (DEG3$P.Value < pvalue_t)&(DEG3$logFC > logFC_t);table(k2)
DEG3$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))
table(DEG3$change)
head(DEG3)
tj = data.frame(deseq2 = as.integer(table(DEG1$change)),
edgeR = as.integer(table(DEG2$change)),
limma_voom = as.integer(table(DEG3$change)),
row.names = c("down","not","up")
);tj
save(DEG1,DEG2,DEG3,Group,tj,file = paste0(proj,"_DEG.Rdata"))
2.可视化
代码语言：javascript复制library(ggplot2)
library(tinyarray)
exp[1:4,1:4]
# cpm 去除文库大小的影响
dat = log2(cpm(exp) 1)
pca.plot = draw_pca(dat,Group);pca.plot
save(pca.plot,file = paste0(proj,"_pcaplot.Rdata"))
cg1 = rownames(DEG1)[DEG1$change !="NOT"]
cg2 = rownames(DEG2)[DEG2$change !="NOT"]
cg3 = rownames(DEG3)[DEG3$change !="NOT"]
h1 = draw_heatmap(dat[cg1,],Group,n_cutoff = 2)
h2 = draw_heatmap(dat[cg2,],Group,n_cutoff = 2)
h3 = draw_heatmap(dat[cg3,],Group,n_cutoff = 2)
v1 = draw_volcano(DEG1,pkg = 1,logFC_cutoff = logFC_t)
v2 = draw_volcano(DEG2,pkg = 2,logFC_cutoff = logFC_t)
v3 = draw_volcano(DEG3,pkg = 3,logFC_cutoff = logFC_t)
library(patchwork)
(h1 h2 h3) / (v1 v2 v3) plot_layout(guides = 'collect') &theme(legend.position = "none")
ggsave(paste0(proj,"_heat_vo.png"),width = 15,height = 10)
3.三大R包差异基因对比
代码语言：javascript复制UP=function(df){
rownames(df)[df$change=="UP"]
}
DOWN=function(df){
rownames(df)[df$change=="DOWN"]
}
up = intersect(intersect(UP(DEG1),UP(DEG2)),UP(DEG3))
down = intersect(intersect(DOWN(DEG1),DOWN(DEG2)),DOWN(DEG3))
dat = log2(cpm(exp) 1)
hp = draw_heatmap(dat[c(up,down),],Group,n_cutoff = 2)
#上调、下调基因分别画维恩图
up_genes = list(Deseq2 = UP(DEG1),
edgeR = UP(DEG2),
limma = UP(DEG3))
down_genes = list(Deseq2 = DOWN(DEG1),
edgeR = DOWN(DEG2),
limma = DOWN(DEG3))
up.plot <- draw_venn(up_genes,"UPgene")
down.plot <- draw_venn(down_genes,"DOWNgene")
#维恩图拼图，终于搞定
library(patchwork)
#up.plot down.plot
# 拼图
pca.plot hp up.plot down.plot plot_layout(guides = "collect")
ggsave(paste0(proj,"_heat_ve_pca.png"),width = 15,height = 10)
