8. 输出COUNT矩阵
前文得到了每个样本的peak，需要对peak的染色体位置、起始和终止位置、还有读取count进行统计，方便后续差异分析及基因组注释。
library("GenomicFeatures")#处理基因组数据
library(magrittr)#管道操作符
mPeak = GRanges()#存储峰值的基因组区域信息
#创建peak列表，合并每个样本的peaks
projPath = "G:/linux/20231129cuttag/bam/bed1/bedgraph/"
histL <- c("sample1","sample2","sample3")
repL <- c("1","2")
#迭代处理每个样本（hist 和 rep 的组合）
#读取 SEACR 生成的峰值文件，并将它们合并到 mPeak 对象中。
for (hist in histL) {
for (rep in repL) {
peakRes = read.table(paste0(projPath,hist,"-",rep,".seacr_control.peaks.stringent.bed"), header = FALSE, fill = TRUE)
mPeak = GRanges(seqnames = peakRes$V1,IRanges(start = peakRes$V2,end = peakRes$V3),strand = "*")%>% append(mPeak, .)
}
}
#合并 mPeak 中重叠的峰值
masterPeak = reduce(mPeak)
library(DESeq2)
# 初始化 countMat 矩阵
projPath = "G:/linux/20231129cuttag/bam/"
countMat <- matrix(NA, nrow = length(masterPeak), ncol = length(histL) * length(repL))
colnames(countMat) <- paste(rep(histL, each = length(repL)), rep(repL, length(histL)), sep = "_")
# 获取重叠区域的计数
i <- 1
library(chromVAR)#分析染色体可变性
for (hist in histL) {
for (rep in repL) {
bamFile = paste0(projPath,hist,"-",rep,"_sortname.bam")
fragment_counts <- getCounts(bamFile,masterPeak,paired = TRUE,by_rg =FALSE, format = "bam")
countMat[,i] = counts(fragment_counts)[,1]
i=i+1
}
}
# 从 masterPeak 中提取基因组区域信息作为行名
row_names <- paste0(seqnames(masterPeak), "_", start(masterPeak), "_", end(masterPeak))
rownames(countMat) <- row_names
View(countMat)
write.csv(countMat,"countMat.csv")
9. DESeq2差异分析
样本有生物学重复，且读入的count符合DESeq2输入要求，官网也用的这个。
多组DESeq2建议多组一起比较，我是进行的两两单独比较，后续再改。
library(DESeq2)
#测序深度标准化和差异富集peaks检测
countMat <- read.csv("countMat.csv")
View(countMat)
row.names(countMat) <- countMat$X
data <- countMat[,-1]
View(data)
selectR <- which(rowSums(data)>5)
dataS <- data[selectR,]
dim(dataS)
dim(data)
m <- data[,c(3,4,1,2)]
View(m)
# group settings
colnames(m)
group <- factor(c(rep("sample1", 2),rep("sample2",2)))
names(group) <- colnames(m)
group <- data.frame(ID = colnames(m),
group = factor(c(rep("sample1", 2),rep("sample2",2))))
View(group)
rownames(group) <- group$ID
vs_ <- factor(group$group,levels = c("sample1","sample2"))
design = model.matrix(~0 + vs_)
rownames(design) <- group$ID
DESeq2_design <- design
View(design)
#确保表达矩阵的列名与分组矩阵行名相一致
all(rownames(DESeq2_design)==colnames(m))
coldata <- data.frame(row.names = colnames(m),
group)
dds <- DESeqDataSetFromMatrix(countData = m,
colData = as.data.frame(vs_),
design = ~vs_)
#DESeq2数据格式的构建
#dds <- dds[ rowSums(counts(dds)) > 1, ] #过滤一些low count的数据；
DDS <- DESeq(dds)#DESeq进行标准化；
resultsNames(DDS)
res <- results(DDS)
summary(res)#查看经过标准化矩阵的基本情况；
# 获取标准化后的计数
normDDS <- counts(DDS,normalized = TRUE)#normalization with the respect to the sequencing depth
colnames(normDDS) <- paste0(colnames(normDDS),"_norm")
#获得差异结果
res <- as.data.frame(res)
a <- cbind(m,normDDS,res)
View(a)
a$anno <- row.names(a)
#基因注释
library(ChIPseeker)
library("ChIPpeakAnno")
library("GenomicFeatures")
library("org.Hs.eg.db")
?makeTxDbFromGFF
txdb <- makeTxDbFromGFF(file="F:/GENCODE_hisat2/gencode.v43.primary_assembly.annotation.gff3",format="gff3")
peakAnno <- annotatePeak(masterPeak,
tssRegion = c(-3000, 3000),
TxDb = txdb,
annoDb = "org.Hs.eg.db")
write.csv(as.data.frame(peakAnno),"peakAnno.csv")
data_anno <- read.csv("peakAnno.csv")
View(data_anno)
data_anno$anno<- paste0(data_anno$seqnames,"_",data_anno$start,"_",data_anno$end)
names(data_anno)
data <- data_anno[,c("SYMBOL","anno")]
View(data)
nnn <- merge(a,data,by="anno")
View(nnn)
write.csv(nnn,".csv")
得到差异分析结果并进行了基因注释，方便后续和转录组联合分析。
image.png
DESeq2: vignettes/DESeq2.Rmd (rdrr.io)
