评估富集

在 R 中使用 Bioconductor 进行 ChIP-seq 分析

Peter Humburg

Statistician, Macquarie University

延伸reads

加载数据：

reads <- readGAlignments(bam)
reads_gr <- granges(reads[[1]])

获取平均片段长度：

frag_length <- fragmentlength(qc_report)["GSM1598218"]

延伸reads并计算覆盖度：

reads_ext <- resize(reads_gr, width=frag_length)
cover_ext <- coverage(reads_ext)

峰值覆盖度

在全基因组创建200 bp分箱。

bins <- tileGenome(seqinfo(reads), tilewidth=200, 
                   cut.last.tile.in.chrom=TRUE)

查找与峰值重叠的所有分箱。

peak_bins_overlap <- findOverlaps(bins, peaks)
peak_bins <- bins[from(peak_bins_overlap), ]

统计每个峰值分箱的重叠reads数。

peak_bins$score <- countOverlaps(peak_bins, reads)

分箱覆盖函数

count_bins <- function(reads, target, bins){
  # Find all bins overlapping peaks
  overlap <- from(findOverlaps(bins, target))
  target_bins <- bins[overlap, ]

  # Count the number of reads overlapping each peak bin
  target_bins$score <- countOverlaps(target_bins, reads)
  target_bins
}

黑名单区域的覆盖度

peak_bins <- count_bins(reads_ext, peaks, bins)
bl_bins <- count_bins(reads_ext, blacklist.hg19, bins)

背景覆盖度

移除已计入的所有分箱。

bkg_bins <- subset(bins, !bins %in% peak_bins & !bins %in% bl_bins)

统计每个剩余分箱的重叠reads数。

bkg_bins$score <- countOverlaps(bkg_bins, reads_ext)

Vamos praticar!

在 R 中使用 Bioconductor 进行 ChIP-seq 分析