样本间相关性评估
上一步得到各个样本的BAM文件之后，就可以在全基因组范围上看看这几个样本之间是否有差异。也就是先将基因组分成N个区间，然后用统计每个区间上比对上的read数。
脚本scripts/07.genome_bin_read_coverage.sh如下
#!/bin/bash
set -e
set -u
set -o pipefail
samples=${1?missing sample file}
chromsize=${2:-index/hg19.chrom.sizes}
size=${3:-3000}
ALIGN_DIR="analysis/2-read-align"
COV_DIR="analysis/3-genome-coverage"
mkdir -p ${COV_DIR}
exec 0< $samples
while read sample
do
bedtools makewindows -g $chromsize -w $size | \
bedtools intersect -b ${ALIGN_DIR}/${sample}.flt.bam -a - -c -bed > ${COV_DIR}/${sample}.ReadsCoverage
done
准备一个输入文件存放待处理样本的前缀，然后运行脚本bash scripts/07.genome_bin_read_coverage.sh samples_rep.txt
HKE293-D210N-Input-Rep1
HKE293-D210N-Input-Rep2
HKE293-D210N-Input-Rep3
HKE293-D210N-V5ChIP-Rep1
HKE293-D210N-V5ChIP-Rep2
HKE293-D210N-V5ChIP-Rep3
最后将得到的文件导入到R语言中进行作图，使用的是基础绘图系统的光滑散点图(smoothScatter)。
files_list <- list.files("r-chip/analysis/3-genome-coverage","ReadsCoverage")
files_path <- file.path("r-chip/analysis/3-genome-coverage",files_list)
input_rep1 <- read.table(files_path[1], sep='\t')
input_rep2 <- read.table(files_path[2], sep='\t')
input_rep3 <- read.table(files_path[3], sep='\t')
chip_rep1 <- read.table(files_path[4], sep='\t')
chip_rep2 <- read.table(files_path[5], sep='\t')
chip_rep3 <- read.table(files_path[6], sep='\t')
pw_plot <- function(x, y,
xlab="x",
ylab="y", ...){
log2x <- log2(x)
log2y <- log2(y)
smoothScatter(log2x,log2y,
cex=1.2,
xlim=c(0,12),ylim=c(0,12),
xlab=xlab,
ylab=ylab)
text(3,10,paste("R = ",round(cor(x,y),2),sep=""))
}
par(mfrow=c(2,3))
pw_plot(chip_rep1[,4], chip_rep2[,4],
xlab = "Rep 1 (Log2 Tag Counts)",
ylab = "Rep 2 (Log2 Tag Counts)")
pw_plot(chip_rep1[,4], chip_rep3[,4],
xlab = "Rep 1 (Log2 Tag Counts)",
ylab = "Rep 2 (Log2 Tag Counts)")
pw_plot(chip_rep2[,4], chip_rep3[,4],
xlab = "Rep 1 (Log2 Tag Counts)",
ylab = "Rep 2 (Log2 Tag Counts)")
pw_plot(input_rep1[,4], input_rep2[,4],
xlab = "Rep 1 (Log2 Tag Counts)",
ylab = "Rep 2 (Log2 Tag Counts)")
pw_plot(input_rep1[,4], input_rep3[,4],
xlab = "Rep 1 (Log2 Tag Counts)",
ylab = "Rep 3 (Log2 Tag Counts)")
pw_plot(input_rep2[,4], input_rep3[,4],
xlab = "Rep 2 (Log2 Tag Counts)",
ylab = "Rep 3 (Log2 Tag Counts)")
correlationship
这种多个BAM文件之间相关性衡量，其实也可以用
deepTools的plotCorrelation画出来，但是我觉得应该没有R语言画 的好看。
BigWig可视化
由于同一个样本间的BAM文件具有很强的相关性，因此可以将这些样本合并起来转换成bigwig格式，这样子在基因组浏览器（例如IGV, UCSC Browser, JBrowse）上方便展示。
如下的代码的目的就是先合并BAM，然后转换成BigWig，拆分成正链和反链进行保存
#!/bin/bash
set -e
set -u
set -o pipefail
samples=${1?please provied sample file}
threads=${2-8}
bs=${3-50}
ALIGN_DIR="analysis/2-read-align"
BW_DIR="analysis/4-normliazed-bw"
mkdir -p $BW_DIR
exec 0< $samples
cd $ALIGN_DIR
while read sample
do
bams=$(ls ${sample}*flt.bam | tr '\n' ' ')
if [ ! -f ../$(basename ${BW_DIR})/${sample}.tmp.bam ]; then
echo "samtools merge -f -@ ${threads} ../$(basename ${BW_DIR})/${sample}.tmp.bam $bams &&
samtools index ../$(basename ${BW_DIR})/${sample}.tmp.bam" | bash
fi
done
cd ../../
exec 0< $samples
cd ${BW_DIR}
while read sample
do
bamCoverage -b ${sample}.tmp.bam -o ${sample}_fwd.bw -of bigwig \
--filterRNAstrand forward --binSize ${bs} --normalizeUsing CPM --effectiveGenomeSize 2864785220 \
--extendReads 150 -p ${threads} 2> ../log/${sample}_fwd.log
bamCoverage -b ${sample}.tmp.bam -o ${sample}_rvs.bw -of bigwig \
--filterRNAstrand reverse --binSize ${bs} --normalizeUsing CPM --effectiveGenomeSize 2864785220 \
--extendReads 150 -p ${threads} 2> ../log/${sample}_rvs.log
rm -f ${sample}.tmp.bam ${sample}.tmp.bam.bai
done
得到的BW文件你可以在IGV上初步看看，比如说检查下文章Figure 1(E)提到的CIRH1A基因
CIRH1A
发表文章时肯定不能用上图，我们可以用R的Gviz进行展示下
library(Gviz)
#下面将scale等track写入tracklist
tracklist<-list()
itrack <- IdeogramTrack(genome = "hg19", chromosome = 'chr16',outline=T)
tracklist[["itrack"]]<-itrack
# 读取BigWig
bw_file_path <- "C:/Users/DELL/Desktop/FigureYa/R-ChIP/"
bw_file_names <- list.files(bw_file_path, "*.bw")
bw_files <- file.path("C:/Users/DELL/Desktop/FigureYa/R-ChIP/",
bw_file_names)
tracklist[['D210-fwd']] <- DataTrack(range = bw_files[3],
genome="hg19",
type="histogram",
name='D210 + ',
ylim=c(0,4),
col.histogram="#2167a4",
fill.histogram="#2167a4")
tracklist[['D210-rvs']] <- DataTrack(range = bw_files[4],
genome="hg19",
type="histogram",
name='D210 - ',
ylim=c(4,0),
col.histogram="#eb1700",
fill.histogram="#eb1700")
tracklist[['WKDD-fwd']] <- DataTrack(range = bw_files[9],
genome="hg19",
type="histogram",
name='WKDD + ',
ylim=c(0,4),
ylab=2,
col.histogram="#2167a4",
fill.histogram="#2167a4")
tracklist[['WKDD-rvs']] <- DataTrack(range = bw_files[10],
genome="hg19",
type="histogram",
name=' WKDD -',
ylim=c(4,0),
col.histogram="#eb1700",
fill.histogram="#eb1700",
showAxis=TRUE)
#写入比例尺
scalebar <- GenomeAxisTrack(scale=0.25,
col="black",
fontcolor="black",
name="Scale",
labelPos="above",showTitle=F)
tracklist[["scalebar"]]<-scalebar
# 画图
plotTracks(trackList = tracklist,
chromosome = "chr16",
from = 69141913, to= 69205033,
background.panel = "#f6f6f6",
background.title = "white",
col.title="black",col.axis="black",
rot.title=0,cex.title=0.5,margin=10,title.width=1.75,
cex.axis=1
)
Gviz
后续用AI修改下坐标轴，就几乎和原图差不多了。此外可能还要调整之前脚本的bin size， 使得整体更加平滑
看图说话： 由于WKKD在D210N的基础上继续突变了几个位点，使得原本只丧失了RNASEH1的催化活性的D210N进一步丧失了结合到RNA/DNA杂合体的能力，在实验中就可以作为 负对照。上图就是其中一个有代表性的区间。
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