这里是佳奥！表观调控的分析进入尾声，我们来看看ChIP-Seq分析也会使用的可视化操作吧。

1 单个.bed文件可视化

回到第十步的环境。

##根据gr生成.bed文件
lapply(1:7,function(i){
  gr=ol$peaklist[[i]]
  dat=as.data.frame(gr)[,1:3]
  dat[,1]=gsub('chr','',dat[,1])##去除chr开头
  write.table(dat,sep = '\t',##'\t'，不是空格隔开
              col.names = F,file = paste0('G',i,'_in.bed')##生产G1-G7in
               ,quote = F,row.names = F)
})

ol <- findOverlapsOfPeaks(tmp1$peaklist$fs..1..,
                          tmp2$peaklist$fs..2..,
                          tmp3$peaklist$fs..3..)
png('3_factors_overlapVenn_not_domain.png')
makeVennDiagram(ol,
                NameOfPeaks=str_split(bedFiles,'_',simplify = T)[,1],
                TxDb=txdb)
dev.off()

lapply(1:7,function(i){
  gr=ol$peaklist[[i]]
  dat=as.data.frame(gr)[,1:3]
  dat[,1]=gsub('chr','',dat[,1])##去除chr开头
  write.table(dat,sep = '\t',##'\t'，不是空格隔开
              col.names = F,file = paste0('G',i,'_out.bed')##生产G1-G7out
              ,quote = F,row.names = F)
})

获得了17个.bed文件，用deeptools画图。

进入bw目录：

computeMatrix reference-point -- referencePoint TSS -p 4 \
-b 10000 -a 10000 \ ##上下10kb
-R G1_in.bed \ ##刚刚生成的14个.bed，要去除chr开头
-S Spps_TW.bw \
--skipZeros -0 G1_in_matrix1_test_TSS.gz \
--outFileSortedRegions G1_in_regions1_test_genes.bed

生成G1_in_matrix1_test_TSS.gz，进行可视化：

plotHeatmap -m matrix1_test_TSS.gz -out G1_in_Heatmap.png

plotHeatmap -m matrix1_test_TSS.gz -out test_Heatmap.pdf --plotFileFormat pdf --dpi 720
plotProfile -m matrix1_test_TSS.gz -out test_Profile.png
plotProfile -m matrix1_test_TSS.gz -out test_Profile.pdf --plotFileFormat pdf --perGroup --d

更换对象就换比如G4_in.bed

另外，需要把.bam文件的内容一一对应上：用bamCompare

##读取.bam文件，匹配输出.bw，会比较耗时
bamCompare -b1 ../Spps_WT.merge.bam -b2 ../Input_WT.merge.bam \
--operation log2 -of bigwig -o Spps_WT.log2.bw -p 4

##生成新热图
computeMatrix reference-point --referencePoint center -p 4 \
-b 5000 -a 5000 \
-R G4_in.bed \
-S Spps_WT.log2.bw \
--skipZeros -o G4_in_matrix1_log2_TSS.gz \
--outFileSortedRegions G4_in_regions1_log2_genes.bed

plotHeatmap -m matrix1_log2_TSS.gz -out G4_in_log2_Heatmap.png

2 批量.bed文件可视化

ls ../*_WT.merge.bam | while read id;
do
bamCompare -b1 $id -b2 ../Input_WT.merge.bam \
--operation log2 -of bigwig -o $(basename $id "_WT.merge.bam" ).log2.bw -p 4
done

##获得了全部的log2.bw文件
computeMatrix reference-point --referencePoint center -p 4 \
-b 5000 -a 5000 \
-R G{1..7}_in.bed \
-S *.log2.bw \
--skipZeros -o G4_in_matrix1_log2_TSS.gz \
--outFileSortedRegions all_in_regions1_log2_genes.bed

plotHeatmap -m all_matrix1_log2_TSS.gz -out all_in_log2_Heatmap.png

第十一步，bw-heatmap。(上述代码整理)

computeMatrix reference-point  --referencePoint TSS  -p 4  \
-b 5000 -a 5000    \
-R G4_in.bed  \
-S Spps_WT.bw  \
--skipZeros  -o G4_in_matrix1_test_TSS.gz  \
--outFileSortedRegions G4_in_regions1_test_genes.bed

plotHeatmap -m G4_in_matrix1_test_TSS.gz  -out G4_in_Heatmap.png

 

bamCompare -b1 ../Spps_WT.merge.bam -b2 ../Input_WT.merge.bam  \
--operation log2 -of bigwig -o  Spps_WT.log2.bw -p 4


computeMatrix reference-point  --referencePoint center  -p 4  \
-b 5000 -a 5000    \
-R G4_in.bed  \
-S Spps_WT.log2.bw   \
--skipZeros  -o G4_in_matrix1_log2_TSS.gz  \
--outFileSortedRegions G4_in_regions1_log2_genes.bed
plotHeatmap -m G4_in_matrix1_log2_TSS.gz  -out G4_in_log2_Heatmap.png

##批量代码
 ls ../*_WT.merge.bam |while read id;
 do 
 echo bamCompare -b1 $id -b2 ../Input_WT.merge.bam  \
 --operation log2 -of bigwig -o  $(basename $id "_WT.merge.bam" ).log2.bw -p 4
done 

echo computeMatrix reference-point  --referencePoint center  -p 4  \
-b 5000 -a 5000    \
-R G{1..7}_in.bed  \
-S *_WT.log2.bw   \
--skipZeros  -o G4_in_matrix1_log2_TSS.gz  \
--outFileSortedRegions all_in_regions1_log2_genes.bed
plotHeatmap -m all_in_matrix1_log2_TSS.gz  -out all_in_log2_Heatmap.png

QQ截图20220825203126.png

G4_in_Heatmap和G4_in_log2_Heatmap

下一篇我们最后处理一下RNA-Seq处理的数据。

我们下一篇再见！