生信地基系列--deeptools

The tools — deepTools 3.5.0 documentation

image.png

Tools for BAM and bigWig file processing

multiBamSummary

multiBigwigSummary

correctGCBias

bamCoverage

bamCompare

bigwigCompare

computeMatrix

alignmentSieve

Tools for QC

plotCorrelation

plotPCA

plotFingerprint

bamPEFragmentSize

computeGCBias

plotCoverage

Heatmaps and summary plots

plotHeatmap

plotProfile

plotEnrichment

Miscellaneous

computeMatrixOperations

estimateReadFiltering

tool	type	input files	main output file(s)	application
multiBamSummary	data integration	2 or more BAM	interval-based table of values	perform cross-sample analyses of read counts –> plotCorrelation, plotPCA
multiBigwigSummary	data integration	2 or more bigWig	interval-based table of values	perform cross-sample analyses of genome-wide scores –> plotCorrelation, plotPCA
plotCorrelation	visualization	bam/multiBigwigSummary output	clustered heatmap	visualize the Pearson/Spearman correlation
plotPCA	visualization	bam/multiBigwigSummary output	2 PCA plots	visualize the principal component analysis
plotFingerprint	QC	2 BAM	1 diagnostic plot	assess enrichment strength of a ChIP sample
computeGCBias	QC	1 BAM	2 diagnostic plots	calculate the exp. and obs. GC distribution of reads
correctGCBias	QC	1 BAM, output from computeGCbias	1 GC-corrected BAM	obtain a BAM file with reads distributed according to the genome’s GC content
bamCoverage	normalization	BAM	bedGraph or bigWig	obtain the normalized read coverage of a single BAM file
bamCompare	normalization	2 BAM	bedGraph or bigWig	normalize 2 files to each other (e.g. log2ratio, difference)
computeMatrix	data integration	1 or more bigWig, 1 or more BED	zipped file for plotHeatmap or plotProfile	compute the values needed for heatmaps and summary plots
estimateReadFiltering	information	1 or more BAM files	table of values	estimate the number of reads filtered from a BAM file or files
alignmentSieve	QC	1 BAM file	1 filtered BAM or BEDPE file	filters a BAM file based on one or more criteria
plotHeatmap	visualization	computeMatrix output	heatmap of read coverages	visualize the read coverages for genomic regions
plotProfile	visualization	computeMatrix output	summary plot (“meta-profile”)	visualize the average read coverages over a group of genomic regions
plotCoverage	visualization	1 or more BAM	2 diagnostic plots	visualize the average read coverages over sampled genomic positions
bamPEFragmentSize	information	1 BAM	text with paired-end fragment length	obtain the average fragment length from paired ends
plotEnrichment	visualization	1 or more BAM and 1 or more BED/GTF	A diagnostic plot	plots the fraction of alignments overlapping the given features
computeMatrixOperations	miscellaneous	1 or more BAM and 1 or more BED/GTF	A diagnostic plot	plots the fraction of alignments overlapping the given features

computeMatrix 计算过程

Bed文件下载
https://mp.weixin.qq.com/s/POPN8kzMQT1jcil8ICvPxg
Table Browser (ucsc.edu)

image.png
image.png

• 用于计算相对于一个点（reference-point）的信号分布，例如，每个基因组区域的开始或结束
• 用于计算一组区域（scale-regions）上的信号，其中所有区域都缩放到相同的大小

image.png
image.png

单个计算bw的computeMatrix reference-point

computeMatrix reference-point  --referencePoint TSS  -p 5  \
-b 10000 -a 10000    \
-R /home/data/vip13t16/project/epi/tss/ucsc.refseq.bed  \
-S /home/data/vip13t16/project/epi/mergeBam/H2Aub1.bw  \
--skipZeros  -o matrix1_test_TSS.gz  \
--outFileSortedRegions regions1_test_genes.bed

从bw开始批量计算computeMatrix reference-point

 ls *bw|while read id;do echo $id;sample=${id%%.*};echo $sample;computeMatrix reference-point  --referencePoint TSS  -p 50 -b 10000 -a 10000 -S $id -R ../BED/hg38.Refseq.bed --skipZeros  -o matrix1_${sample}_TSS.gz --outFileSortedRegions regions1_${sample}_genes.bed ;done

从bam开始批量计算computeMatrix reference-point

rm -rf Outbw
mkdir Outbw
ls *bam |while read id

do

file=$(basename $id )

sample=${file%%.*}

echo $sample

bamCoverage -b $id -o Outbw/$sample.bw -p 50 --binSize 10 --normalizeUsing RPGC   --effectiveGenomeSize 2913022398
###  2913022398是官网写的hg38的大小

computeMatrix reference-point --referencePoint TSS -b 2500 -a 2500 -R hg38.Refseq.bed  -S Outbw/$sample.bw --skipZeros -o Outbw/matrix1_${sample}_TSS.gz --outFileSortedRegions Outbw/regions1_${sample}_genes.bed -p 50

plotHeatmap -m Outbw/matrix1_${sample}_TSS.gz -out Outbw/${sample}.png
 plotHeatmap -m  Outbw/matrix1_${sample}_TSS.gz -out Outbw/${sample}2.png --colorMap RdBu    --whatToShow 'heatmap and colorbar'
done

scale-region

这里的genes19.bed genesX.bed 应该是从基因组之中提取出来的

# run compute matrix to collect the data needed for plotting
computeMatrix scale-regions -S H3K27Me3-input.bigWig \
                                 H3K4Me1-Input.bigWig  \
                                 H3K4Me3-Input.bigWig \
                              -R genes19.bed genesX.bed \
                              --beforeRegionStartLength 3000 \
                              --regionBodyLength 5000 \
                              --afterRegionStartLength 3000
                              --skipZeros -o matrix.mat.gz
plotHeatmap -m matrix.mat.gz \
      -out ExampleHeatmap1.png \

image.png

换一下颜色，从白到蓝

plotHeatmap -m matrix1_chr19_TSS.gz --missingDataColor 1     --colorList 'white,#0066CC'             --heatmapHeight 12      -o scaleRegion-heatmap.pdf

神器之 computeMatrix + 绘图 (qq.com)

ChIP-seq基础入门 - 简书 (jianshu.com)
ChIPseeker: an R package for ChIP peak Annotation, Comparison and Visualization (bioconductor.org)