Version Compatibility
Reference examples tested with: GenomicRanges 1.54+, deepTools 3.5+
Before using code patterns, verify installed versions match. If versions differ:
- R:
packageVersion('<pkg>') then ?function_name to verify parameters
- CLI:
<tool> --version then <tool> --help to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
ChIP-seq Visualization
"Create a heatmap of ChIP-seq signal around peaks" → Generate signal heatmaps, profile plots, and genome browser tracks showing enrichment patterns around genomic features.
- CLI:
deeptools computeMatrix reference-point → plotHeatmap
- R:
Gviz, ChIPseeker::plotAvgProf()
deepTools - Compute Matrix
Goal: Build a signal matrix of ChIP-seq coverage around reference points for downstream heatmaps and profiles.
Approach: Use computeMatrix to extract bigWig signal values in windows around genomic features like TSS.
# Compute signal matrix around TSS
computeMatrix reference-point \
--referencePoint TSS \
-b 3000 -a 3000 \ # 3kb upstream and downstream
-R genes.bed \ # Reference regions
-S sample.bw \ # Signal file (bigWig)
-o matrix.gz \
--outFileSortedRegions sorted_genes.bed
deepTools - Scale-Regions
Goal: Visualize ChIP signal across gene bodies scaled to a uniform length.
Approach: Scale all gene regions to equal size and compute signal with flanking windows.
# Signal across gene bodies
computeMatrix scale-regions \
-R genes.bed \
-S sample1.bw sample2.bw \
-b 3000 -a 3000 \ # Flanking regions
-m 5000 \ # Scaled body length
-o matrix_scaled.gz
deepTools - Heatmap
Goal: Generate a heatmap of ChIP-seq signal intensity across genomic regions.
Approach: Render the precomputed signal matrix as a clustered heatmap with optional profile summary.
# Generate heatmap from matrix
plotHeatmap \
-m matrix.gz \
-o heatmap.png \
--colorMap RdBu \
--whatToShow 'heatmap and colorbar' \
--zMin -3 --zMax 3
# With profile on top
plotHeatmap \
-m matrix.gz \
-o heatmap_with_profile.png \
--plotTitle 'H3K4me3 Signal' \
--heatmapHeight 15 \
--refPointLabel TSS
deepTools - Profile Plot
Goal: Display average ChIP-seq signal profiles across genomic regions for sample comparison.
Approach: Plot mean signal from the computed matrix, optionally overlaying multiple samples.
# Average profile plot
plotProfile \
-m matrix.gz \
-o profile.png \
--plotTitle 'Average Signal Profile' \
--perGroup
# Multiple samples comparison
plotProfile \
-m matrix_multi.gz \
-o profile_compare.png \
--colors red blue green \
--plotTitle 'Sample Comparison'
Create BigWig from BAM
Goal: Convert BAM alignments to normalized bigWig signal tracks for visualization.
Approach: Use bamCoverage for single-sample normalization or bamCompare for log2 ratio of ChIP over input.
# Normalized bigWig (CPM)
bamCoverage \
-b sample.bam \
-o sample.bw \
--normalizeUsing CPM \
--binSize 10 \
--numberOfProcessors 8
# With input subtraction
bamCompare \
-b1 chip.bam \
-b2 input.bam \
-o chip_vs_input.bw \
--operation log2ratio \
--binSize 50
ChIPseeker Profile Heatmap (R)
Goal: Visualize peak distribution around TSS using ChIPseeker tag matrices and profile plots.
Approach: Build a tag density matrix from peak locations relative to promoter windows, then plot as heatmap or average profile.
library(ChIPseeker)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
# Load peaks
peaks <- readPeakFile('sample_peaks.narrowPeak')
# Get promoter regions
promoter <- getPromoters(TxDb = txdb, upstream = 3000, downstream = 3000)
# Compute tag matrix
tagMatrix <- getTagMatrix(peaks, windows = promoter)
# Heatmap
tagHeatmap(tagMatrix, xlim = c(-3000, 3000), color = 'red')
# Profile plot
plotAvgProf(tagMatrix, xlim = c(-3000, 3000), xlab = 'Distance from TSS (bp)',
ylab = 'Peak Count Frequency')
# With confidence interval
plotAvgProf2(tagMatrix, xlim = c(-3000, 3000), conf = 0.95)
Gviz - Genome Browser Tracks (R)
Goal: Create publication-quality genome browser views combining signal tracks, gene models, and ideograms.
Approach: Layer Gviz track objects (ideogram, axis, data, gene) and render a specific genomic region.
library(Gviz)
library(GenomicRanges)
# Define region
chr <- 'chr1'
start <- 1000000
end <- 1100000
# Ideogram track
itrack <- IdeogramTrack(genome = 'hg38', chromosome = chr)
# Genome axis
gtrack <- GenomeAxisTrack()
# Data track from bigWig
dtrack <- DataTrack(
range = 'sample.bw',
genome = 'hg38',
type = 'histogram',
name = 'ChIP Signal',
col.histogram = 'darkblue',
fill.histogram = 'darkblue'
)
# Gene track
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
grtrack <- GeneRegionTrack(txdb, genome = 'hg38', chromosome = chr, name = 'Genes')
# Plot
plotTracks(list(itrack, gtrack, dtrack, grtrack),
from = start, to = end, chromosome = chr)
Multiple Samples in Gviz
Goal: Compare ChIP-seq signal from multiple samples in a single browser view.
Approach: Create separate DataTrack objects per sample and stack them in the plotTracks call.
# Create data tracks for each sample
dtrack1 <- DataTrack(range = 'control.bw', genome = 'hg38', name = 'Control',
type = 'histogram', col.histogram = 'blue', fill.histogram = 'blue')
dtrack2 <- DataTrack(range = 'treatment.bw', genome = 'hg38', name = 'Treatment',
type = 'histogram', col.histogram = 'red', fill.histogram = 'red')
plotTracks(list(itrack, gtrack, dtrack1, dtrack2, grtrack),
from = start, to = end, chromosome = chr)
EnrichedHeatmap (R)
Goal: Generate customizable heatmaps of ChIP signal around genomic features using ComplexHeatmap framework.
Approach: Normalize bigWig signal to a matrix around target sites and render with EnrichedHeatmap.
library(EnrichedHeatmap)
library(rtracklayer)
# Load signal and regions
signal <- import('sample.bw')
tss <- promoters(txdb, upstream = 0, downstream = 1)
# Normalize to matrix
mat <- normalizeToMatrix(signal, tss, extend = 3000, mean_mode = 'w0', w = 50)
# Heatmap
EnrichedHeatmap(mat, name = 'Signal', col = c('white', 'red'))
IGV Batch Screenshot
Goal: Automate genome browser screenshots at specific loci without manual interaction.
Approach: Write an IGV batch script that loads tracks, navigates to regions, and saves snapshots.
# Create IGV batch script
cat > igv_batch.txt << 'EOF'
new
genome hg38
load sample.bw
load peaks.bed
goto chr1:1000000-1100000
snapshot region1.png
goto chr2:50000000-51000000
snapshot region2.png
exit
EOF
# Run IGV in batch mode
igv.sh -b igv_batch.txt
Key Tools Comparison
| Tool | Type | Best For |
|---|
| deepTools | CLI | Large-scale heatmaps, profiles |
| ChIPseeker | R | Peak-centric visualization |
| Gviz | R | Publication-quality browser |
| EnrichedHeatmap | R | Customizable heatmaps |
| IGV | GUI | Interactive exploration |
deepTools Key Commands
| Command | Purpose |
|---|
| bamCoverage | BAM to bigWig |
| bamCompare | Compare two BAMs |
| computeMatrix | Signal matrix |
| plotHeatmap | Heatmap visualization |
| plotProfile | Profile plot |
| multiBigwigSummary | Compare multiple bigWigs |
| plotCorrelation | Sample correlation |
Related Skills
- peak-calling - Generate peaks for visualization
- peak-annotation - Annotation pie charts
- alignment-files - Prepare BAM files