Circos Plots
Circular genome visualizations for displaying multiple data tracks around chromosome ideograms.
Tool Options
Tool Language Best For
Circos Perl/CLI Publication-quality, complex layouts
pyCircos Python Programmatic generation, integration
circlize R Quick plots, Bioconductor integration
Circos (Original)
Installation
conda install -c bioconda circos
Or download from http://circos.ca
Basic Configuration
Circos requires configuration files defining the plot structure.
circos.conf (main config)
Chromosome definitions
karyotype = data/karyotype.human.hg38.txt
<ideogram> <spacing> default = 0.005r </spacing> radius = 0.90r thickness = 20p fill = yes </ideogram>
<image> dir = output file = circos.png png = yes svg = yes radius = 1500p </image>
<<include etc/colors_fonts_patterns.conf>> <<include etc/housekeeping.conf>>
Data Tracks
Scatter Plot Track
<plots> <plot> type = scatter file = data/scatter.txt r0 = 0.75r r1 = 0.85r min = 0 max = 1 glyph = circle glyph_size = 8p color = red </plot> </plots>
Histogram Track
<plot> type = histogram file = data/histogram.txt r0 = 0.60r r1 = 0.74r min = 0 max = 100 fill_color = blue </plot>
Heatmap Track
<plot> type = heatmap file = data/heatmap.txt r0 = 0.50r r1 = 0.59r color = spectral-9-div </plot>
Link/Arc Data (Interactions)
<links> <link> file = data/links.txt radius = 0.45r bezier_radius = 0.1r color = grey_a5 thickness = 2p
<rules> <rule> condition = var(intrachr) color = red </rule> </rules> </link> </links>
Data File Formats
Scatter/histogram: chr start end value
hs1 1000000 1500000 0.75 hs1 2000000 2500000 0.45
Links: chr1 start1 end1 chr2 start2 end2
hs1 1000000 1500000 hs5 5000000 5500000
Run Circos
circos -conf circos.conf
pyCircos (Python)
Installation
pip install pyCircos
Basic Genome Plot
from pycircos import Gcircle import matplotlib.pyplot as plt
Initialize with genome size
circle = Gcircle()
Add chromosome data (name, length)
chromosomes = [ ('chr1', 248956422), ('chr2', 242193529), ('chr3', 198295559), ('chr4', 190214555), ('chr5', 181538259), ('chr6', 170805979), ('chr7', 159345973), ('chr8', 145138636), ('chr9', 138394717), ('chr10', 133797422), ('chr11', 135086622), ('chr12', 133275309) ]
for name, length in chromosomes: circle.add_garc(Garc(arc_id=name, size=length, interspace=2, raxis_range=(900, 950), labelposition=80, label_visible=True))
circle.set_garcs()
Save
fig = circle.figure fig.savefig('genome_circle.png', dpi=300)
Add Data Tracks
from pycircos import Gcircle, Garc import numpy as np
circle = Gcircle()
Add chromosomes
for name, length in chromosomes: arc = Garc(arc_id=name, size=length, interspace=3, raxis_range=(800, 850), labelposition=60) circle.add_garc(arc)
circle.set_garcs()
Add scatter track
for name, length in chromosomes: positions = np.random.randint(0, length, 50) values = np.random.random(50) circle.scatterplot(name, data=values, positions=positions, raxis_range=(700, 780), facecolor='red', markersize=5)
Add bar track
for name, length in chromosomes: positions = np.linspace(0, length, 100) values = np.random.random(100) * 100 circle.barplot(name, data=values, positions=positions, raxis_range=(600, 680), facecolor='blue')
Add links
circle.chord_plot(('chr1', 10000000, 20000000), ('chr5', 50000000, 60000000), raxis_range=(0, 550), facecolor='purple', alpha=0.5)
fig = circle.figure fig.savefig('circos_with_data.png', dpi=300)
circlize (R)
Installation
install.packages('circlize')
Basic Plot
library(circlize)
Initialize with genome
circos.initializeWithIdeogram(species = 'hg38')
Add track with data
bed <- data.frame( chr = paste0('chr', sample(1:22, 100, replace=TRUE)), start = sample(1:1e8, 100), end = sample(1:1e8, 100), value = runif(100) ) bed$end <- bed$start + 1e6
circos.genomicTrack(bed, panel.fun = function(region, value, ...) { circos.genomicPoints(region, value, pch=16, cex=0.5, col='red') })
Add links
link_data <- data.frame( chr1 = c('chr1', 'chr3'), start1 = c(1e7, 5e7), end1 = c(2e7, 6e7), chr2 = c('chr5', 'chr10'), start2 = c(3e7, 8e7), end2 = c(4e7, 9e7) ) for (i in 1:nrow(link_data)) { circos.link(link_data$chr1[i], c(link_data$start1[i], link_data$end1[i]), link_data$chr2[i], c(link_data$start2[i], link_data$end2[i]), col = 'grey') }
circos.clear()
Genomic Density Plot
library(circlize)
circos.initializeWithIdeogram(species = 'hg38', plotType = c('axis', 'labels'))
Gene density track
circos.genomicDensity(gene_bed, col = 'blue', track.height = 0.1)
Variant density track
circos.genomicDensity(variant_bed, col = 'red', track.height = 0.1)
Heatmap track
circos.genomicHeatmap(expression_bed, col = colorRamp2(c(-2, 0, 2), c('blue', 'white', 'red')))
circos.clear()
Common Use Cases
CNV Visualization
pyCircos CNV plot
cnv_data = [ ('chr1', 10000000, 20000000, 2.5), # Gain ('chr3', 50000000, 80000000, 0.5), # Loss ('chr7', 100000000, 120000000, 3.0), # Amplification ]
for chrom, start, end, log2 in cnv_data: color = 'red' if log2 > 1.5 else 'blue' if log2 < 0.7 else 'grey' circle.barplot(chrom, data=[log2], positions=[(start+end)//2], width=end-start, raxis_range=(600, 700), facecolor=color)
Fusion Genes
Visualize gene fusions as arcs
fusions = [ ('chr9', 133600000, 133700000, 'chr22', 23200000, 23300000), # BCR-ABL ('chr2', 42300000, 42500000, 'chr2', 29400000, 29600000), # EML4-ALK ]
for chr1, s1, e1, chr2, s2, e2 in fusions: circle.chord_plot((chr1, s1, e1), (chr2, s2, e2), raxis_range=(0, 500), facecolor='purple', alpha=0.7)
Hi-C Contact Map
library(circlize)
circos.initializeWithIdeogram(chromosome.index = paste0('chr', 1:22))
Add Hi-C links with color by contact frequency
for (i in 1:nrow(hic_contacts)) { col = colorRamp2(c(0, 100), c('grey90', 'red'))(hic_contacts$count[i]) circos.link(hic_contacts$chr1[i], c(hic_contacts$start1[i], hic_contacts$end1[i]), hic_contacts$chr2[i], c(hic_contacts$start2[i], hic_contacts$end2[i]), col = col) }
circos.clear()
Complete Workflow: Variant Summary
from pycircos import Gcircle, Garc import pandas as pd
Load data
variants = pd.read_csv('variants.bed', sep='\t', names=['chr', 'start', 'end', 'type']) cnv = pd.read_csv('cnv.bed', sep='\t', names=['chr', 'start', 'end', 'log2'])
Initialize
circle = Gcircle()
chromosomes = [('chr' + str(i), size) for i, size in enumerate([ 248956422, 242193529, 198295559, 190214555, 181538259, 170805979, 159345973, 145138636, 138394717, 133797422, 135086622, 133275309, 114364328, 107043718, 101991189, 90338345, 83257441, 80373285, 58617616, 64444167, 46709983, 50818468 ], start=1)]
for name, length in chromosomes: arc = Garc(arc_id=name, size=length, interspace=2, raxis_range=(850, 900)) circle.add_garc(arc) circle.set_garcs()
Variant density track
for chrom, length in chromosomes: chrom_vars = variants[variants['chr'] == chrom] if len(chrom_vars) > 0: hist, bins = np.histogram(chrom_vars['start'], bins=50, range=(0, length)) circle.barplot(chrom, data=hist, positions=bins[:-1], raxis_range=(750, 840), facecolor='steelblue')
CNV track
for chrom, length in chromosomes: chrom_cnv = cnv[cnv['chr'] == chrom] for _, row in chrom_cnv.iterrows(): color = 'red' if row['log2'] > 0.3 else 'blue' if row['log2'] < -0.3 else 'grey' circle.fillplot(chrom, data=[abs(row['log2'])], positions=[(row['start'] + row['end']) // 2], raxis_range=(650, 740), facecolor=color)
fig = circle.figure fig.savefig('genome_summary.png', dpi=300, bbox_inches='tight')
Related Skills
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data-visualization/genome-tracks - Linear genome visualization
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hi-c-analysis/hic-visualization - Hi-C-specific circos
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copy-number/cnv-visualization - CNV visualization
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variant-calling/structural-variant-calling - SV data for circos