FridrichMethod 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
- data-visualization/genome-tracks - Linear genome visualization
- hi-c-analysis/hic-visualization - Hi-C-specific circos
- copy-number/cnv-visualization - CNV visualization
- variant-calling/structural-variant-calling - SV data for circos