/bio-data-visualization-circos-plots

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--- name: bio-data-visualization-circos-plots description: Create circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations. tool_type: mixed primary_tool: Circos measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

---

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
cir