Bio Data Visualization Circos Plots

Version Compatibility

Reference examples tested with: circlize 0.4.16+ (R), pyCirclize 1.4+ (Python), Circos 0.69-9 (Perl CLI), ComplexHeatmap 2.18+ (uses circlize for color mapping).

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name
• Python: pip show <package> then help(module.function)

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Circular Genome Plots (Circos)

"Make a circos plot" -> Render genome chromosomes around a circle with stacked tracks (histogram, scatter, heatmap) and arcs/chords showing interactions. Krzywinski 2009 Genome Res 19:1639 introduced the genre for genome-scale comparative views. The single decision that matters: does the circular layout convey meaning that Cartesian cannot?

• R: circlize::circos.initializeWithIdeogram + circos.genomicTrack* (Gu 2014)
• Python: pyCirclize.Gcircle
• CLI: Circos (Perl); config-driven; most flexible but steepest learning

The Single Most Important Modern Insight -- Circular Plots Often Hide What Cartesian Reveals

Cleveland-McGill 1984 J Am Stat Assoc 79:531 effectiveness rankings establish that position-on-common-scale (Cartesian) is the most accurate visual channel; circular position requires mental "unwrapping" and impairs precise value comparison. Heer-Bostock 2010 CHI replicated the ranking in modern crowd studies. Use circular only when adjacency on the circle conveys meaning that linear cannot.

Use circular ONLY when:

• Chromosome adjacency matters (whole-genome SVs, Hi-C contacts where genome circularity is the geometry)
• Pairwise interactions between many entities (chord diagrams; chromosome translocations)
• Aesthetic / overview infographic for cover figure

Do NOT use circular for:

• Comparing values across categories (Cartesian bar/dot wins)
• Time series (linear axis wins)
• Anything where precise value reading matters

The circos plot is a beautiful but dangerous default. The most-cited published critique is the genre being applied where it adds no information.

circlize (R) — Modern Default

Goal: Render a multi-track circos plot with ideograms, gene-density histogram, variant-density heatmap, and inter-chromosomal SV links.

Approach: Initialize with chromosome ideograms via circos.initializeWithIdeogram; add tracks with circos.genomicTrack + appropriate panel function; add links with circos.link; always call circos.clear() after the plot completes.

library(circlize)

# 1. Initialize with hg38 ideograms
pdf('circos.pdf', width = 8, height = 8)
circos.par(start.degree = 90,             # 12 o'clock start
           gap.degree = c(rep(1, 23), 5)) # bigger gap before chr1 for visual break
# hg38 specifically benefits from explicit chromosome.index to skip unmapped contigs
circos.initializeWithIdeogram(species = 'hg38',
                               chromosome.index = paste0('chr', c(1:22, 'X', 'Y')),
                               plotType = c('axis', 'labels', 'ideogram'))

# 2. Gene-density histogram (outermost data track)
circos.genomicDensity(gene_bed, col = '#0072B2', track.height = 0.08)

# 3. Variant-density heatmap
circos.genomicHeatmap(variant_bed,
                       col = colorRamp2(c(0, 100), c('white', '#D55E00')),
                       heatmap_height = 0.08, side = 'inside')

# 4. CNV scatter
circos.genomicTrack(cnv_bed, ylim = c(-2, 2),
                     panel.fun = function(region, value, ...) {
                         circos.genomicPoints(region, value,
                                              col = ifelse(value > 0.3, '#D55E00',
                                                           ifelse(value < -0.3, '#0072B2', 'grey60')),
                                              pch = 16, cex = 0.4)
                     },
                     track.height = 0.1)

# 5. Inter-chromosomal SV links
for (i in seq_len(nrow(sv_df))) {
    circos.link(sv_df$chr1[i], c(sv_df$start1[i], sv_df$end1[i]),
                sv_df$chr2[i], c(sv_df$start2[i], sv_df$end2[i]),
                col = '#888888', lwd = 0.4)
}

# 6. CRITICAL -- clear global state
circos.clear()
dev.off()

The circos.clear() Trap

circos.par() settings (start.degree, gap.degree, canvas.xlim, canvas.ylim, clock.wise, circle.margin) are GLOBAL state. After a plot completes, those settings persist into the next plot.

Forgetting circos.clear() produces:

• Next circos.par() calls silently fail to take effect (warning, easily missed in loops)
• Re-initialization may error or render at wrong angles
• Loop-rendered figures inherit state from the previous iteration

Always call circos.clear() after every plot. Make it the last line of the plotting block alongside dev.off().

pyCirclize (Python)

from pycirclize import Circos
import matplotlib.pyplot as plt

sectors = {'chr1': 248956422, 'chr2': 242193529, ...}
circos = Circos(sectors, space=2)                       # space = degree gap between sectors

for sector in circos.sectors:
    sector.text(sector.name, r=110, size=8)
    # outer ideogram
    sector.axis(r_lim=(95, 100), fc='lightgrey')
    # data track
    track = sector.add_track((75, 90))
    track.bar(positions, heights, width=bin_size, color='#0072B2')

# Inter-sector links (chord diagram)
circos.link(('chr1', 1e8, 1.1e8), ('chr5', 2e8, 2.1e8),
            color='#888888', alpha=0.5)

fig = circos.plotfig()
fig.savefig('circos_py.pdf', bbox_inches='tight')

pyCirclize is a younger package than circlize but actively developed (Shimoyama 2024+). API more Pythonic than circlize-via-rpy2.

Circos CLI (Perl) — Most Powerful, Steepest Curve

# config: circos.conf with karyotype, ideogram, plots, links sections
circos -conf circos.conf -outputfile output.png

Circos (Krzywinski 2009) is the original; supports unlimited tracks and arbitrary geometries via configuration. For publication-grade complex figures the Perl tool remains the most powerful. For Python/R workflows, circlize/pyCirclize are more accessible.

Decision Tree by Use Case

Ideogram + Karyogram Without Circos

For per-chromosome data display where circularity is not required, karyoploteR (Gel 2017 Bioinformatics 33:3088) renders linear ideograms with stacked data tracks — often the better choice for CNV per-chromosome views.

library(karyoploteR)
kp <- plotKaryotype(genome = 'hg38', chromosomes = c('chr1', 'chr7', 'chr17'))
kpAddBaseNumbers(kp)
kpLines(kp, data = cnv_gr, y = cnv_gr$log2)
kpAddCytobandLabels(kp)

See copy-number/cnv-visualization for karyoploteR in depth.

Per-Method Failure Modes

circos.clear() forgotten in a loop

Trigger: Plotting multiple circos figures in a for loop without circos.clear() between.

Mechanism: circos.par settings (gap.degree, start.degree, clock.wise) persist across plots.

Symptom: Plots 2..N inherit state from plot 1; gap sizes, rotation differ unexpectedly.

Fix: End every plot block with circos.clear(). Make it a hygiene rule.

Using circular when Cartesian would be better

Trigger: "Circos plot of gene expression across 20 conditions."

Mechanism: Circular impairs value comparison (Cleveland-McGill 1984; Heer-Bostock 2010).

Symptom: Reviewer or coauthor says "I can't tell which condition is highest."

Fix: Use clustered heatmap. Reserve circos for genome-adjacency or chord-diagram use cases.

Too many links produce a black blob

Trigger: Plotting 10000+ chord links between chromosomes.

Mechanism: Overlap saturates the center; no individual link visible.

Symptom: Center of circos is uniformly dark.

Fix: Filter to top-confidence links; OR color-bin by interaction strength with alpha; OR aggregate to chromosome-level summary then link.