Circos Plot Generator

Circos Plot Generator

Generate configuration files for Circos circular visualization plots, enabling genomics data visualization including genomic variations, chromosome ideograms, cell-cell communication networks, and custom track annotations.

Key Capabilities:

• Genomic Variation Visualization: SNPs, CNVs, structural variants (translocations, inversions)
• Cell-Cell Communication Networks: Intercellular interactions and signaling pathways
• Chromosome Ideograms: Chromosome structure with bands and annotations
• Multiple Track Types: Histograms, scatter plots, links, heatmaps, text tracks
• Custom Tracks: Histogram and link track types via tracks configuration key
• Publication-Ready Output: High-quality PNG/SVG figures

Input Validation

This skill accepts: genomic variation data (TSV/CSV with chrom, start, end, type, value columns) or cell communication data (TSV with source, target, weight columns), plus optional configuration parameters.

Rendering constraint: This skill generates Circos configuration files only. Rendering requires Circos installed separately (conda install -c bioconda circos). This constraint applies to every invocation.

If the request does not involve generating a Circos configuration for genomic or cell communication data — for example, asking to perform variant calling, run statistical analysis, or create non-circular plots — do not proceed. Instead respond:

"Circos Plot Generator is designed to generate Circos configuration files for circular genomics visualization. Please provide a data file (TSV/CSV) with genomic coordinates or cell communication data. For other visualization tasks, use a more appropriate tool."

Quick Check

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the input data file, plot type (variation or cell-comm), and output parameters.
2. Data size check: If input data has >5,000 rows, proactively warn: "Large dataset detected (>5,000 rows) — rendering performance may degrade. Consider filtering for significance or increasing bin size before generating the config."
3. Validate that the request matches the documented scope; stop if the task requires unsupported assumptions.
4. Run the script or apply the documented configuration path with only the inputs available.
5. Return a structured result separating assumptions, deliverables, risks, and unresolved items.
6. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Fallback: If --data is missing, respond: "Required parameter --data not provided. Please supply an input data file (TSV/CSV). Cannot generate Circos configuration without input data."

Core Capabilities

1. Genomic Variation Track

from scripts.main import CircosConfig
config = {
    "type": "variation",
    "title": "Sample Genomic Variations",
    "data": "variations.csv",
    "width": 1200, "height": 1200,
    "color_scheme": "nature",
    "output": "./circos_output"
}
generator = CircosConfig(config)
config_path = generator.generate()

Input Data Format:

2. Cell-Cell Communication

config = {
    "type": "cell-comm",
    "title": "Tumor Microenvironment Interactions",
    "data": "cell_communication.csv",
    "color_scheme": "cell",
    "output": "./cell_comm_plots"
}

Input Format:

3. Custom Tracks

The custom track type supports histogram and link tracks via the tracks configuration key:

config = {
    "type": "custom",
    "tracks": [
        {"type": "histogram", "data": "expression.txt", "r0": "0.6r", "r1": "0.8r"},
        {"type": "link", "data": "links.txt", "color": "red"}
    ]
}

4. Color Schemes

CLI Usage

# Generate genomic variation Circos plot
python scripts/main.py --data variations.tsv --output genome.svg

# Cell communication plot with custom colors
python scripts/main.py --data cell_comm.tsv --type cell-communication --colors nature

# Custom radius
python scripts/main.py --data data.tsv --radius 500 --output large.svg

Parameters

Output Files

Constraint: This skill generates configuration files only. Rendering requires Circos installed separately (conda install -c bioconda circos). Always include this constraint in every response.

Output Requirements

Every final response must make these explicit:

• Objective or requested deliverable
• Inputs used (data file, plot type, color scheme) and assumptions introduced
• Configuration generated and track layout
• Core result: config file path and rendering instructions
• Constraints: Rendering requires Circos installed separately (conda install -c bioconda circos). This skill generates configuration files only.
• Unresolved items and next-step checks

Error Handling

• If --data is missing, state the missing parameter and request it. Do not proceed.
• If chromosome naming is inconsistent (chr1 vs 1), flag and request standardization.
• If input data has >5,000 rows, warn about rendering performance and suggest filtering.
• If scripts/main.py fails, report the failure point and provide manual configuration fallback.
• Do not fabricate configuration files or rendering outputs.

Common Pitfalls

• Inconsistent chromosome names: Use consistent "chr" prefix (chr1, not 1)
• Coordinates out of bounds: Verify all positions ≤ chromosome size
• Too many data points: Filter for significance (>5,000 rows degrades rendering); increase bin size
• Tracks overlap: Adjust radius ranges; use transparency
• Image too small: Use minimum 1200×1200 for publications

References

• Circos Official Documentation: http://circos.ca/documentation
• Circos Tutorials: http://circos.ca/documentation/tutorials
• Bioconda Circos: https://bioconda.github.io/recipes/circos/README.html