Heatmap Beautifier

Source: https://github.com/aipoch/medical-research-skills

Heatmap Beautifier

ID: 147

Professional beautification tool for gene expression heatmaps, automatically adds clustering trees, color annotation tracks, and intelligently optimizes label layout.

When to Use

• Use this skill when the task needs Professional beautification tool for gene expression heatmaps, automatically adds clustering trees, color annotation tracks, and intelligently optimizes label layout.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

See ## Features above for related details.

• Scope-focused workflow aligned to: Professional beautification tool for gene expression heatmaps, automatically adds clustering trees, color annotation tracks, and intelligently optimizes label layout.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• matplotlib: unspecified. Declared in requirements.txt.
• numpy: unspecified. Declared in requirements.txt.
• pandas: unspecified. Declared in requirements.txt.
• seaborn: unspecified. Declared in requirements.txt.

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/heatmap-beautifier"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan."

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Features

• Automatic Clustering: Automatically adds row/column clustering trees based on hierarchical clustering
• Annotation Tracks: Supports multiple color annotation tracks (sample grouping, gene classification, etc.)
• Smart Labels: Automatically calculates optimal font size to avoid row/column label overlap
• Flexible Color Schemes: Built-in multiple professional scientific research color schemes
• Export Options: Supports PDF, PNG, SVG, and other formats

Dependency Installation

pip install seaborn matplotlib scipy pandas numpy

Usage

Basic Usage

from skills.heatmap_beautifier.scripts.main import HeatmapBeautifier

# Initialize
hb = HeatmapBeautifier()

# Load data and generate heatmap
hb.create_heatmap(
    data_path="expression_matrix.csv",
    output_path="output/heatmap.pdf"
)

Heatmap with Annotation Tracks

hb.create_heatmap(
    data_path="expression_matrix.csv",
    output_path="output/heatmap_annotated.pdf",
    # Row annotations (gene classification)
    row_annotations={
        "Gene Type": gene_type_dict,  # {"gene1": "Kinase", "gene2": "Transcription Factor", ...}
        "Pathway": pathway_dict
    },
    # Column annotations (sample grouping)
    col_annotations={
        "Condition": condition_dict,  # {"sample1": "Control", "sample2": "Treatment", ...}
        "Time": time_dict
    },
    # Custom colors
    annotation_colors={
        "Condition": {"Control": "#2ecc71", "Treatment": "#e74c3c"},
        "Gene Type": {"Kinase": "#3498db", "Transcription Factor": "#9b59b6"}
    }
)

Full Parameter Example

hb.create_heatmap(
    data_path="expression_matrix.csv",
    output_path="output/heatmap.pdf",
    title="Gene Expression Heatmap",
    cmap="RdBu_r",                    # Color map
    center=0,                         # Color center value
    vmin=-2, vmax=2,                  # Value range
    row_cluster=True,                 # Row clustering
    col_cluster=True,                 # Column clustering
    standard_scale=None,              # Standardization: "row", "col", None
    z_score=None,                     # Z-score: 0 (row), 1 (col), None
    # Label optimization
    max_row_label_fontsize=10,
    max_col_label_fontsize=10,
    rotate_col_labels=45,             # Column label rotation angle
    hide_row_labels=False,
    hide_col_labels=False,
    # Size
    figsize=(12, 10),
    dpi=300
)

Parameters

Input Data Format

Expression Matrix (CSV)

,sample1,sample2,sample3,sample4
Gene_A,2.5,-1.2,0.8,-0.5
Gene_B,-0.8,1.5,-2.1,0.3
Gene_C,1.2,0.5,-0.7,1.8
...

• First column: Gene names (row index)
• First row: Sample names (column names)
• Data: Expression values (e.g., log2 fold change, TPM, FPKM, etc.)

Annotation File Format

Annotation dictionary format: {item_name: category_value}

Example:

condition_dict = {
    "sample1": "Control",
    "sample2": "Control", 
    "sample3": "Treatment",
    "sample4": "Treatment"
}

Color Schemes

Built-in color schemes:

• "RdBu_r" - Red-Blue (classic differential expression)
• "viridis" - Yellow-Purple (continuous data)
• "RdYlBu_r" - Red-Yellow-Blue
• "coolwarm" - Cool-Warm
• "seismic" - Seismic
• "bwr" - Blue-White-Red

Command Line Usage

# Basic usage
python -m skills.heatmap_beautifier.scripts.main \
    --input expression_matrix.csv \
    --output heatmap.pdf

# With clustering and annotations
python -m skills.heatmap_beautifier.scripts.main \
    --input expression_matrix.csv \
    --output heatmap.pdf \
    --row-cluster \
    --col-cluster \
    --row-annotations row_annot.json \
    --col-annotations col_annot.json \
    --title "Gene Expression"

Output Description

Generated heatmap includes:

1. Main Heatmap: Expression matrix visualization
2. Left Clustering Tree: Row (gene) hierarchical clustering
3. Top Clustering Tree: Column (sample) hierarchical clustering
4. Left Annotation Bar: Row annotations (e.g., gene types)
5. Top Annotation Bar: Column annotations (e.g., sample groups)
6. Color Scale: Color bar corresponding to expression values

Notes

1. Data Preprocessing: It is recommended to perform log2 transformation or standardization on data first
2. Memory Usage: Large datasets (>5000 rows) may take longer
3. Label Visibility: When there are too many rows/columns, some labels will be automatically hidden
4. Clustering Distance: Default uses Euclidean distance and Ward method