heatmap-beautifier

What This Skill Does

The Heatmap Beautifier is a specialized OpenClaw AI agent skill designed for bioinformaticians, researchers, and data scientists. It transforms raw gene expression matrices into publication-quality visualizations. At its core, the tool automates the heavy lifting of heatmap generation, including hierarchical clustering for both rows and columns, which helps in identifying co-expressed gene modules. It features intelligent label management, ensuring that even large datasets remain readable by dynamically optimizing font sizes to prevent clutter. Furthermore, it supports the integration of color annotation tracks, allowing users to map metadata like sample groups, treatment conditions, or gene ontologies directly onto the heatmap layout. With support for multiple export formats including high-resolution PDF, PNG, and SVG, it ensures figures are ready for academic manuscripts and presentations.

Installation

To install this skill, run the following command in your terminal within the OpenClaw environment: clawhub install openclaw/skills/skills/aipoch-ai/heatmap-beautifier Ensure you have the required dependencies installed on your system by running: pip install seaborn matplotlib scipy pandas numpy.

Use Cases

• Manuscript Preparation: Quickly standardize the look and feel of multiple figures for a research paper.
• Exploratory Data Analysis: Visualize gene expression trends quickly to identify clusters of interest using the built-in clustering algorithms.
• Metadata Visualization: Clearly delineate sample groupings or clinical phenotypes using customizable color annotation tracks.
• Automated Reporting: Incorporate the skill into a data analysis pipeline to automatically generate heatmap visualizations after differential expression processing.

Example Prompts

1. "I have an expression matrix in 'data.csv'. Please generate a publication-quality heatmap with row clustering and a color track for 'Control' vs 'Treatment'."
2. "Can you beautify the gene expression data in my file 'study_results.csv' and export it as an SVG file?"
3. "Run a demo of the heatmap-beautifier so I can see the default layout and styling options before I upload my own data."

Tips & Limitations

• Input Requirements: The tool strictly requires a CSV file where genes are rows and samples are columns. Ensure your data is normalized (e.g., TPM or RPKM) before input.
• Performance: While highly efficient, extremely large datasets (thousands of genes) may require significant memory; consider subsetting your data to highly variable genes first.
• Out of Scope: This tool is strictly for visualization. It will not perform statistical tests like t-tests or DESeq2; please handle your statistical analysis before passing the matrix to this skill.