heatmap-dimensions

Calculate heatmap dimensions from gene and sample counts for ComplexHeatmap.

Updated Jun 13, 2025
One-click install
npx skills add https://github.com/sahuno/llm_configs --skill heatmap-dimensions
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: heatmap-dimensions
Source: https://github.com/sahuno/llm_configs/tree/main/claude/skills/heatmap-dimensions
Command: npx skills add https://github.com/sahuno/llm_configs --skill heatmap-dimensions

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Heatmaps for gene expression data often require publication-grade sizing and labeling, which can be time-consuming to adjust manually.

Core Features & Use Cases

  • Automatically calculates publication-ready heatmap dimensions based on the number of genes and samples.
  • Integrates with ComplexHeatmap to produce high-quality visuals suitable for journals.
  • Use case: quickly generate a heatmap for DE genes or correlation data with consistent dimensions and clear labeling.

Quick Start

Provide an expression matrix and sample metadata, then generate a publication-quality heatmap with automatically calculated dimensions.

Frequently Asked Questions about heatmap-dimensions

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automatically calculate heatmap dimensions for a publication?▼

You can automatically calculate publication heatmap dimensions by providing an expression matrix and sample metadata, which triggers dynamic height calculation and a fixed 180mm width based on your gene and sample counts.

How do I create a publication-quality heatmap for DE genes using ComplexHeatmap?▼

Creating a publication-quality heatmap for DE genes involves integrating an expression matrix with ComplexHeatmap, which ensures precise sizing and clear labeling suitable for journal submission.

Can I generate correlation matrix heatmaps with fixed width and dynamic height?▼

Yes, you can generate correlation matrix heatmaps with a fixed 180mm width and dynamic height calculation, automatically adjusting the visualization size based on your specific matrix dimensions.

What is the best way to format gene expression heatmaps for journals?▼

The best way to format gene expression heatmaps for journals is to use automatic dimension calculation that enforces a fixed 180mm width and dynamic height, ensuring publication-friendly output directly from your data.

Do I need sample metadata to generate a publication-ready heatmap?▼

Yes, you need to provide sample metadata along with your expression matrix to generate a publication-ready heatmap with automatically calculated dimensions and clear labeling.