bio-data-visualization-heatmaps-clustering

What problem does it solve?

This Skill addresses the need to visualize complex biological data matrices, such as gene expression or methylation levels, by employing hierarchical clustering to reveal underlying patterns, co-expression modules, and sample relationships.

Core Features & Use Cases

• Clustered Heatmaps: Generates heatmaps with dendrograms for rows and columns to show similarity and group related features or samples.
• Tool Flexibility: Supports R packages pheatmap and ComplexHeatmap, as well as Python's seaborn for diverse visualization needs.
• Annotation Support: Integrates sample and feature annotations to contextualize the data patterns.
• Use Case: Visualize differential gene expression results across different experimental conditions, highlighting genes that are coordinately up- or down-regulated and samples that cluster together based on their expression profiles.

Quick Start

Create a heatmap of my expression matrix with row and column clustering and annotations.