TreeAndLeaf

TreeAndLeaf visualizes binary trees and dendrograms by integrating unrooted and force-directed graph algorithms with traditional tree layout techniques to emphasize and display detailed information on leaf elements in biological datasets.

Key Features:

• Hybrid Layout Strategy: Combines unrooted and force-directed graph algorithms with traditional tree layout techniques to produce flexible representations of hierarchical data.
• Focus on Leaf Elements: Supports projecting multiple layers of information onto leaf nodes to display detailed annotations associated with each node.
• Integrated Layout Algorithms: Integrates different layout algorithms into a single computational framework for consistent combined layouts.
• Case Studies: Demonstrates applications on breast cancer epidemiological and experimental datasets to illustrate visualization of complex biological relationships.
• Implementation: Implemented in R and distributed as a Bioconductor package.

Scientific Applications:

• Genomics: Visualizing hierarchical relationships and leaf-associated annotations in large genomic trees.
• Epidemiology: Displaying hierarchical clustering and detailed sample-level metadata in epidemiological studies, including breast cancer datasets.
• Systems Biology: Representing complex hierarchical interactions and node-level data in systems biology analyses.
• Exploratory and Hypothesis-Driven Analysis: Supporting exploratory visualization and hypothesis evaluation by exposing rich leaf-level information within tree structures.

Methodology:

Combines unrooted graph layouts, force-directed graph algorithms, and traditional tree layout techniques and projects multiple annotation layers onto leaf nodes.