HilbertCurve

HilbertCurve maps one-dimensional genomic data onto two-dimensional Hilbert curves to visualize chromosome- and genome-wide datasets while preserving spatial locality.

Key Features:

• High-Resolution Visualization: Represents genomic information at high resolution using Hilbert curve space-filling mappings for dense data display.
• Chromosome- and Genome-wide Scaling: Supports visualization at both chromosome-scale and whole-genome scales to accommodate different analysis scopes.
• Virtual Axis Transformation: Abstracts Hilbert curve construction into a virtual axis for algorithmic mapping of linear genomic coordinates to the curve.
• Multiple-Layer Overlay Support: Enables overlaying multiple genomic feature layers to visualize spatial relationships and co-localization across datasets.
• Locality Preservation: Maintains proximal relationships from the one-dimensional genomic sequence when mapping to the two-dimensional Hilbert layout.

Scientific Applications:

• Genome-wide visualization: Visualizing large-scale genomic datasets across whole genomes to inspect distribution and density of features.
• Chromosome-scale analysis: Examining patterns and spatial organization within individual chromosomes.
• Spatial correlation of genomic features: Correlating spatial distributions of multiple genomic feature layers to identify co-localization or segregation.
• Pattern and anomaly detection: Identifying patterns, hotspots, and anomalies in genomic data through two-dimensional spatial representation.

Methodology:

Maps one-dimensional genomic coordinates onto a two-dimensional Hilbert curve using an algorithmic transformation that preserves locality and implements the curve construction via a virtual axis abstraction.