supraHex

supraHex maps and visualizes genome-wide omics data using a supra-hexagonal self-organizing map to enable gene clustering, sample correlation, and multilayer integration such as CpG density, replication timing, and gene expression.

Key Features:

• Supra-hexagonal mapping: Uses a supra-hexagonal self-organizing map to self-organize input omics data and reveal high-dimensional patterns.
• Gene clustering and sample correlation: Performs simultaneous gene clustering and sample correlation to organize genes and samples into coherent structures.
• Overlaying additional data: Overlays external datasets (e.g., CpG density, replication timing, expression) onto the trained map for multilayer comparison and to reveal relationships such as late replication, low CpG-density promoters, and reduced gene expression.
• Advanced visualization tools: Produces heatmap visualizations and tree-based analyses of sample relationships to enhance interpretability.
• Scalable post-analysis functionalities: Provides scalable functionalities for downstream exploratory analyses and hypothesis testing on mapped data.

Scientific Applications:

• Replication-timing analysis: Applied to DNA replication timing studies, including mouse embryogenesis, to capture relationships among replication timing, CpG density, and expression.
• Developmental biology: Investigates genomic feature dynamics across developmental stages.
• Epigenetics and integrative omics: Enables integrative multilayer comparisons of omics datasets (e.g., CpG, replication timing, expression) to explore interactions among genomic elements.

Methodology:

Self-organizing supra-hexagonal mapping of input data, simultaneous gene clustering and sample correlation, overlay of additional datasets onto the trained map, and visualization via heatmaps and tree-based analyses.