Data Sheet 4_Geometric multidimensional representation of omic signatures.xlsx

A geometric framework dataset representing 24,796 metabolic regulatory circuitries across 32 TCGA cancer types as convex polytopes in an 18-dimensional latent space. Created by Higor Almeida Cordeiro Nogueira and last updated in April 2026, it provides intrinsic geometric measurements like barycenter distance, volume, and anisotropy to quantify signature concordance and complexity. The data is available through a Shiny application and is shared under a CC-BY-4.0 license.

Use Cases

• Compare and de-redundant multi-omic biomarkers based on geometric signatures.
• Analyze metabolic pathway stratification using geometric phenotypes like asymmetry and anisotropy.
• Study regulatory and metabolic signature concordance across cancer types using barycenter distance.
• Investigate high-dimensional geometric organization of omic signatures beyond vector-based summaries.

Strengths

• Contains data for 24,796 metabolic regulatory circuitries.
• Covers 32 distinct TCGA cancer types.
• Encodes signatures in a shared 18-dimensional latent space.
• Provides derived geometric descriptors like volume and anisotropy.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect bias inherent to the TCGA source.

Provenance

Source

TCGA (The Cancer Genome Atlas) and author's geometric framework.

Collection Method

Geometric framework applied to reconstructed metabolic regulatory circuitries.

Freshness

Last updated 2026-04-17 05:22:16.