Data Sheet 1_Geometric multidimensional representation of omic signatures.docx

A geometric framework transforms multi-omic signatures into measurable objects in an 18-dimensional latent space. The dataset includes 24,796 metabolic regulatory circuitries reconstructed across 32 TCGA cancer types, with derived geometric descriptors. The data was authored by Higor Almeida Cordeiro Nogueira and last updated on 2026-04-17.

Use Cases

• Quantifying concordance and divergence in regulatory systems based on geometric measurements like barycenter distance and volume.
• Stratifying metabolic pathways and superfamilies based on geometric phenotypes derived from paired regulatory and metabolic signatures.
• Comparing multi-omic biomarkers using a geometric framework that preserves internal structural organization.
• Analyzing complex regulatory systems in cancer using intrinsic geometric measurements of asymmetry and anisotropy.

Strengths

• Contains 24,796 metabolic regulatory circuitries, providing a substantial scale for analysis.
• Covers 32 distinct TCGA cancer types, offering broad disease representation.
• Data is available through an interactive Shiny application (SigPolytope), facilitating exploration.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the underlying geometric descriptor tables is unknown, which may limit suitability assessment.
• The primary file is a 40.4 KB DOCX document, suggesting the dataset itself may be a summary or description rather than the raw data.

Provenance

Source

figshare

Collection Method

Geometric analysis applied to reconstructed metabolic circuitries from TCGA data.

Freshness

Last updated 2026-04-17 05:22:15; freshness should be verified.