Data Sheet 6_Geometric multidimensional representation of omic signatures.zip

24,796 metabolic regulatory circuitries from 32 TCGA cancer types are represented as convex polytopes in an 18-dimensional latent space. The dataset, created by Higor Almeida Cordeiro Nogueira and last updated in April 2026, provides geometric descriptors like barycenter distance and volume for analyzing signature concordance and complexity. These multidimensional representations aim to make the internal structure of omic signatures more accessible than traditional one-dimensional summaries.

Use Cases

• Compare multi-omic biomarker concordance based on geometric descriptors like barycenter distance and volume
• Stratify metabolic pathways and superfamilies based on geometric phenotypes such as asymmetry and anisotropy
• Analyze complex regulatory systems in cancer based on signatures embedded in a shared latent space integrating clinical constraints

Strengths

• Contains 24,796 metabolic regulatory circuitries reconstructed from a major cancer genomics resource (TCGA)
• Represents data in an 18-dimensional latent space integrating regulatory, phenotypic, and clinical constraints
• Provides derived geometric descriptors like barycenter distance and volume for quantitative analysis

Limitations

• Row count for the derived geometric descriptors is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• Data may reflect the specific biases inherent to the TCGA source and the described geometric framework

Provenance

Source

figshare

Collection Method

Geometric framework applied to metabolic circuitries reconstructed from TCGA data

Freshness

Last updated 2026-04-17 05:22:13