DiNiro

DiNiro reconstructs de-novo transcriptional gene regulatory network modules from single-cell RNA sequencing (scRNA-seq) data and identifies modules that differentiate single-cell clusters.

Key Features:

• De-novo Reconstruction: Constructs transcriptional regulatory network modules from scRNA-seq data without relying on pre-existing models or databases.
• Identification of Differentiating Modules: Pinpoints small, interpretable network modules that distinguish between single-cell clusters.
• Mechanistic Insight: Provides mechanistic models that explain differential cellular gene expression programs across clusters.
• Application in Disease Mechanism Research: Learns differential regulatory mechanisms directly from single-cell data to uncover disease-associated regulatory changes and potential therapeutic targets.

Scientific Applications:

• Developmental Biology: Investigate how specific regulatory networks contribute to cell fate decisions during development.
• Oncology: Explore heterogeneity within tumor microenvironments by identifying regulatory modules associated with cancer subtypes or stages.
• Immunology: Study immune responses at single-cell resolution to reveal regulatory mechanisms driving diverse functional states of immune cells.

Methodology:

Integrates differential expression analysis with network modeling to identify and reconstruct transcriptional gene regulatory networks from scRNA-seq data.