Constructing Cell-Specific Causal Networks of Individual Cells for Depicting Dynamical Biological Processes.

Area of Science:

• Genomics and Systems Biology
• Computational Biology
• Molecular Biology

Background:

• Causal inference is vital for understanding biological complexity, cellular behavior, and disease.
• Gene regulatory network (GRN) inference is key to uncovering molecular mechanisms.
• Current GRN inference methods face challenges in dynamic rewiring, causality, and context specificity.

Purpose of the Study:

• To introduce SiCNet, a novel causal network construction method for single-cell gene expression data.
• To address limitations in current GRN inference, particularly for dynamic and context-specific networks.
• To enable single-cell level molecular regulatory network construction.

Main Methods:

• Utilizes single-cell gene expression profiles and a causal inference strategy.
• Constructs cell-specific causal networks and a network outdegree matrix (ODM).
• Applies SiCNet to analyze cellular reprogramming, development, and gene regulation.

Main Results:

• SiCNet successfully constructs single-cell resolution molecular regulatory networks.
• The method enhances cell clustering performance through cell-specific network information.
• Identifies key regulators in cell fate transitions and developmental processes.

Conclusions:

• SiCNet provides a powerful approach for inferring context-specific GRNs at the single-cell level.
• The method offers deep insights into dynamic regulatory processes and gene regulation during development.
• SiCNet advances the understanding of mechanisms governing cellular transitions.