TIME-CoExpress: Temporal Trajectory Modeling of Dynamic Gene Co-expression Patterns Using Single-Cell Transcriptomics...

Area of Science:

• Genomics
• Computational Biology
• Developmental Biology

Background:

• Single-cell RNA sequencing (scRNAseq) offers high-resolution transcriptomic data, but standard analyses often overlook gene interactions.
• Gene co-expression analysis is crucial for understanding coordinated gene expression during cellular processes like development.
• Current methods for analyzing gene co-expression along temporal trajectories are limited by assumptions of linear change.

Purpose of the Study:

• To develop a flexible statistical framework for modeling non-linear gene co-expression changes in scRNAseq data along cellular temporal trajectories.
• To address inherent data characteristics of scRNAseq, such as over-dispersion and zero-inflation.
• To identify differential co-expression patterns that provide deeper biological insights into developmental processes.

Main Methods:

• Proposed a copula-based statistical approach incorporating data-driven smoothing functions.
• Developed a modeling framework capable of handling over-dispersion and zero-inflation common in scRNAseq data.
• Evaluated the algorithm's performance through simulation studies and application to a real scRNAseq dataset.

Main Results:

• The proposed copula-based method effectively models non-linear gene co-expression dynamics.
• The approach successfully incorporates and accounts for over-dispersion and zero-inflation in scRNAseq data.
• Identified differential co-expression gene pairs along the cell temporal trajectory in pituitary embryonic development using a mouse model.

Conclusions:

• The novel copula-based method advances the analysis of gene co-expression in scRNAseq data by capturing non-linear relationships.
• This approach provides a more biologically realistic way to study coordinated gene expression changes during development.
• The findings offer new avenues for understanding genetic interactions and regulatory mechanisms in complex biological systems.