Reconstruction of Single-Cell Trajectories Using Stochastic Tree Search

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) provides high-resolution insights into cellular dynamics.
• Trajectory inference methods are crucial for reconstructing developmental processes and estimating cell pseudotimes.
• Current methods often yield locally optimal solutions, limiting biological discovery.

Purpose of the Study:

• To develop a novel framework for robust cell trajectory inference and pseudotime estimation.
• To address the limitations of existing methods in finding globally optimal solutions.
• To enhance the accuracy and reliability of pseudotime analysis in scRNA-seq data.

Main Methods:

• A penalized likelihood-based framework for modeling cell trajectories.
• Introduction of a stochastic tree search (STS) algorithm to explore the tree space.
• Application of the STS algorithm to reconstruct global cell lineages.

Main Results:

• The proposed STS algorithm demonstrates superior accuracy in cell ordering compared to existing methods.
• Pseudotime estimation using the STS framework is more robust and reliable.
• Experiments with both simulated and real scRNA-seq data validate the approach's effectiveness.

Conclusions:

• The penalized likelihood and STS framework offers a significant advancement in trajectory inference for scRNA-seq.
• This method provides a more accurate and globally optimal solution for understanding dynamic cellular processes.
• The enhanced pseudotime estimation facilitates deeper biological insights from single-cell data.