TrAGEDy-trajectory alignment of gene expression dynamics.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell transcriptomics enables comparisons across biological processes.
• Integrating asymmetric processes in single-cell data is challenging with current methods.
• Existing approaches often require sample integration before analysis, risking errors.

Purpose of the Study:

• To introduce Trajectory Alignment of Gene Expression Dynamics (TrAGEDy) for aligning independent single-cell trajectories.
• To overcome limitations of current methods in handling asymmetric biological processes.
• To improve the analysis of complex biological systems using single-cell data.

Main Methods:

• TrAGEDy aligns independent trajectories, bypassing the need for sample integration.
• The method is implemented in R and freely available.
• Performance is evaluated on simulated and real biological datasets.

Main Results:

• TrAGEDy accurately recovers underlying alignments in simulated data, outperforming existing tools.
• The method successfully captures complex asymmetric alignments missed by other approaches.
• Application to T cell development and Trypanosoma brucei knockout data identified more relevant genes and processes.

Conclusions:

• TrAGEDy offers a robust solution for integrating asymmetric single-cell trajectories.
• The tool enhances the discovery of biologically significant findings in complex developmental studies.
• TrAGEDy provides a valuable advancement for single-cell data analysis in comparative biology.