Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell transcriptomic assays allow reconstruction of lineage differentiation trajectories and characterization of cellular heterogeneity.
• Existing methods primarily focus on transcriptomic data, with limited capabilities for epigenomic data analysis and trajectory visualization.

Purpose of the Study:

• To present STREAM, an interactive pipeline for disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.
• To provide an open-source software package for advanced single-cell data analysis.

Main Methods:

• Developed an interactive pipeline named STREAM.
• Integrated analysis of single-cell transcriptomic and epigenomic data.
• Tested STREAM on synthetic and real datasets from various single-cell technologies.

Main Results:

• STREAM successfully disentangles and visualizes complex branching trajectories.
• Demonstrated utility in analyzing myoblast differentiation.
• Showcased effectiveness in resolving heterogeneity in hematopoiesis across different organisms.

Conclusions:

• STREAM is a versatile and powerful tool for analyzing complex single-cell differentiation trajectories.
• The pipeline enhances the understanding of cellular heterogeneity and state transitions using multi-omic data.
• STREAM is an open-source solution, promoting accessibility and further development in the field.