CStreet: a computed Cell State trajectory inference method for time-series single-cell RNA sequencing data.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Time-series single-cell RNA sequencing (scRNA-seq) data is rapidly accumulating.
• Connecting distinct cell states to understand continuous transcriptional dynamics is crucial.
• Existing trajectory inference methods often struggle with complex cell state topologies.

Purpose of the Study:

• To develop a novel computational method for inferring cell state trajectories from time-series scRNA-seq data.
• To accurately reconstruct the topology of cell state transitions, including branching paths.
• To provide a robust tool for analyzing dynamic biological processes at the single-cell level.

Main Methods:

• CStreet utilizes time-series information to build k-nearest neighbor connections within and between time points.
• It estimates cell state connection probabilities.
• Trajectory visualization is achieved using a force-directed graph, accommodating multiple start points and paths.

Main Results:

• CStreet demonstrates high accuracy in inferring cell state trajectories on both simulated and real scRNA-seq datasets.
• The method shows high tolerance in reconstructing complex trajectory topologies.
• Performance comparisons validate CStreet's superiority over six commonly used trajectory reconstruction methods.

Conclusions:

• CStreet offers an accurate and robust approach for cell state trajectory inference in time-series scRNA-seq data.
• The method effectively captures continuous transcriptional dynamics and complex branching patterns.
• CStreet enhances the analysis of cell state transitions and underlying biological mechanisms.