SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data.

Area of Science:

• Computational Biology
• Genomics
• Developmental Biology

Background:

• Single-cell experiments offer snapshots of biological processes.
• Reconstructing dynamic biological processes from static snapshots is challenging due to nonlinear gene expression and branching cell populations.

Purpose of the Study:

• To develop a computational method for inferring complex cellular dynamics from single-cell data.
• To address challenges including nonlinear gene expression, irrelevant genes, and branching trajectories.

Main Methods:

• Introduced SLICER (Selective Locally Linear Inference of Cellular Expression Relationships).
• SLICER infers nonlinear trajectories, selects relevant genes, and identifies branching points and loops.
• Evaluated performance on simulated data and previously published single-cell datasets.

Main Results:

• SLICER accurately reconstructs nonlinear developmental trajectories.
• The method effectively identifies and characterizes trajectory branches and loops.
• SLICER demonstrates superior performance in ordering cells along trajectories compared to existing methods.

Conclusions:

• SLICER is a robust tool for analyzing complex single-cell data.
• The method enhances the understanding of biological processes by accurately inferring cellular dynamics.
• SLICER's ability to handle nonlinearities and branching trajectories makes it valuable for developmental biology research.