PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories.

Area of Science:

• Computational biology
• Single-cell genomics
• Systems biology

Background:

• Inferring cell differentiation trajectories from single-cell data is crucial for understanding developmental processes.
• Predicting complex, multi-branching differentiation trees from multimodal data remains a significant computational challenge.

Purpose of the Study:

• To present PHLOWER, a novel computational method for inferring complex cell differentiation trajectories from multimodal single-cell data.
• To leverage the harmonic component of the Hodge decomposition for trajectory inference and differentiation tree estimation.

Main Methods:

• PHLOWER utilizes the Hodge decomposition on simplicial complexes to derive harmonic components.
• These harmonic components serve as natural representations for cell differentiation trajectories.
• The method was benchmarked using simulated multi-branching trees and real kidney organoid data.

Main Results:

• PHLOWER successfully infers complex, multi-branching differentiation trees from multimodal single-cell data.
• The method accurately identifies transcription factors regulating off-target cells in kidney organoids.
• Demonstrated robust performance in trajectory inference and regulatory element prediction.

Conclusions:

• PHLOWER provides a powerful framework for reconstructing complex cellular differentiation landscapes.
• The method enhances the prediction of transcriptional regulators by integrating multimodal single-cell information.
• PHLOWER advances the analysis of single-cell data for developmental biology and disease modeling.