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Updated: Jan 11, 2026
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
Published on: March 12, 2021
Simultaneously infer cell pseudotime, velocity field, and gene interaction from multi-branch scRNA-seq data with
Zhen Zhou1, Jiachen Li1, Hongyi Xin2
1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
This study introduces single-cell Piecewise Network (scPN), a new method for modeling cell development from single-cell RNA sequencing data. scPN simultaneously infers cell pseudotime, velocity, and gene interactions, improving understanding of complex differentiation processes.
Area of Science:
- Computational Biology
- Genomics
- Systems Biology
Background:
- Single-cell RNA sequencing (scRNA-seq) is crucial for dissecting cellular dynamics and gene regulatory networks.
- Existing methods often struggle to reconcile pseudotime and velocity estimations, particularly in complex multi-branch differentiation scenarios.
- Simultaneous inference of cell pseudotime and gene interaction networks remains a significant challenge.
Purpose of the Study:
- To develop a novel computational approach for high-dimensional dynamical modeling of scRNA-seq data.
- To address the limitations of current methods in handling multi-branch differentiation.
- To enable the simultaneous inference of cellular pseudotime, velocity fields, and gene interaction networks.
Main Methods:
- Introduction of single-cell Piecewise Network (scPN), a high-dimensional dynamical modeling approach.
- Iterative extraction of temporal patterns and inter-gene relationships from scRNA-seq data.
- Modeling gene regulatory dynamics using piecewise gene-gene interaction networks to capture multi-branch differentiation.
Main Results:
- scPN demonstrates superior performance in reconstructing cellular dynamics on synthetic and real scRNA-seq datasets.
- The method effectively identifies key transcription factors involved in cellular development.
- scPN successfully recovers pseudotime, velocity fields, and gene interactions simultaneously for multi-branch datasets.
Conclusions:
- scPN offers an interpretable framework for deciphering complex gene regulation patterns over time.
- This novel approach overcomes limitations of existing methods in modeling multi-branch differentiation.
- scPN represents a significant advancement in simultaneously modeling cellular dynamics and gene regulatory interactions from scRNA-seq data.
