Simultaneously Infer Cell Pseudotime,Velocity Field and Gene Interaction from Multi-Branch scRNA-seq Data with scPN

TL;DR

scPN is a novel method that simultaneously infers cell pseudotime, velocity fields, and gene interaction networks from multi-branch scRNA-seq data, improving understanding of cellular development.

Contribution

The paper introduces scPN, the first approach to jointly model pseudotime, velocity, and gene interactions in complex multi-branch differentiation scenarios.

Findings

scPN outperforms existing methods in reconstructing cellular dynamics.

It accurately identifies key transcription factors involved in development.

Demonstrates effectiveness on synthetic and real scRNA-seq datasets.

Abstract

Modeling cellular dynamics from single-cell RNA sequencing (scRNA-seq) data is critical for understanding cell development and underlying gene regulatory relationships. Many current methods rely on single-cell velocity to obtain pseudotime, which can lead to inconsistencies between pseudotime and velocity. It is challenging to simultaneously infer cell pseudotime and gene interaction networks, especially in multi-branch differentiation scenarios. We present single-cell Piecewise Network (scPN), a novel high-dimensional dynamical modeling approach that iteratively extracts temporal patterns and inter-gene relationships from scRNA-seq data. To tackle multi-branch differentiation challenges, scPN models gene regulatory dynamics using piecewise gene-gene interaction networks, offering an interpretable framework for deciphering complex gene regulation patterns over time. Results on synthetic data and multiple scRNA-seq datasets demonstrate the superior performance of scPN in reconstructing cellular dynamics and identifying key transcription factors involved in development compared to existing methods. To the best of our knowledge, scPN is the first attempt at modeling that can recover pseudotime, velocity fields, and gene interactions all at once on multi-branch datasets.