# Gene Regulatory Network Inference from Pseudotime-Ordered scRNA-seq Data via Time-Lagged Divergence Measures

## TL;DR

This paper introduces PseudoGRN, a new method for building gene regulatory networks from single-cell RNA sequencing data that tracks changes over time.

## Contribution

PseudoGRN combines pseudotime inference, time-lagged divergence measures, and penalized network inference to improve GRN reconstruction.

## Key findings

- PseudoGRN outperforms existing methods in reconstructing gene regulatory networks from scRNA-seq data.
- The method provides robust and interpretable results for dynamic regulatory mechanisms in single-cell systems.

## Abstract

Inferring cell type-specific gene regulatory networks (GRNs) from time-series single-cell RNA sequencing (scRNA-seq) data is challenging due to sparse temporal resolution, high dimensionality, and inherent cellular heterogeneity. We present a novel integrative framework, called PseudoGRN, that unifies multiple pseudotime inference methods, different time-lagged divergence measures, non-redundant penalized network inference, and partial correlation analysis to reconstruct directed GRNs from time-series scRNA-seq data. Applying our method to the real-world scRNA-seq dataset, we demonstrate its superior performance over existing approaches, offering a robust and interpretable tool for uncovering dynamic regulatory mechanisms in single-cell systems.