A Scalable Algorithm for Structure Identification of Complex Gene Regulatory Network from Temporal Expression Data

TL;DR

This paper introduces a scalable algorithm for reconstructing large gene regulatory networks from temporal expression data, effectively handling networks with thousands of nodes by integrating biological prior knowledge and topological properties.

Contribution

The authors propose a novel regularized algorithm that maintains high performance for large networks, addressing the curse of dimensionality in genome-wide network inference.

Findings

Algorithm scales to networks with 10,000+ nodes

Effective in real influenza infection data analysis

Outperforms existing methods in large-scale network reconstruction

Abstract

Motivation: Gene regulatory interactions are of fundamental importance to various biological functions and processes. However, only a few previous computational studies have claimed success in revealing genome-wide regulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover, recent work suggests that these methods still suffer from the curse of dimensionality if network size increases to 100 or higher. Result: We present a novel scalable algorithm for identifying genome-wide regulatory network structures. The highlight of our method is that its superior performance does not degenerate even for a network size on the order of $10^4$, and is thus readily applicable to large-scale complex networks. Such a breakthrough is achieved by considering both prior biological knowledge and multiple topological properties (i.e., sparsity and hub gene structure) of complex networks in the regularized formulation. We also illustrate the application of our algorithm in practice using the time-course expression data from an influenza infection study in respiratory epithelial cells. Availability and Implementation: The algorithm described in this article is implemented in MATLAB$^\circledR$. The source code is freely available from https://github.com/Hongyu-Miao/DMI.git. Contact: jliu@cs.rochester.edu; hongyu.miao@uth.tmc.edu Supplementary information: Supplementary data are available online.