Time-lagged Ordered Lasso for network inference

TL;DR

This paper introduces a time-lagged Ordered Lasso method with temporal constraints for more accurate gene regulatory network inference from limited and sparse time-course data, incorporating prior knowledge for enhanced discovery.

Contribution

It adapts the Ordered Lasso for dynamic network inference and develops a semi-supervised approach to embed prior network information, improving accuracy and discovery.

Findings

Accurate network inference on simulated and real data.

Effective incorporation of prior network information.

Improved handling of sparse, short time-course datasets.

Abstract

Accurate gene regulatory networks can be used to explain the emergence of different phenotypes, disease mechanisms, and other biological functions. Many methods have been proposed to infer networks from gene expression data but have been hampered by problems such as low sample size, inaccurate constraints, and incomplete characterizations of regulatory dynamics. Since expression regulation is dynamic, time-course data can be used to infer causality, but these datasets tend to be short or sparsely sampled. In addition, temporal methods typically assume that the expression of a gene at a time point depends on the expression of other genes at only the immediately preceding time point, while other methods include additional time points without any constraints to account for their temporal distance. These limitations can contribute to inaccurate networks with many missing and anomalous links. We adapted the time-lagged Ordered Lasso, a regularized regression method with temporal monotonicity constraints, for \textit{de novo} reconstruction. We also developed a semi-supervised method that embeds prior network information into the Ordered Lasso to discover novel regulatory dependencies in existing pathways. We evaluated these approaches on simulated data for a repressilator, time-course data from past DREAM challenges, and a HeLa cell cycle dataset to show that they can produce accurate networks subject to the dynamics and assumptions of the time-lagged Ordered Lasso regression.