Time-lagged Ordered Lasso for network inference

• Published in: BMC bioinformatics Vol. 19; no. 1; pp. 545 - 15
• Main Authors: Nguyen, Phan, Braun, Rosemary
• Format: Journal Article
• Language: English
• Published: London BioMed Central 29.12.2018; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Bioinformatics; Biomedical and Life Sciences; Causality; Cell cycle; Cells (Biology); Cheminformatics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Datasets; Dynamical systems; Gene expression; Gene Expression - genetics; Gene network reconstruction; Gene regulation; Gene Regulatory Networks - genetics; Genomes; Humans; Lasso; Life Sciences; Methodology; Methodology Article; Microarrays; Network inference; Networks; Networks analysis; Penalized regression; Phenotypes; Regression Analysis; Regularization; Reverse engineering; Time course data; Network inference; Gene regulation; Lasso; Gene network reconstruction; Penalized regression; Regularization
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-018-2558-7

Background 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. Results We adapted the time-lagged Ordered Lasso, a regularized regression method with temporal monotonicity constraints, for 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. R code is available at https://github.com/pn51/laggedOrderedLassoNetwork . Conclusions 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.