Grouped graphical Granger modeling for gene expression regulatory networks discovery

Abstract

We consider the problem of discovering gene regulatory networks from time-series microarray data. Recently, graphical Granger modeling has gained considerable attention as a promising direction for addressing this problem. These methods apply graphical modeling methods on time-series data and invoke the notion of ‘Granger causality’ to make assertions on causality through inference on time-lagged effects. Existing algorithms, however, have neglected an important aspect of the problem—the group structure among the lagged temporal variables naturally imposed by the time series they belong to. Specifically, existing methods in computational biology share this shortcoming, as well as additional computational limitations, prohibiting their effective applications to the large datasets including a large number of genes and many data points. In the present article, we propose a novel methodology which we term ‘grouped graphical Granger modeling method’, which overcomes the limitations mentioned above by applying a regression method suited for high-dimensional and large data, and by leveraging the group structure among the lagged temporal variables according to the time series they belong to. We demonstrate the effectiveness of the proposed methodology on both simulated and actual gene expression data, specifically the human cancer cell (HeLa S3) cycle data. The simulation results show that the proposed methodology generally exhibits higher accuracy in recovering the underlying causal structure. Those on the gene expression data demonstrate that it leads to improved accuracy with respect to prediction of known links, and also uncovers additional causal relationships uncaptured by earlier works.Contact: aclozano@us.ibm.com

Metabolic Pathway Relationships Revealed by an Integrative Analysis of the Transcriptional and Metabolic Temperature Stress-Response Dynamics in Yeast

“…Furthermore, by applying the concept of Granger causality, directed metabolite-transcript temporal associations indicative of potential cause-effect relationships may become identifiable. Granger causality was shown in the past to yield meaningful directed relationships between transcripts when applied to gene expression time series (Lozano et al, 2009;Mukhopadhyay and Chatterjee, 2007). Here, we aim to identify candidate pairs for directed cause-effect relationships across different levels of molecular organization and to test whether these directed relationships relate to the preferred directions of metabolic reactions as currently understood.…”

Temporal Graphical Models for Cross-Species Gene Regulatory Network Discovery

“…Recently, L 1 -based auto-regression algorithms have been adapted and combined with Granger casuality 9 to discover the temporal "causal" networks between genes from time-series microarray data that reveals important dependency information between current observations and histories 8 . This approach serves as the foundation of our proposed algorithm.…”

“…Recently, several graphical modeling approaches have been developed to determine the causal relationships between multiple time series variables 19,20,8 . These approaches are based on L 1 regularized regression (e.g.…”

“…hidden Markov random field with L 1 penalty, to solve this problem. It is based on the temporal causal models 10,11,8 , but unlike 8 , which can handle only one task (i.e. one species, cell type or environmental condition), our proposed method performs regulatory network discovery in a multi-task learning manner.…”

Granger causality with signal-dependent noise

“…Actually, Granger causality has been widely used to explore intrinsic relationships in temporal data, such as neurophysiological recordings (Ge et al, 2009), financial data (Hong et al, 2009), gene expression data (Lozano et al, 2009), etc. Recently, many new insights and developments of Granger causality have also been reported (Barnett et al, 2009;Dhamala et al, 2008).…”