Fused regression for multi-source gene regulatory network inference

Abstract

Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

Low‐temperature tolerance of the Antarctic species Deschampsia antarctica: A complex metabolic response associated with nutrient remobilization

“…In the first step, the metabolic associations common to both species were inferred by applying fused LASSO regression, whereas in the second step, the species‐dependent metabolic associations were identified from the unexplained part of the response (i.e., the residuals) by applying LASSO regression. Although fused LASSO regression has been successfully applied to infer conserved and condition‐specific gene regulatory networks across multiple species and conditions (Omranian et al, , Lam, Westrick, Müller, Christiaen, & Bonneau, , Deng et al, 2018; Lyu et al, 2018), the application of fused LASSO in the two‐step approach allows inference of both common and species‐dependent associations (Figure ).…”

High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0

“…Many TFs bind cooperatively as protein complexes, or antagonistically via competitive binding, and explicit modeling of these TF-TF interactions would also improve GRN inference and make novel biological predictions. Finally, we note that core regulatory networks are likely to be conserved between related species, and further work to develop multi-species inference techniques can leverage evolutionary relationships to improve GRN inference [56]. The modular Inferelator 3.0 framework will allow us to further explore these open problems in regulatory network inference without having to repeatedly reinvent and reimplement existing work.…”

Towards precise reconstruction of gene regulatory networks by data integration

“…The problem of inferring gene regulations from multi-omics data is thus changed to identify these parameters in the linear regression equations [87]. More complicated regression techniques joint with the latter categories are expected to achieve more precision reconstruction [57,[88][89][90].…”