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Inferring regulatory networks from expression data using tree-based methods.

ABSTRACT: One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.

SUBMITTER: Huynh-Thu VA

PROVIDER: S-EPMC2946910 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundA fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown. In this study, we evaluate the applicability of five general methods and three single cell methods for inferring gene regulatory networks from both experimental single cell gene expression data and in silico simulated data.ResultsStandard evaluation metrics using ROC curves and Precision-Recall curves against reference sets sourced from the literature demonstrated that most of the methods performed poorly when they were applied to either experimental single cell data, or simulated single cell data, which demonstrates their lack of performance for this task. Using default settings, network methods were applied to the same datasets. Comparisons of the learned networks highlighted the uniqueness of some predicted edges for each method. The fact that different methods infer networks that vary substantially reflects the underlying mathematical rationale and assumptions that distinguish network methods from each other.ConclusionsThis study provides a comprehensive evaluation of network modeling algorithms applied to experimental single cell gene expression data and in silico simulated datasets where the network structure is known. Comparisons demonstrate that most of these assessed network methods are not able to predict network structures from single cell expression data accurately, even if they are specifically developed for single cell methods. Also, single cell methods, which usually depend on more elaborative algorithms, in general have less similarity to each other in the sets of edges detected. The results from this study emphasize the importance for developing more accurate optimized network modeling methods that are compatible for single cell data. Newly-developed single cell methods may uniquely capture particular features of potential gene-gene relationships, and caution should be taken when we interpret these results.

Project description:Under both physiological and pathological conditions gene expression programs are shaped through the interplay of regulatory proteins and their gene targets, interactions between which form intricate gene regulatory networks (GRN). While the assessment of genome-wide expression for the complete set of genes at a given condition has become rather straight-forward and is performed routinely, we are still far from being able to infer the topology of gene regulation simply by analyzing its "descendant" expression profile. In this work we are trying to overcome the existing limitations for the inference and study of such regulatory networks. We are combining our approach with state-of-the-art gene set enrichment analyses in order to create a tool, called Regulatory Network Enrichment Analysis (RNEA) that will prioritize regulatory and functional characteristics of a genome-wide expression experiment.RNEA combines prior knowledge, originating from manual literature curation and small-scale experimental data, to construct a reference network of interactions and then uses enrichment analysis coupled with a two-level hierarchical parsing of the network, to infer the most relevant subnetwork for a given experimental setting. It is implemented as an R package, currently supporting human and mouse datasets and was herein tested on one test case for each of the two organisms. In both cases, RNEA's gene set enrichment analysis was comparable to state-of-the-art methodologies. Moreover, through its distinguishing feature of regulatory subnetwork reconstruction, RNEA was able to define the key transcriptional regulators for the studied systems as supported from the literature.RNEA constitutes a novel computational approach to obtain regulatory interactions directly from a genome-wide expression profile. Its simple implementation, with minimal requirements from the user is coupled with easy-to-parse enrichment lists and a subnetwork file that may be readily visualized to reveal the most important components of the regulatory hierarchy. The combination of prior information and novel concept of a hierarchical reconstruction of regulatory interactions makes RNEA a very useful tool for a first-level interpretation of gene expression profiles.

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Inferring regulatory networks from expression data using tree-based methods.

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