BRANE Clust: cluster-assisted gene regulatory network inference refinement

The joined Gene Regulatory Network (GRN)  inference and clustering tool BRANE Clust has just been published in BRANE Clust: cluster-assisted gene regulatory network inference refinement in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017 (doi:10.1109/TCBB.2017.2688355).

It is also featured on RNA-seq blog and OMIC tools.

Alternative versions are available as a preprint, on biorxiv, with a page and software and in HAL. Another brick in the BRANE series wall, a series of bioinformatics tools based on graphs and optimization, dedicated to -omics gene expression data for GRN (Gene Regulatory Network) inference.

While traditional Next-generation sequencing (NGS) pipelines often combine motley assumptions (correlation, normalization, clustering, inference), this work is an first step toward gracefully combining network inference and clustering. 

BRANE Clust works as a post-processing tool upon classical network thresholding refinement. From a complete weighted network (obtained from any network inference method) BRANE Clust favors edges both having higher weights (as in standard thresholding) and linking nodes belonging to a same cluster. It  relies on an optimization procedure. It  computes an optimal gene clustering (random walker algorithm) and an optimal edge selection jointly. The introduction of a clustering step in the edge selection process improves gene regulatory network inference. This is demonstrated on both synthetic (five networks of  DREAM4 and network 1 of DREAM5) and real (network 3 of DREAM5) data. These conclusions are drawn after comparing classical thresholding on CLR and GENIE3 networks to our proposed post-processing. Significant improvements in terms of Area Under Precision-Recall curve are obtained. The  predictive power on real data yields promising results: predicted links specific to BRANE Clust reveal plausible biological interpretation. GRN approaches that produce a complete weighted network to prune could benefit from BRANE Clust post-processing.

Escherichia coli network built using BRANE Clust on GENIE3 weights and containing 236 edges. Large dark gray nodes refers to transcription factors (TFs). Inferred edges also reported in the ground truth are colored in black while predictive edges are light gray. Dashed edges correspond to a link inferred by both BRANE Clust and GENIE3 while solid links refer to edges specifically inferred by BRANE Clust.

Abstract:

Discovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells.
Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy issues. BRANE Clust (Biologically-Related A priori Network Enhancement with Clustering) refines gene regulatory network (GRN) inference thanks to cluster information. It works as a post-processing tool for inference methods (i.e. CLR, GENIE3). In BRANE Clust, the clustering is based on the inversion of a system of linear equations involving a graph-Laplacian matrix promoting a modular structure. Our approach is validated on DREAM4 and DREAM5 datasets with objective measures, showing significant comparative improvements. We provide additional insights on the discovery of novel regulatory or co-expressed links in the inferred Escherichia coli network evaluated using the STRING database. The comparative pertinence of clustering is discussed computationally (SIMoNe, WGCNA, X-means) and biologically (RegulonDB). BRANE Clust software is available at:
http://www-syscom.univ-mlv.fr/~pirayre/Codes-GRN-BRANE-clust.html

BRANE Cut: Biologically-Related Apriori Network Enhancement with Graph cuts

[BRANE Cut featured on RNA-Seq blog][Omic tools][bioRxiv preprint][PubMed/Biomed Central][BRANE Cut code][BRANE Omics]

Gene regulatory networks are somehow difficult to infer. This first work from an on-going work on BRANE Omics (termed BRANE *, for Biologically Related Apriori Netwok Enhancement) introduces an optimization method (based on Graph cuts, borrowed from computer vision/image processing) to infer graphs based on biologically-related a priori (including sparsity). It is succesfully tested on DREAM challenge data and an Escherichia coli network, with a specific work to derive optimization parameters from gene network cardinality properties. And it is quite fast.

BRANE Cut: Biologically-Related Apriori Network Enhancement with Graph cuts for Gene Regulatory Network inference (doi, BRANE Cut webpage, preprint)

Background: Inferring gene networks from high-throughput data constitutes an important step in the discovery of relevant regulatory relationships in organism cells. Despite the large number of available Gene Regulatory Network inference methods, the problem remains challenging: the underdetermination in the space of possible solutions requires additional constraints that incorporate a priori information on gene interactions.

Methods: Weighting all possible pairwise gene relationships by a probability of edge presence, we formulate the regulatory network inference as a discrete variational problem on graphs. We enforce biologically plausible coupling between groups and types of genes by minimizing an edge labeling functional coding for a priori structures. The optimization is carried out with Graph cuts, an approach popular in image processing and computer vision. We compare the inferred regulatory networks to results achieved by the mutual-information-based Context Likelihood of Relatedness (CLR) method and by the state-of-the-art GENIE3, winner of the DREAM4 multifactorial challenge.
Results

Our BRANE Cut approach infers more accurately the five DREAM4 in silico networks (with improvements from 6 % to 11 %). On a real Escherichia coli compendium, an improvement of 11.8 % compared to CLR and 3 % compared to GENIE3 is obtained in terms of Area Under Precision-Recall curve. Up to 48 additional verified interactions are obtained over GENIE3 for a given precision. On this dataset involving 4345 genes, our method achieves a performance similar to that of GENIE3, while being more than seven times faster. The BRANE Cut code is available at: http://​www-syscom.​univ-mlv.​fr/~pirayre/Codes-GRN-BRANE-cut.html.

Conclusions: BRANE Cut is a weighted graph thresholding method. Using biologically sound penalties and data-driven parameters, it improves three state-of-the art GRN inference methods. It is applicable as a generic network inference post-processing, due to its computational efficiency.

Keywords:  Network inference, Reverse engineering, Discrete optimization, Graph cuts, Gene expression data, DREAM challenge.

Additional information of the BRANE Power page