 Software
 Open Access
 Published:
GeNeCK: a web server for gene network construction and visualization
BMC Bioinformatics volume 20, Article number: 12 (2019)
Abstract
Background
Reverse engineering approaches to infer gene regulatory networks using computational methods are of great importance to annotate gene functionality and identify hub genes. Although various statistical algorithms have been proposed, development of computational tools to integrate results from different methods and userfriendly online tools is still lagging.
Results
We developed a web server that efficiently constructs gene networks from expression data. It allows the user to use ten different network construction methods (such as partial correlation, likelihood, Bayesian and mutual informationbased methods) and integrates the resulting networks from multiple methods. Hub gene information, if available, can be incorporated to enhance performance.
Conclusions
GeNeCK is an efficient and easytouse web application for gene regulatory network construction. It can be accessed at http://lce.biohpc.swmed.edu/geneck.
Background
A gene regulatory network (GRN) describes biological interactions among genes and provides a systematic understanding of cellular signaling and regulatory processes. It depicts how a set of genes interact with each other to form a functional module and how different gene modules are related. A typical GRN approximates a scalefree network topology with a few highly connected genes (i.e. hub genes) and many poorly connected nodes [1]. These hub genes are master regulators in a gene network, and usually play essential roles in a biological system. Investigations of GRN can facilitate the systematic functional annotation of genes [2] and help identify the hub genes, which may lead to potential clinical applications [3].
Reverse engineering approaches to construct gene networks from transcriptomic data have greatly facilitated biomedical research. Statistical methods proposed for inferring network structure can be categorized into four classes: 1) probabilistic networkbased approaches, mainly Bayesian networks (BN); 2) correlationbased methods; 3) partial correlationbased methods; and 4) information theorybased methods [4]. Comparative evaluation among different methods for constructing large scale GRNs revealed the strengths and weaknesses of each method with respect to different scenarios, with no single method outperforming others universally [5]. An ensemblebased network aggregation (ENA) method was proposed to integrate different methods to improve the accuracy of network inference [6]. Recent advancements in statistical methods have extended algorithms to incorporate prior knowledge of hub genes [7]. Besides above statistical methods that aim to infer the latent covariance matrix of all the components in a graph using gene expression data, other algorithms like Petri Nets [8] and ordinary differential equations (ODE) [9] focus more on simulating the dynamics of specific pathways that involve important disease genes.
Despite the development of various computational methods and corresponding R packages for inferring genegene interactions, implementation of those algorithms with graphical interface is still lagging. CoExpNetViz [10] is an online tool developed for constructing coexpression networks in plant research, but its application is limited by simple statistics and compulsory “bait” genes input. To provide easy accessibility for the network construction tool, we introduce a web server called GeNeCK (Gene Network Construction Tool Kit, see Fig. 1) which allows users to upload their own gene expression data and choose their preferred method to infer and visualize the network, as well as integrate different methods to obtain a more confident result.
Implementation
GeNeCK is a web server (http://lce.biohpc.swmed.edu/geneck) with a userfriendly graphical interface. A quick user guide on how to upload data and submit jobs is provided on the website and in the supplementary material (Additional file 1: Figure S9). GeNeCK offers the flexibility for experienced users to select methods and set preferred parameters. Using ENA is more straightforward for most users since it generally performs well in all scenarios, does not require choosing tuning parameters, and can provide a pvalue for each connection, which indicates the statistical significance of the connection. The constructed network will be displayed on the website once the job is finished (Fig. 1). Genes with a high degree of connection (i.e. hub genes) will be plotted with different colors. Users can interactively explore the constructed network. Clicking on a specific gene will highlight the gene itself along with its connected neighbors, and the corresponding information will be displayed at the bottom (Fig. 1).Although the current version of GeNeCK does not provide a function for users to download the figure, users can use screenshot software tools to get the figure for the network structure. We recommend that users download and import the constructed network structure into other visualization tools, such as Cytoscape, for further visualization and analysis (Additional file 2: Figure S10).
Methods
GeNeCK allows users to construct network using 11 different methods (summarized in Additional file 3: Table S1). Readers can refer to Yu et al. [7] for a comprehensive review of the different network construction methods.
Network inference methods
Partial correlationbased methods calculate the inverse covariance matrix Ω (also known as the precision matrix) of gene expressions, in which ω_{j,h}=0 indicates gene j and h given the expressions of all the other genes is conditional independent. GeneNet [11] employs MoorePenrose pseudoinverse and bootstrap methods to obtain a shrink estimate of Ω. Meinshausen and Bühlmann [12] proposed the neighborhood selection (NS) method, which converts the precision matrix estimation problem to a regression problem by fitting a LASSO to each gene using others as predictors. Sparse partial correlation estimation (SPACE) is a joint spare regression model developed by Peng et al. [13], which resolves a symmetrically constrained and L_{1}regularizated regression problem under highdimensional settings.
Likelihoodbased approaches, such as graphical LASSO (GLASSO [14]) and GLASSO with a reweighted strategy for scalefree networks (GLASSOSF [15]), optimize a penalized maximum likelihood function to estimate Ω. Bayesian graphical LASSO (BayesianGLASSO [16]) is a fully Bayesian treatment of GLASSO that uses a double exponential prior and employs a block Gibbs sampler for exploring the posterior distribution.
Mutual information (MI) is a measure in information theory of pairwise dependency between two variables. Zhang et al. [17] proposed a path consistency algorithm based on conditional mutual information (PCACMI) to infer graphical structure, and further conditional mutual inclusive informationbased network inference (CMI2NI [18]) method that improves the PCACMI method.
Hub gene incorporation
Gene networks usually have scalefree characteristics. In other words, there are usually a few hub genes regulating many others. In practice, most of such hub genes in biological pathways have been well studied and validated through biological experiments. To properly incorporate this prior knowledge, Yu et al. [7] proposed extended sparse partial correlation estimation (ESPACE) and extended graphical LASSO (EGLASSO) methods. In these methods, during the covariance estimation of original SPACE and GLASSO methods, hub gene information can be incorporated to improve the network inferences.
Network integration
An ensemblebased network aggregation (ENA) method [6] combines networks reconstructed from different methods. The original ENA algorithm does not report the confidence level of estimated edges. To derive the pvalue of an edge between a pair of genes, we adapted ENA by implementing an additional permutation step to generate the distribution of null hypothesis. We first permute the given gene expression dataset to obtain a resampled dataset D^{(m)}. Then we implement the ENA algorithm to get the ensemble rank matrix \(\tilde {R}^{(m)} \) for this dataset. This procedure is repeated M times. The empirical null distribution F^{null} of all possible pairwise connection for p genes can be obtained based on all the harmonic means in the M permutations, i.e. \(\left \{\tilde {r}^{(m)}_{jh},\,m=1,...,M,1\leq j < h \leq p\right \} \). Then the pvalue of the estimated edge between gene j and h is approximated by the quantile of \(\tilde {r}_{jh} \) in the null distribution F^{null} with BenjaminiHochberg adjustment [19] to avoid multiple comparison problems.
In the simulation studies, we ensembled the networks constructed by NS, GLASSO, GLASSOSF, PCACMI, SPACE, and BayesianGLASSO. GeneNet and CMI2NI were excluded because GeneNet performed the worst in all the scenarios (Additional file 4: Figure S1S8) and CMI2NI produced the exact same results as PCACMI in default settings. We run all the processes in a single node of UT Southwestern BioHPC cluster (Intel(R) Xeon(R) CPU E52650 v3 @ 2.30GHz, 32GB RAM).
Results
To comprehensively evaulate different models, we simulated coexpression data from four real proteinprotein interaction networks (Fig. 2) used in Allen et al. [5], which was selected Keshava Prasad et al. [20]. See the download link for the four real network structure in the Availability of data and materials section. Details of the generative model are discussed below. We investigated the performance of each method for data with various noise levels and sample sizes.
Generative model
We used Gaussian graphical models that are mainly used to infer the gene association network to simulate expression data. Let y_{i}=(y_{i1},…,y_{ij},…,y_{ip}) denotes the collection of expression levels for each gene observed in sample i. This was simulated from a zeromean multivariate normal distribution y_{i}=MN(0_{p}, Σ+ε^{2}I_{p×p}), where 0_{p} denotes the pdimension zero vector and I_{p×p} denotes the pbyp identity matrix. For the covariance matrix Σ, we generated its concentration matrix Ω=Σ^{−1} following Peng, et al. [13]. The initial matrix Ω was created by setting
where Uniform(a,b) represents uniform distribution on interval (a,b), j∼h indicates that there is an edge between gene j and h, \(j\nsim h \) means otherwise. The network structure was chosen from one of the four real proteinprotein interaction networks [20, 21], each of which was approximately scalefree (see Fig. 2). Then, the nonzero elements in Ω were rescaled to assure positive definiteness. Specifically, for each row, we first summed the absolute values of the offdiagonal elements, and divided each offdiagonal entry by 1.5fold their sum. Next, we averaged this rescaled matrix with its transpose to ensure symmetry. We then set 0.1 to those nonzero entries with absolute value smaller than 0.1. After that, the inverse of the final matrix was denoted by A=Ω^{−1}. Each element in the covariance matrix Σ was determined by \(\delta _{jh}=\alpha _{jh}/\sqrt {\alpha _{jj}\alpha _{hh}}\). For the noise level ε, we considered three cases: ε=0, 0.1, 0.5.
Performance metric
We evaluated the result of each method by plotting its operating characteristic curve (ROC) and calculating the area under the ROC curve (AUC). As different methods generate different outputs, we used their corresponding approaches to plot ROC curves for a fair comparison. GeneNet and BayesianGLASSO yield a continuous estimate of each partial correlation ρ_{jh}. They do not require a tuning parameter. Thus, an edge between gene j and h was determined if the absolute value of ρ_{jh} was greater than a certain threshold. Then the ROC curves were obtained by plotting false positive rates (FPRs) against true positive rates (TPRs) under different thresholds. For mutual informationbased methods, we choose the tuning parameter α=0.03 as suggested by the authors [17, 18]. Then, an edge between gene j and h was determined if the estimated entropy was greater than a threshold. The ROC curves were obtained by plotting FPRs against TPRs under different thresholds. Note that we only included PCACMI in the simulation, since CMI2NI produced the same result as PCACMI did. For the other methods that need a tuning parameter, the ROC curves were obtained by plotting FPRs and TPRs under different choices of the tuning parameter.
Result summarization
As shown in the result of simulation study (Additional file 4: Figure S1S8), BayesianGLASSO and ENA generally outperform other methods, which is consistent with the literature [6, 16]. Besides, mutual informationbased methods also show competitive results. NS, GLASSO, and GLASSOSF, which share the same strategy, have similar accuracy. As the earliest developed method, GeneNet has significantly lagged performance. Not surprisingly, all methods lose power when either a higher level of noise manifests or a smaller number of samples is generated.
We also logged the computational time of each method in Table S2 (Additional file 5). The Bayesian method consumed several orders of magnitude more time, and it soon went beyond real applicability when the number of genes in the network increased to hundreds. Most other methods shared similar efficacy in the simulation settings, with mutual informationbased methods being a little slower.
Discussion
GeNeCK infers a genegene connection based on the expression pattern of the two genes. It can provide a hint of their potential functional relationship, but does not necessarily imply a real biological interaction. One should be very cautious when interpreting the result, especially when the tuning parameter is out of a reasonable range (e.g. an almost fully connected network may be a sign of choosing a problematic parameter value). As different methods use different measurements to evaluate the confidence of estimated edges (e.g. partial correlation, mutual information), this may not be easy to interpret for users with little statistical background. We suggest users choose the ENA method, which outputs pvalues to indicate the significance of genegene connections. More importantly, it generally achieves the best performance. For extended methods (EGLASSO and ESPACE) that allow for the “hub genes” specification, additional attention needs to be paid when choosing the value for the confidence index α. The α value can be selected by different statistical methods, such as the generalized information criterion (GIC) [22]. In practice, we suggest an initial try with no or a very weak prior brief to see if the genes of interest are picked up by the algorithm. Usually a very small α value is not desired, as the influence of hub genes should already be presented in the data if the prior information is correct. Otherwise this can lead to a biased result.
Conclusion
Reconstructions of gene networks from gene expression data greatly facilitate our understanding of underlying biological mechanisms and provide new opportunities for drug and biomarker discoveries. GeNeCK, the online tool kit presented in this paper, enables us to integrate various statistical methods to construct gene networks based on gene expression data. Furthermore, the information of hub genes, which usually play an essential role in gene regulation and biological processes, could be incorporated into GeNeCK to improve the performance of the related methods. It is believed that the tool will cater to a wide audience in the field of biology.
Availability and requirements
Project name: GeNeCK
Project home page: http://lce.biohpc.swmed.edu/geneck/
Operating systems: Windows, Linux and Mac
Programming language: PHP, HTML, JavaScript and R
License: GPL
Abbreviations
 AUC:

Area under curve
 BayesianGLASSO:

Bayesian graphical LASSO
 CMI2NI:

Conditional mutual inclusive informationbased network inference
 EGLASSO:

Extended GLASSO
 ENA:

Ensemblebased network aggregation
 ESPACE:

Extended SPACE
 GeNeCK:

Gene network construction tool kit
 GLASSO:

Graphical LASSO
 GLASSOSF:

GLASSO with reweighted strategy for scalefree network
 GRN:

Gene regulatory network
 MI:

Mutual information
 NS:

Neighborhood selection
 PCACMI:

Path consistency algorithm based on conditional mutual information
 ROC:

Operating characteristic curve
 SPACE:

Sparse partial correlation estimation
References
 1
Barabasi AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004; 5(2):101.
 2
Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D, Friedman N. Module networks: identifying regulatory modules and their conditionspecific regulators from gene expression data. Nat Genet. 2003; 34(2):166.
 3
Tang H, Xiao G, Behrens C, Schiller J, Allen J, Chow CW, Suraokar M, Corvalan A, Mao J, White MA, et al.A 12gene set predicts survival benefits from adjuvant chemotherapy in nonsmall cell lung cancer patients. Clin Cancer Res. 2013; 19(6):1577–86.
 4
Bansal M, Belcastro V, AmbesiImpiombato A, Bernardo DD. How to infer gene networks from expression profiles. Mol Syst Biol. 2007; 3(1):78.
 5
Allen JD, Xie Y, Chen M, Girard L, Xiao G. Comparing statistical methods for constructing large scale gene networks. PloS ONE. 2012; 7(1):29348.
 6
Zhong R, Allen JD, Xiao G, Xie Y. Ensemblebased network aggregation improves the accuracy of gene network reconstruction. PloS ONE. 2014; 9(11):106319.
 7
Yu D, Lim J, Wang X, Liang F, Xiao G. Enhanced construction of gene regulatory networks using hub gene information. BMC Bioinformatics. 2017; 18(1):186.
 8
Rohr C, Marwan W, Heiner M. Snoopy—a unifying petri net framework to investigate biomolecular networks. Bioinformatics. 2010; 26(7):974–5.
 9
Kim J. Validation and selection of ode models for gene regulatory networks. Chemometr Intell Lab Syst. 2016; 157:104–10.
 10
Tzfadia O, Diels T, Meyer SD, Vandepoele K, Aharoni A, de Peer YV. Coexpnetviz: comparative coexpression networks construction and visualization tool. Front Plant Sci. 2016; 6:1194.
 11
Schäfer J, Strimmer K. A shrinkage approach to largescale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol. 2005; 4(1):1175–1189.
 12
Meinshausen N, Bühlmann P. Highdimensional graphs and variable selection with the lasso. Ann Stat. 2006;:1436–62.
 13
Peng J, Wang P, Zhou N, Zhu J. Partial correlation estimation by joint sparse regression models. J Am Stat Assoc. 2009; 104(486):735–46.
 14
Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008; 9(3):432–41.
 15
Liu Q, Ihler A. Learning scale free networks by reweighted l1 regularization. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics: 2011. p. 40–48.
 16
Wang H, et al.Bayesian graphical lasso models and efficient posterior computation. Bayesian Anal. 2012; 7(4):867–86.
 17
Zhang X, Zhao XM, He K, Lu L, Cao Y, Liu J, Hao JK, Liu ZP, Chen L. Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics. 2011; 28(1):98–104.
 18
Zhang X, Zhao J, Hao JK, Zhao XM, Chen L. Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks. Nucleic Acids Res. 2014; 43(5):31.
 19
Benjamini, Yoav, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodological),. 1995:289–300.
 20
Prasad TSK, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, et al.Human protein reference database—2009 update. Nucleic Acids Res. 2008; 37(suppl_1):767–72.
 21
Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TKB, Chandrika KN, Deshpande N, Suresh S, et al.Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 2004; 32(suppl_1):497–501.
 22
Yu D, Son W, Lim J, Xiao G. Statistical completion of a partially identified graph with applications for the estimation of gene regulatory networks. Biostatistics. 2015; 16(4):670–85.
Acknowledgments
The authors would like to thank Jessie Norris for helping us in the manuscript.
Funding
This work was supported by the National Institutes of Health [1R01CA172211, 5P50CA070907 and 1R01GM115473], the National Research Foundation of Korea [NRF2018R1C1B6001108], and the Cancer Prevention and Research Institute of Texas [RP120732]. The funding bodies had no role in the design, collection, analysis, or interpretation of data in this study.
Availability of data and materials
The adjacency matrices corresponding to the four real proteinprotein interaction networks, and all the simulated datasets generated based on the four real proteinprotein interaction networks used in the simulation study have been deposited in Figshare (https://figshare.com/projects/GeNeCK/36035).
Author information
Affiliations
Contributions
MZ have constructed the web server. QL and MZ have collaborated in the simulation study. DY have contributed to the review of different methods. BY and WG have contributed to network visulization of web server. YX and GX have conceived the study and supervised the web application development and the statistical analyses. All authors have contributed to the writing of the manuscript. All authors have read and approved the final manuscript.
Corresponding author
Correspondence to Guanghua Xiao.
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Additional files
Additional file 1
Figure S9. GeNeCK user guide. A simple tutorial on how to run GeNeCK. (DOCX 195 kb)
Additional file 2
Figure S10. External visulization of GeNeCK inference result. Example of how to import GeNeCK output to Cytoscape for enhanced visulization. (DOCX 326 kb)
Additional file 3
Table S1. Summary of basic information of different methods in GeNeCK. (DOCX 14 kb)
Additional file 4
Figure S1S8. Comparison of model performance of different methods in simulation studies. Network structures are based on real proteinprotein interaction networks. Expression data are simulated under different noise levels. (DOCX 776 kb)
Additional file 5
Table S2. Summary of runtime of different methods in GeNeCK. (DOCX 18 kb)
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
About this article
Cite this article
Zhang, M., Li, Q., Yu, D. et al. GeNeCK: a web server for gene network construction and visualization. BMC Bioinformatics 20, 12 (2019). https://doi.org/10.1186/s1285901825600
Received:
Accepted:
Published:
Keywords
 Gene network
 Gene network
 Statistical method
 Web server
 Correlation
 Likelihood
 Bayesian
 Mutual information
 Ensemble
 Hub gene
 Visualization