Skip to main content
Download PDF

sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters

BMC Bioinformatics volume 18, Article number: 325 (2017) Cite this article

Abstract

Background

sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles.

Results

The purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected.

Conclusions

sgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

Background

Networks provide a statistical and mathematical framework for the general understanding of the complex functioning of biological systems because the causal relationship between different entities, such as proteins, genes or metabolites, defines how a cellular system functions collectively. This leads to an emergent behavior, e.g., with respect to phenotypic aspects of organisms [1–4]. Unfortunately, understanding of the system’s functioning of a cell is not an easy task and one reason for this is that the causal inference of gene network itself is a formidable problem [5, 6]. For this reason, we provide the R package sgnesR (Stochastic Gene Network Expression Simulator in R). Specifically, sgnesR can be used to generate biologically realistic gene expression data based on an underlying gene regulatory network that can be used to test network inference methods qualitatively. In this way an inferred network can be compared with the known true gene regulatory network, which is for most real biological systems unknown requiring the usage of approximations, e.g., by using transcriptional regulatory networks or protein interaction networks [7]. Overall, our package sgnesR enables the quantitative estimation of important statistical measures, e.g., the power, false discovery rate or AUROC values of such inferred networks. Furthermore, the resulting gene expression profiles can be itself of use for instance for comparison with real measurements of gene expression values for the identification of model parameters.

In general, the simulation of biologically realistic gene expression values is a challenging task because it requires the specification of transcription and translation mechanisms of biological cells, which are far from being understood in every detail. Specifically, there are two major components that need to be defined for the simulation of such a process. The first relates to the connection structure among the genes and the second to the parameter values of the modeling equations. The connection structure corresponds to the regulatory network which defines which genes control the expression of other genes. Our package sgnesR allows the usage of previously inferred biological networks or the usage of artificially simulated networks. For the identification of the parameters of the modeling equations of the transcription and translation processes values can be sampled from plausible distributional assumptions.

In the following, we discuss some existing methods that have been proposed and implemented for the simulation of gene expression data. An overview of these simulation methods for which software implementations are available is shown in Table 1. One of the most widely used methods is syntren [8]. Syntren uses an interaction kinetics model based on the equations of Michaelis-Menten and Hill kinetics. In contrast, netsim applies a fuzzy logic for the representation of interactions for a given topology of a gene regulatory network and differential equations to generate expression data [9]. Despite these differences, both simulation methods aim at emulating a biological model of transcription regulation and translation. A completely different approach is used by GeneNet [10]. This method samples network data from a Gaussian graphical model (GGM) for a given network structure. A similar approach is used in [11].

Table 1 A list of network sampling and simulation methods
Full size table

Our R package sgnesR provides an easy-to-use interface to simulate gene expression data generated by the stochastic simulation algorithm (SSA) [12, 13]. That means a gene regulatory network is modeled whose activation patterns are defined by the transcription and translation which are modeled as multiple time delayed events. The delays itself can be drawn from a variety of distributions and the reaction rates can be determined via complex functions or from physical parameters. The original implementation of the ’Stochastic Gene Networks Simulator’ (SGNSim) algorithm [13] is available in C/C++. However, by providing the R interface sgnesR, it is possible to perform all relevant analysis steps, e.g., for testing network inference methods or for investigating pathway methods, within the R environment. This is not only convenient but leads to a natural integration of all parts making the overall analysis reproducible in the most straight forward way [14]. In addition, our package sgnesR allows selection capabilities for various biological and artificially simulated gene regulatory networks that can be used as realistic wiring diagrams for the interactions between genes.

The paper is organized as follows. In the next section we describe our gene expression simulator sgnesR in detail and present some working examples. These examples will demonstrate the capabilities of sgnesR. The paper finishes with a summary and conclusions.

Implementation

In this section, we provide a description of the organizational structure corresponding to the workflow of the sgnesR package and its components. Schematically, the overview of the workflow is shown in Fig. 1. The first step consists in specifying the network topology. Here the user has two choices: A) use an external network or B) generate a simulated network. For B) we are using the igraph package in R. The igraph package provides a comprehensive set of functions that allows to generate or create several types of networks and compute several network related features; for the visualization of networks see [15]. A user can easily generate a network forming the connections for a set of reactions as the input of the SGNS algorithm [13]. Alternatively, a user can select biological networks as input as provided by public databases, e.g., [16, 17]. For convenience, we provide two biological networks in the sgnesR package. The first one is a transcription regulator network of E. coli [18] and the second a subnetwork of the human signaling network [19].

Fig. 1
figure 1

A flow chart of R implemented interface of Stochastic Gene Networks Simulator

Full size image

In addition to the specification of a graph topology, the assignment of initial populations of RNAs and proteins for each node and the activation or suppression indicator for each edge of the network are initialized in the first step of the sgnesR package. In the following, a brief description of the generation of the set of reactions from a network topology is provided.

Suppose, we have a network consisting four genes (nodes) A, B, C and D. Their interactions are described as follows:

In order to represent the following network topology as a set of chemical reactions we assume that each node is represented by a promoter, an RNA and a protein product. For example the node A is represented as ProA (promoter), RA (RNA) and PA (protein produce). In the following example below, A interacts with three nodes so A has three different promoter sites where the protein products of different genes (B, C and D) bind to activate or suppress the expression of A. The set of reactions are divided into three sections as follows:

  1. 1.

    Reactions for translation and degradation for each gene: In this step, three steps of reactions describe the translation of RNAs of each node into the protein products and the respective decay of each RNA and protein product. The example is shown below.

  2. 2.

    Binding-unbinding reactions: This set of reactions describe the binding of protein products of interacting genes to the promoter sites of interacted gene. In the given example, genes B and C activate and gene D suppress the expression of gene A so the protein products of B, C, and D interact with their respective promoter sites ProA.NoB, ProA.NoC and ProA.NoD in gene A and form intermediary products ProA.B, ProA.C and ProA.D. These intermediary products take part in the transcription process of the gene A. The gene D suppresses the expression of gene A, in this process an intermediary product of suppressor gene (ProA.D) is formed by Protein product of D (PD) by binding to the promoter site of the gene A (ProA.NoD). The intermediary product of suppressor gene D (ProA.D) does not allow to express gene A, therefore avoids the transcription process and releases after sometime. The example of binding and the unbinding of proteins to promoters sites is shown below.

  3. 3.

    Transcription reactions: This is a set of reactions of the transcription process of the gene to which all possible combinations of the intermediary products of the activators of the genes contributes to the expression of gene A. In this example, the two activators B and C can have three possible choices to contribute to the expression of A in which the intermediary product of only B, intermediary product of only C and intermediary products of both B and C contribute to the expression of the RNA of gene A. The example reaction is shown below:

These three sets of reactions along with other reaction parameters are passed to the SGNS algorithm to generate the expression profiles for the different genes. The additional reaction parameters needed are the initial population, reaction rates and delay parameters which are described in the following:

  • Initial populations: The initial population of parameters assigns the initial values of promoters, RNAs and proteins for all the genes in the network.

  • Reaction rates: The reaction rate parameter assigns values for reaction-rate to different reaction types for translation and degradation reactions as translation rate, RNA degradation rate and protein degradation rate. For binding and unbinding reactions it assigns binding and unbinding rates and for transcription rates it assigns transcription rate.

  • Delay parameters: The delay parameter assigns a delay time for RNAs and proteins in translation and degradation reactions to be released at a certain time point. Also, the promoter delay is assigned to the products of transcriptions reactions to be released at a certain time point.

The sgnesR package provides two options to obtain the expression profiles of different genes as either time series data or steady-state values. The time series data is a set of expression values of different genes between the different time points of starting time and end time of reactions which are captured at fixed time intervals. The steady state values are final expression values of different genes at the end of the reaction. Furthermore the sgnesR packages allows to repeat the simulation of a input network n times and generates this way an ensemble of steady-state expression values of sample size n.

Results and discussion

In this section, we present some working examples for the usage of our package sgnesR. These examples demonstrate some of the available features of its capabilities. The sgnesR package provides options to apply various parameters using base R functions and a variety of network topologies, based on several network features as parameters for generating simulated data. Further parameters are assigned to each reaction by defining two data objects of the “rsgns.param” and “rsgns.data” class. These are defined as follows.

  • “rsgns.param”: This class defines the initial parameters which include “start time”, “stop time” and “read-out interval” for time series data.

  • “rsgns.data”: The class defines a data object for the input which includes the network topology and other parameters such as the initial populations of RNA and protein molecules of each node/gene, rate constants, delay parameters and initial population parameters of different molecules.

  • “rsgns.waitlist”: This class defines the molecules placed in a waiting list and to be released a specific number of molecules at a particular time during the reaction. This class includes “nodes”, “time”, “mol” and “type” for time series data.

R functions for generating data from a given network

  • getreactions : This function generates an object of class “rsgns.reactions” which contains a set of reactions, their initial values and the wait-list of reactions. This object can be supplied to the SGNS API for generating gene expression data. The “rsgns.reactions” object is a list containing six components which are “population”, “activation”, “binding_unbinding”, “trans_degradation” and “waitlist”. Each component of the list is a matrix object and user can modify those reaction parameters depending on the requirements before passing it to “rsgns.rn” function as an input.

  • rsgns.rn: This function is an interface to the SGNS API for simulating timeseries data. A user can either provide a “rsgns.reactions” class object directly to the function or the “rsgns.data” class object to receive the output. There are further options available to tune the reaction parameters. The function itself returns a “sgnesR” class object which contains the generated expression data, the input network and the reaction kinetics information.

  • plot.sgnesR: This function provides different options to visualize the expression profiles. The function has two major options available. The first one is to visualize the expression values in terms of RNA numbers at different time points and the second option is to visualize the distribution of RNA numbers for different nodes/genes at different time points or the sample-distribution of an ensemble of steady state values.

Generating time series data from a scale-free network

The first example we demonstrate how to use sgnesR package to generate time series data from a scale-free network. The code for this is presented in Example 1. For reasons of simplicity, in this example we do not consider delay parameters for the translation and transcription processes (see Example 2 for an extension). The visualization of the network and the generated expression values are shown in Fig. 2.

Fig. 2
figure 2

A plot of sample network and the expression values at different time points of different nodes from the simulation. a The input network b Expression values of genes which show incoming edges

Full size image

Generating time series data from a scale-free network with delay parameters

In Example 2 we provide a working example to generate time series data from a scale-free network with delay parameters. That means we are assigning delay parameters for the translation reactions of the RNA delay and the protein delay and in transcription reactions for a promoter delay. The user can assign delay parameters chosen from a Gaussian distribution with different mean values and variance. Further choices are delay functions such as a gamma distribution or an exponential function for the delays. However, for simulating real biological gene expression data it is preferable to use the “gamma” function to assign delays [20].

Generating steady-state samples of expression values from an Erdos-Renyi network

Here ’steady-state samples’ means ’asymptotic samples’ in the sense that we run our simulations until the expression values of the genes reach constant values where a further continuation of the simulations lead to no further changes of expression values of the molecules. Example 3 provides a working example to demonstrate the usage of our package. The visualization of the results of the network and the distribution of the ensemble of generated expression profiles is shown in Fig. 3. We want to remark that the ’sample’ option for the function ’rsgns.rn’ means that the simulations are repeated n times, as defined by the value of ’sample=n’, by using the same initial values of all parameters. In case the user wants to use different initial values, then ’sample=1’ needs to be used and an explicit loop over ’rsgns.rn’ needs to be carried out.

Fig. 3
figure 3

A plot of input network and the the distribution of expression values of different samples from the simulation. a The input network b Distribution of expression values of genes for different samples

Full size image

Generating time series data from a known set of equations

In this example we demonstrate how to use sgnesR package to generate time series data from a user defined set of reactions. The code for this is presented in Example 4. This example is based on the toggle switch reactions without cooperative binding. The purpose of this example is to simulate a set of reactions when we know the information of promoter regions along with RNA and protein binding information. Suppose the equations are described as follows:

  1. 1.

    ProA + *Ind –[0.002]– > A + ProA

  2. 2.

    ProB + *Ind –[0.002]– > B + ProB

  3. 3.

    A –[0.005]– >

  4. 4.

    B –[0.005]– >

  5. 5.

    A + ProB + *ProA –[0.2]– > ProB.A

  6. 6.

    B + ProA + *ProB –[0.2]– > ProA.B

  7. 7.

    ProB.A –[0.01]– > ProB + A

  8. 8.

    ProA.B –[0.01]– > ProA + B

  9. 9.

    ProB.A –[0.005]– > ProB

  10. 10.

    ProA.B –[0.005]– > ProA

Application in network inference

In this section, we present two examples to generate expression profiles and the inference of networks from the expression profiles using BC3NET [22]. BC3NET is a network inference method based on the ensemble of inferred networks by assigning an edge for a gene-pair if at least one of these two genes show maximal mutual information with respect to all other genes [23]. For simulation, we chose two types of networks the first one are the scale-free artificial networks with 50 nodes and edges of different edge densities. The second network is a subnetwork of ecoli transcription regulatory network [24] which contains 59 nodes and 60 edges. The subnetwork is shown in Fig. 5(a). The generated expression profiles of ecoli transcription subnetwork are based on hypothetical promoter regions where an RNA molecule of a gene binds to a hypothetical promoter region of another gene if there is an edge exist between them. The other parameters of the reactions are hypothetical assumptions for the reactions. The details of these parameters and generation of expression profiles are provided in the supplementary R file (ecolisim_script.R). In the first step, we generate expression profiles of artificial networks and ecoli subnetwork using sgnesR, in the second step we used expression profile for inferring networks using BC3NET. For all three types of artificial networks, we repeat simulation 20 times. For each simulation step, the mutual information is calculated between all pairs of nodes using BC3NET which assigns weights to all pair of nodes. In this simulation, we highlight the distribution of weights of gene-pairs which are connected by edges and gene-pairs which are not connected with each other (non-edge). The results are shown in Fig. 4. Similarly, we generate expression profiles using the ecoli network and inferred the network using BC3NET. The distribution of weights of gene-pairs which are connected by edges and gene-pairs which are not connected with each other (non-edge) are shown in Fig. 5(b). In these examples, we clearly see that the BC3NET assigns higher weights by computing mutual information of expression profiles to the pairs of nodes for edge components compare to the non-edge components of simulated networks and ecoli subnetwork. Similarly, the other measures can be used to evaluate the performance of different network inference methods.

Fig. 4
figure 4

The distribution of edge-weights of gene-pairs of non-edge components and edge components of inferred networks using BC3NET from the simulated expression profiles of artificial networks generated by sgnesR. In (a), (b) and (c) example networks are shown that have a different number of edges

Full size image
Fig. 5
figure 5

a A subnetwork of transcription regulatory network of ecoli used to simulate expression profiles using sgnesR. b The distribution of edge-weights of gene-pairs of non-edge components and edge components of inferred network using BC3NET from the expression profiles of ecoli subnetwork generated by sgnesR

Full size image

Computational complexity

Overall, the computational complexity of the algorithm depends on the edge density of the used network and specifically on the in-degree of each node. However, for networks with up to ∼1000 genes the package generates rapid results. A practical overview of the run time of our sgnesR package is shown in Table 2. The average run time is shown in seconds for different network sizes. We repeated the analysis 10 times for each network size shown in the table.

Table 2 Estimated time by sgnesR, in seconds for different type of networks
Full size table

We would like to remark that the theoretical computational complexity of the implementation of the SGNS algorithm has a formal time complexity of O(T R∗(D logR+ logW)). Where T = simulation time, R = number of reactions, D = max degree in propensity update dependency graph between reactions, W = max wait list size. However, our sgnesR package contains an additional layer of complexity consisting of the automatic generation of all reaction equations for a given network topology.

Conclusions

In this paper, we described the R implementation of the sgnesR (Stochastic Gene Network Expression Simulator) package. The main objective of the sgnesR package is to utilize the applicability of gene expression simulations, e.g., for validating the performance of network inference methods [5, 6]. The sgnesR package allows an easy-to-use interface for the simulation of gene expression profiles from a given network structure. A user can easily either utilize a given biological network or generate a topological structure of different network types for which reaction parameters are specified in correspondence to given constraints. In our package the reaction parameters can be modeled and used in a very flexible manner, e.g., with respect to the underlying parameter distributions. The resulting gene expression data can be either obtained as time series data for user defined sampling time steps or as steady-steady data.

Availability and requirements

Project name: sgnesR

Project home page: “Package is currently available on: https://github.com/shaileshtripathi/sgnesR”

Operating system(s): Windows, Linux, OS X

Programming language: R, C

License: Free

Abbreviations

AUROC:

Area under receiver operator characteristics (ROC) curve

sgnesR:

Stochastic gene network expression simulator in R

SSA:

Stochastic simulation algorithm

References

  1. Kauffman SA. The origins of order: Self-organization and selection in evolution. Underst Origs. 1992; 65:153–81.

    Article  Google Scholar 

  2. Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009; 461:218–23.

    CAS  Article  PubMed  Google Scholar 

  3. Emmert-Streib F, Glazko GV. Network Biology: A direct approach to study biological function. Wiley Interdiscip Rev Syst Biol Med. 2011; 3(4):379–91.

    CAS  Article  PubMed  Google Scholar 

  4. Vidal M. A unifying view of 21st century systems biology. FEBS Lett. 2009; 583(24):3891–4.

    CAS  Article  PubMed  Google Scholar 

  5. Emmert-Streib F, Glazko GV, Altay G, de Matos Simoes R. Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Front Genet. 2012; 3:8.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Markowetz F, Spang R. Inferring cellular networks–a review. BMC Bioinforma. 2007; 8:5.

    Article  Google Scholar 

  7. de Matos Simoes R, Dehmer M, Emmert-Streib F. B-cell lymphoma gene regulatory networks: Biological consistency among inference methods. Front Genet. 2013; 4:281.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Van den Bulcke T, Van Leemput K, Naudts B, van Remortel P, Ma H, Verschoren A, De Moor B, Marchal K. Syntren: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinforma. 2006; 7(1):43. doi:10.1186/1471-2105-7-43.

    Article  Google Scholar 

  9. Di Camillo B, Toffolo G, Cobelli C. A gene network simulator to assess reverse engineering algorithms. Ann N Y Acad Sci. 2009; 1158(1):125–42. doi:10.1111/j.1749-6632.2008.03756.x.

    Article  PubMed  Google Scholar 

  10. Castelo R, Roverato A. Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J Comput Biol. 2009; 16(2):213–7.

    CAS  Article  PubMed  Google Scholar 

  11. Opgen-Rhein R, Strimmer K. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol. 2007; 1(1):37. doi:10.1186/1752-0509-1-37.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ribeiro AS, Zhu R, Kauffman SA. A general modeling strategy for gene regulatory networks with stochastic dynamics. J Comput Biol. 2006; 13(9):1630–9.

    CAS  Article  PubMed  Google Scholar 

  13. Ribeiro AS, Lloyd-Price J. Sgn sim, a stochastic genetic networks simulator. Bioinformatics. 2007; 23(6):777.

    CAS  Article  PubMed  Google Scholar 

  14. Peng RD. Reproducible research in computational science. Science. 2011; 334(6060):1226–7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Tripathi S, Dehmer M, Emmert-Streib F. NetBioV: An R package for visualizing large network data in biology and medicine. Bioinformatics. 2014; 30(19):2834–6.

    CAS  Article  PubMed  Google Scholar 

  16. Breitkreutz BJ, Stark C, Reguly T, Boucher L, Breitkreutz A, Livstone M, Oughtred R, Lackner DH, Bähler J, Wood V, Dolinski K, Tyers M. The BioGRID Interaction Database: 2008 update. Nucl Acids Res. 2008; 36(suppl_1):D637–40.

    CAS  PubMed  Google Scholar 

  17. Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A, Derow C, Feuermann M, Ghanbarian AT, Kerrien S, Khadake J, Kerssemakers J, Leroy C, Menden M, Michaut M, Montecchi-Palazzi L, Neuhauser SN, Orchard S, Perreau V, Roechert B, van Eijk K, Hermjakob H. The IntAct molecular interaction database in 2010. Nucl Acids Res. 2010; 38(suppl_1):D525–31.

    CAS  Article  PubMed  Google Scholar 

  18. Salgado H, Peralta-Gil M, Gama-Castro S, Santos-Zavaleta A, Muñiz-Rascado L, García-Sotelo JS, Weiss V, Solano-Lira H, Martínez-Flores I, Medina-Rivera A, Salgado-Osorio G, Alquicira-Hernández S, Alquicira-Hernández K, López-Fuentes A, Porrón-Sotelo L, Huerta AM, Bonavides-Martínez C, Balderas-Martínez YI, Pannier L, Olvera M, Labastida A, Jiménez-Jacinto V, Vega-Alvarado L, del Moral-Chávez V, Hernández-Alvarez A, Morett E, Collado-Vides J. Regulondb v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more. Nucleic Acids Res. 2013; 41(D1):203–13. doi:10.1093/nar/gks1201.

    Article  Google Scholar 

  19. Wang E. Cancer systems biology. Chapman & Hall/CRC Mathematical and Computational Biology. 2010.

  20. Gibson MA, Bruck J. Efficient exact stochastic simulation of chemical systems with many species and many channels. J Phys Chem A. 2000; 104(9):1876–89. doi:10.1021/jp993732q.

    CAS  Article  Google Scholar 

  21. Barabási AL, Albert R. Emergence of scaling in random networks. Science. 1999; 206:509–12.

    Google Scholar 

  22. de Matos Simoes R, Emmert-Streib F. Bagging statistical network inference from large-scale gene expression data. PLOS ONE. 2012; 7(3):1–11. doi:10.1371/journal.pone.0033624.

    Article  Google Scholar 

  23. Altay G, Emmert-Streib F. Inferring the conservative causal core of gene regulatory networks. BMC Syst Biol. 2010; 4(1):132. doi:10.1186/1752-0509-4-132.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Gama-Castro S, Salgado H, Santos-Zavaleta A, Ledezma-Tejeida D, Muniz-Rascado L, García-Sotelo JS, Alquicira-Hernández K, Martínez-Flores I, Pannier L, Castro-Mondragón JA, Medina-Rivera A, Solano-Lira H, Bonavides-Martínez C, Pérez-Rueda E, Alquicira-Hernández S, Porrón-Sotelo L, López-Fuentes A, Hernández-Koutoucheva A, Moral-Chávez VD, Rinaldi F, Collado-Vides J. Regulondb version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond. Nucleic Acids Res. 2016; 44(D1):133. doi:10.1093/nar/gkv1156.

    Article  Google Scholar 

  25. Mendes P, Sha W, Ye K. Artificial gene networks for objective comparison of analysis algorithms. Bioinformatics. 2003; 19:122–9.

    Article  Google Scholar 

  26. Hache H, Wierling C, Lehrach H, Herwig R. Genge: systematic generation of gene regulatory networks. Bioinformatics. 2009; 25(9):1205–7. doi:10.1093/bioinformatics/btp115. http://bioinformatics.oxfordjournals.org/content/25/9/1205.full.pdf+html.

  27. Haynes BC BM. Benchmarking regulatory network reconstruction with grendel. Bioinformatics. 2009; 25(6):801–7.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Roy S, Werner-Washburne M, Lane T. A system for generating transcription regulatory networks with combinatorial control of transcription. Bioinformatics. 2008; 24(10):1318–20. doi:10.1093/bioinformatics/btn126. http://bioinformatics.oxfordjournals.org/content/24/10/1318.full.pdf+html.

Download references

Acknowledgement

Matthias Dehmer thanks the Austrian Science Funds for supporting this work (project P26142).

Funding

Source of funding is not available.

Availability of data and materials

Not applicable.

Authors’ contributions

FES, OYH, MD, ST conceived and designed the analysis. ST, FES implemented the algorithms, and analyzed the data. FES, JLP, AR, OYH, MD, ST wrote the paper. All authors approved the final version.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Authors and Affiliations

  1. Predictive Medicine and Data Analytics Lab, Department of Signal Processing, Tampere University of Technology, Tampere, Finland

    Shailesh Tripathi & Frank Emmert-Streib

  2. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, USA

    Jason Lloyd-Price

  3. Laboratory of Biosystem Dynamics, Department of Signal Processing, Tampere University of Technology, Tampere, Finland

    Jason Lloyd-Price & Andre Ribeiro

  4. Institute for Theoretical Informatics, Mathematics and Operations Research, Department of Computer Science, Universität der Bundeswehr München, Munich, Germany

    Matthias Dehmer

  5. Institute of Biosciences and Medical Technology, Tampere, Finland

    Andre Ribeiro, Olli Yli-Harja & Frank Emmert-Streib

  6. Computational Systems Biology, Department of Signal Processing, Tampere University of Technology, Tampere, Finland

    Olli Yli-Harja

Authors
  1. Shailesh Tripathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jason Lloyd-Price
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andre Ribeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olli Yli-Harja
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthias Dehmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Frank Emmert-Streib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frank Emmert-Streib.

Additional file

Additional file 1

ecolisim_script.R, example R script to simulate expression profiles and the inference of network from the simulated profiles using BC3NET. (R 2 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.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tripathi, S., Lloyd-Price, J., Ribeiro, A. et al. sgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters. BMC Bioinformatics 18, 325 (2017). https://doi.org/10.1186/s12859-017-1731-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12859-017-1731-8

Keywords

  • Gene expression data
  • Gene network
  • Simulation

BMC Bioinformatics

ISSN: 1471-2105

Contact us