AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (577.7 KB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

A Novel Method of Gene Regulatory Network Structure Inference from Gene Knock-Out Expression Data

Xiang Chen, Min Li( ), Ruiqing Zheng, Siyu Zhao, Fang-Xiang Wu, Yaohang Li, Jianxin Wang
School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
Department of Computer Science, Old Dominion University, Norfolk, VA 23529, USA.
Show Author Information

Abstract

Inferring Gene Regulatory Networks (GRNs) structure from gene expression data has been a challenging problem in systems biology. It is critical to identify complicated regulatory relationships among genes for understanding regulatory mechanisms in cells. Various methods based on information theory have been developed to infer GRNs. However, these methods introduce many redundant regulatory relationships in the network inference process due to external noise in the original data, topology sparseness in the network structure, and non-linear dependency among genes. Especially as the network size increases, the performance of these methods decreases dramatically. In this paper, a novel network structure inference method named Loc-PCA-CMI is proposed that first identifies local overlapped gene clusters, and then infers the local network structure for each cluster by a Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). The final structure of the GRN is denoted as dependence among genes by an ensemble of the obtained local network structures. Loc-PCA-CMI was evaluated on DREAM3 knock-out datasets, and its performance was compared to other information theory-based network inference methods including ARACNE, MRNET, PCA-CMI, and PCA-PMI. Experimental results demonstrate our novel method Loc-PCA-CMI outperforms the other four methods in DREAM3 datasets especially in size 50 and 100 networks.

Keywords

gene regulatory networksnetwork inferencepath consistency algorithm

References

[1]
G. Altay and F. Emmert-Streib, Inferring the conservative causal core of gene regulatory networks, BMC Systems Biology, vol. 4, no. 1, p. 132, 2010.
Crossref Google Scholar
[2]
K., Basso A. A., Margolin G., Stolovitzky U., Klein R. Dalla-Favera, and A. Califano, Reverse engineering of regulatory networks in human b cells, Nature Genetics, vol. 37, no. 4, p. 382, 2005.
Crossref Google Scholar
[3]
L., Elnitski V. X., Jin P. J. Farnham, and S. J. Jones, Locating mammalian transcription factor binding sites: A survey of computational and experimental techniques, Genome Research, vol. 16, no. 12, pp. 1455-1464, 2006.
Crossref Google Scholar
[4]
T. R., Hughes M. J., Marton A. R., Jones C. J., Roberts R., Stoughton C. D., Armour H. A., Bennett E., Coffey H., Dai Y. D., He et al., Functional discovery via a compendium of expression profiles, Cell, vol. 102, no. 1, pp. 109-126, 2000.
Crossref Google Scholar
[5]
S. R., Maetschke P. B., Madhamshettiwar M. J. Davis, and M. A. Ragan, Supervised, semi-supervised and unsupervised inference of gene regulatory networks, Briefings in Bioinformatics, vol. 15, no. 2, pp. 195-211, 2013.
Crossref Google Scholar
[6]
A. A., Margolin K., Wang W. K., Lim M., Kustagi I. Nemenman, and A. Califano, Reverse engineering cellular networks, Nature Protocols, vol. 1, no. 2, p. 662, 2006.
Crossref Google Scholar
[7]
V. A., Huynh-Thu A., Irrthum L. Wehenkel, and P. Geurts, Inferring regulatory networks from expression data using tree-based methods, PLoS One, vol. 5, no. 9, pp. 1-10, 2010.
Crossref Google Scholar
[8]
A.-C., Haury F., Mordelet P. Vera-Licona, and J.-P. Vert, TIGRESS: Trustful Inference of Gene REgulation using Stability Selection, BMC Syst. Biol., vol. 6, no. 1, p. 145, 2012.
Crossref Google Scholar
[9]
V. A., Huynh-Thu G., Sanguinetti A. Huynh-thu, and T. Jump, Combining tree-based and dynamical systems for the inference of gene regulatory networks, Bioinformatics, vol. 31, no. 10, pp. 1614-1622, 2014.
Crossref Google Scholar
[10]
L.-Z., Liu F.-X. Wu, and W.-J. Zhang, A group lasso-based method for robustly inferring gene regulatory networks from multiple time-course datasets, BMC Systems Biology, vol. 8, no. S3, p. S1, 2014.
Crossref Google Scholar
[11]
M., Li R., Zheng Y., Li F.-X. Wu, and J. Wang, Mgt-sm: A method for constructing cellular signal transduction networks, IEEE/ACM Transactions on Computational Biology and Bioinformatics, .
Crossref Google Scholar
[12]
R., Zheng M., Li X., Chen F.-X., Wu Y. Pan, and J. Wang, Bixgboost: A scalable, flexible boosting-based method for reconstructing gene regulatory networks, Bioinformatics, .
Crossref Google Scholar
[13]
E. Sakamoto and H. Iba, Inferring a system of differential equations for a gene regulatory network by using genetic programming, in Proceedings of the 2001 Congress on Evolutionary Computation, 2001, vol. 1, pp. 720-726.
[14]
A. R., Chowdhury M. Chetty, and R. Evans, Stochastic s-system modeling of gene regulatory network, Cognitive Neurodynamics, vol. 9, no. 5, pp. 535-547, 2015.
Crossref Google Scholar
[15]
Z., Li P., Li A. Krishnan, and J. Liu, Large-scale dynamic gene regulatory network inference combining differential equation models with local dynamic bayesian network analysis, Bioinformatics, vol. 27, no. 19, pp. 2686-2691, 2011.
Crossref Google Scholar
[16]
K. Murphy and S. Mian, Modelling gene expression data using dynamic Bayesian networks, Technical report, Computer Science Division, University of California, Berkeley, CA, USA, 1999.
[17]
M. Zou and S. D. Conzen, A new Dynamic Bayesian Network (DBN) approach for identifying gene regulatory networks from time course microarray data, Bioinformatics, vol. 21, no. 1, pp. 71-79, 2004.
Crossref Google Scholar
[18]
N. X., Vinh M., Chetty R. Coppel, and P. P. Wangikar, Globalmit: Learning globally optimal dynamic Bayesian network with the mutual information test criterion, Bioinformatics, vol. 27, no. 19, pp. 2765-2766, 2011.
Crossref Google Scholar
[19]
W. C., Young A. E. Raftery, and K. Y. Yeung, Fast Bayesian inference for gene regulatory networks using scanbma, BMC Systems Biology, vol. 8, no. 1, p. 47, 2014.
Crossref Google Scholar
[20]
F., Liu S.-W., Zhang W.-F., Guo Z.-G. Wei, and L. Chen, Inference of gene regulatory network based on local Bayesian networks, PLOS Comput. Biol., vol. 12, no. 8, p. e1005024, 2016.
Crossref Google Scholar
[21]
N., Omranian J. M., Eloundou-Mbebi B. Mueller-Roeber, and Z. Nikoloski, Gene regulatory network inference using fused lasso on multiple data sets, Scientific Reports, vol. 6, p. 20533, 2016.
Crossref Google Scholar
[22]
F.-X., Wu W.-J. Zhang, and A. J. Kusalik, Modeling gene expression from microarray expression data with state-space equations, in Biocomputing 2004. World Scientific, 2003, pp. 581-592.
Crossref
[23]
M., Quach N. Brunel, and F. d’Alché Buc, Estimating parameters and hidden variables in non-linear state-space models based on odes for biological networks inference, Bioinformatics, vol. 23, no. 23, pp. 3209-3216, 2007.
Crossref Google Scholar
[24]
Y., Wang T., Joshi X.-S., Zhang D. Xu, and L. Chen, Inferring gene regulatory networks from multiple microarray datasets, Bioinformatics, vol. 22, no. 19, pp. 2413-2420, 2006.
Crossref Google Scholar
[25]
V. A., Huynh-Thu A., Irrthum L. Wehenkel, and P. Geurts, Inferring regulatory networks from expression data using tree-based methods, PloS One, vol. 5, no. 9, p. e12776, 2010.
Crossref Google Scholar
[26]
W. J., Longabaugh E. H. Davidson, and H. Bolouri, Computational representation of developmental genetic regulatory networks, Developmental Biology, vol. 283, no. 1, pp. 1-16, 2005.
Crossref Google Scholar
[27]
G. Karlebach and R. Shamir, Modelling and analysis of gene regulatory networks, Nature Reviews—Molecular Cell Biology, vol. 9, no. 10, p. 770, 2008.
Crossref Google Scholar
[28]
I., Shmulevich E. R., Dougherty S. Kim, and W. Zhang, Probabilistic boolean networks: A rule-based uncertainty model for gene regulatory networks, Bioinformatics, vol. 18, no. 2, pp. 261-274, 2002.
Crossref Google Scholar
[29]
H., Kim J. K. Lee, and T. Park, Boolean networks using the chi-square test for inferring large-scale gene regulatory networks, BMC Bioinformatics, vol. 8, no. 1, p. 37, 2007.
Crossref Google Scholar
[30]
S. Bornholdt, Boolean network models of cellular regulation: Prospects and limitations, Journal of the Royal Society Interface, vol. 5, no. Suppl 1, pp. S85-S94, 2008.
Crossref Google Scholar
[31]
J. X., Zhou A., Samal A. F., d’Hérouël N. D. Price, and S. Huang, Relative stability of network states in boolean network models of gene regulation in development, Biosystems, vol. 142, pp. 15-24, 2016.
Crossref Google Scholar
[32]
S. Y., Kim S. Imoto, and S. Miyano, Inferring gene networks from time series microarray data using dynamic bayesian networks, Briefings in Bioinformatics, vol. 4, no. 3, pp. 228-235, 2003.
Crossref Google Scholar
[33]
X.-W., Chen G. Anantha, and X. Wang, An effective structure learning method for constructing gene networks, Bioinformatics, vol. 22, no. 11, pp. 1367-1374, 2006.
Crossref Google Scholar
[34]
C. J., Needham J. R., Bradford A. J. Bulpitt, and D. R. Westhead, A primer on learning in Bayesian networks for computational biology, PLoS Computational Biology, vol. 3, no. 8, p. e129, 2007.
Crossref Google Scholar
[35]
L.-Y., Lo M.-L., Wong K.-H. Lee, and K.-S. Leung, High-order dynamic Bayesian network learning with hidden common causes for causal gene regulatory network, BMC Bioinformatics, vol. 16, no. 1, p. 395, 2015.
Crossref Google Scholar
[36]
T. S., Gardner D. Di, Bernardo D. Lorenz, and J. J. Collins, Inferring genetic networks and identifying compound mode of action via expression profiling, Science, vol. 301, no. 5629, pp. 102-105, 2003.
Crossref Google Scholar
[37]
D. di, Bernardo M. J., Thompson T. S., Gardner S. E., Chobot E. L., Eastwood A. P., Wojtovich S. J., Elliott S. E. Schaus, and J. J. Collins, Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks, Nature Biotechnology, vol. 23, no. 3, pp. 377-383, 2005.
Crossref Google Scholar
[38]
M., Bansal G. D. Gatta, and D. Di Bernardo, Inference of gene regulatory networks and compound mode of action from time course gene expression profiles, Bioinformatics, vol. 22, no. 7, pp. 815-822, 2006.
Crossref Google Scholar
[39]
A., Honkela C., Girardot E. H., Gustafson Y.-H., Liu E. E., Furlong N. D. Lawrence, and M. Rattray, Model-based method for transcription factor target identification with limited data, Proceedings of the National Academy of Sciences, vol. 107, no. 17, pp. 7793-7798, 2010.
[40]
T., Lu H., Liang H. Li, and H. Wu, High-dimensional odes coupled with mixed-effects modeling techniques for dynamic gene regulatory network identification, Journal of the American Statistical Association, vol. 106, no. 496, pp. 1242-1258, 2011.
Crossref Google Scholar
[41]
W.-P. Lee and W.-S. Tzou, Computational methods for discovering gene networks from expression data, Briefings in Bioinformatics, vol. 10, no. 4, pp. 408-423, 2009.
Crossref Google Scholar
[42]
D. M., Chickering D. Heckerman, and C. Meek, Large-sample learning of Bayesian networks is np-hard, Journal of Machine Learning Research, vol. 5, pp. 1287-1330, 2004.
Google Scholar
[43]
M., Hecker S., Lambeck S., Toepfer E. Van Someren, and R. Guthke, Gene regulatory network inference: Data integration in dynamic models—A review, Biosystems, vol. 96, no. 1, pp. 86-103, 2009.
Crossref Google Scholar
[44]
D., Marbach J. C., Costello R., Küffner N. M., Vega R. J., Prill D. M., Camacho K. R., Allison M., Kellis J. J., Collins G., Stolovitzky et al., Wisdom of crowds for robust gene network inference, Nature Methods, vol. 9, no. 8, pp. 796-804, 2012.
Crossref Google Scholar
[45]
F.-X. Wu, Inference of gene regulatory networks and its validation, Current Bioinformatics, vol. 2, no. 2, pp. 139-144, 2007.
Crossref Google Scholar
[46]
L.-Z., Liu F.-X. Wu, and W.-J. Zhang, Reverse engineering of gene regulatory networks from biological data, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 2, no. 5, pp. 365-385, 2012.
Crossref Google Scholar
[47]
M., Li H., Gao J. Wang, and F.-X. Wu, Control principles for complex biological networksli control principles for biological networks, Briefings in Bioinformatics, .
Crossref Google Scholar
[48]
Y. R. Wang and H. Huang, Review on statistical methods for gene network reconstruction using expression data, Journal of Theoretical Biology, vol. 362, pp. 53-61, 2014.
Crossref Google Scholar
[49]
J., Ruyssinck P., Geurts T., Dhaene P. Demeester, and Y. Saeys, Nimefi: Gene regulatory network inference using multiple ensemble feature importance algorithms, PLoS One, vol. 9, no. 3, p. e92709, 2014.
Crossref Google Scholar
[50]
H., Brunel J.-J., Gallardo-Chacón A., Buil M., Vallverdú J. M., Soria P. Caminal, and A. Perera, Miss: A non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis, Bioinformatics, vol. 26, no. 15, pp. 1811-1818, 2010.
Crossref Google Scholar
[51]
X., Zhang X.-M., Zhao K., He L., Lu Y., Cao J., Liu J.-K., Hao Z.-P. Liu, and L. Chen, Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information, Bioinformatics, vol. 28, no. 1, pp. 98-104, 2011.
Crossref Google Scholar
[52]
D., Marbach R. J., Prill T., Schaffter C., Mattiussi D. Floreano, and G. Stolovitzky, Revealing strengths and weaknesses of methods for gene network inference, Proceedings of the National Academy of Sciences, vol. 107, no. 14, pp. 6286-6291, 2010.
[53]
A. A., Margolin I., Nemenman K., Basso C., Wiggins G., Stolovitzky R. Dalla Favera, and A. Califano, Aracne: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context, BMC Bioinformatics, vol. 7, no. 1, p. S7, 2006.
Crossref Google Scholar
[54]
P. E., Meyer K., Kontos F. Lafitte, and G. Bontempi, Information-theoretic inference of large transcriptional regulatory networks, EURASIP Journal on Bioinformatics and Systems Biology, vol. 2007, no. 1, p. 79879, 2007.
Crossref Google Scholar
[55]
H., Peng F. Long, and C. Ding, Feature selection based on mutual information criteria of max-dependency, max- relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226-1238, 2005.
Crossref Google Scholar
[56]
J., Zhao Y., Zhou X. Zhang, and L. Chen, Part mutual information for quantifying direct associations in networks, Proceedings of the National Academy of Sciences, vol. 113, no. 18, pp. 5130-5135, 2016.
[57]
H., Jeong B., Tombor R., Albert Z. N. Oltvai, and A.-L. Barabási, The large-scale organization of metabolic networks, Nature, vol. 407, no. 6804, pp. 651-654, 2000.
Crossref Google Scholar
[58]
P., Spirtes C. N. Glymour, and R. Scheines, Causation, Prediction, and Search. MIT Press, 2000.
Crossref
[59]
T., Schaffter D. Marbach, and D. Floreano, GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods, Bioinformatics, vol. 27, no. 16, pp. 2263-2270, 2011.
Crossref Google Scholar
[60]
T. Saito and M. Rehmsmeier, The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets, PloS One, vol. 10, no. 3, p. e0118432, 2015.
Crossref Google Scholar
[61]
P. E., Meyer F. Lafitte, and G. Bontempi, minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information, BMC Bioinformatics, vol. 9, no. 1, p. 461, 2008.
Crossref Google Scholar
[62]
C., Olsen P. E. Meyer, and G. Bontempi, On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information, EURASIP Journal on Bioinformatics and Systems Biology, vol. 2009, no. 1, p. 308959, 2008.
Crossref Google Scholar
[63]
P., Meyer D., Marbach S. Roy, and M. Kellis, Information-theoretic inference of gene networks using backward elimination, in BioComp, 2010, pp. 700-705.
[64]
M., Li X., Meng R., Zheng F.-X., Wu Y., Li Y. Pan, and J. Wang, Identification of protein complexes by using a spatial and temporal active protein interaction network, IEEE/ACM Transactions on Computational Biology and Bioinformatics, .
Crossref Google Scholar
[65]
M., Li J., Yang F.-X., Wu Y. Pan, and J. Wang, Dynetviewer: A cytoscape app for dynamic network construction, analysis and visualization, Bioinformatics, vol. 34, no. 9, pp. 1597-1599, 2017.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 24 Issue 4,
August 2019
Pages 446-455
DOI: 10.26599/TST.2018.9010097
Cite this article:
Chen X, Li M, Zheng R, et al. A Novel Method of Gene Regulatory Network Structure Inference from Gene Knock-Out Expression Data. Tsinghua Science and Technology, 2019, 24(4): 446-455. https://doi.org/10.26599/TST.2018.9010097

640

Views

23

Downloads

17

Crossref

N/A

Web of Science

22

Scopus

2

CSCD

Altmetrics
Received: 05 April 2018
Accepted: 01 May 2018
Published: 07 March 2019
© The author(s) 2019
Return