Discovering study-specific gene regulatory networks
Open accessAbstract
Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets.
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Biclustering does not necessarily perform better than other network analysis methods such as WGCNA, as shown by a comparison using different tools on simulated data.
“Although biclustering is a powerful approach, it does not necessarily perform better than other network analysis methods such as WGCNA, as shown by a comparison using different tools on simulated data [144].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [144]
The Biclustering section reports that Questmotif biclustering performed worse than the authors’ pipeline on simulated data. It does not establish that biclustering performed worse than WGCNA: the WGCNA comparisons in the Wheat and Fusarium sections are against glasso, not biclustering. (LLM, conf=0.88)
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Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135 · see citing works
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,182 · see citing works
Algorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523 · see citing works
Sparse inverse covariance estimation with the graphical lasso
Friedman, J., Hastie, T., & Tibshirani, R. (2008) · Biostatistics · doi:10.1093/biostatistics/kxm045 · Cited by 6,355 · see citing works
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684 · see citing works
Erdős, P., & Rényi, A. (2022) · Publicationes Mathematicae Debrecen · doi:10.5486/pmd.1959.6.3-4.12 · Cited by 5,337 · see citing works
The National Center for Biotechnology Information
Benson, D. A., Boguski, M. S., Lipman, D. J., & Ostell, J. (1990) · Genomics · doi:10.1016/0888-7543(90)90583-g · Cited by 5,109 · see citing works
Summaries of Affymetrix GeneChip probe level data
Irizarry, R. A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gng015 · Cited by 4,906 · see citing works
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
Heckerman, D. E., Geiger, D., & Chickering, D. M. (1995) · Machine Learning · doi:10.1007/bf00994016 · Cited by 4,080 · see citing works
Thimm, O., Bläsing, O. E., Gibon, Y., Nagel, A., Meyer, S., Krüger, P., Selbig, J., Müller, L. A., Rhee, S. Y., & Stitt, M. (2004) · The Plant Journal · doi:10.1111/j.1365-313x.2004.02016.x · Cited by 3,356 · see citing works
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
Heckerman, D. E., Geiger, D., & Chickering, D. M. (1995) · Machine Learning · doi:10.1023/a:1022623210503 · Cited by 3,251 · see citing works
Using Bayesian Networks to Analyze Expression Data
Friedman, N., Michal Linial, B., Nachman, I., & Pe’er, D. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050961 · Cited by 3,248 · see citing works
National Center for Biotechnology Information
Gressner, A. M., & Arndt, T. (2019) · Springer Reference Medizin · doi:10.1007/978-3-662-48986-4_301184 · Cited by 2,610 · see citing works
High dimensional graphs and variable selection with the Lasso
Meinshausen, N., & Bühlmann, P. (2006) · Cited by 2,539 · see citing works
High-dimensional graphs and variable selection with the Lasso
Meinshausen, N., & Bühlmann, P. (2006) · The Annals of Statistics · doi:10.1214/009053606000000281 · Cited by 2,521 · see citing works
Biclustering of expression data.
Cheng, Y., & Church, G. M. (2000) · PubMed · Cited by 2,098 · see citing works
Biclustering algorithms for biological data analysis: a survey
Madeira, S. C., & Oliveira, A. L. (2004) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2004.2 · Cited by 2,049 · see citing works
Learning Bayesian Networks with the bnlearn R Package
Scutari, M. (2010) · Journal of Statistical Software · doi:10.18637/jss.v035.i03 · Cited by 1,798 · see citing works
Segal, E., Shapira, M., Regev, A., Pe’er, D., Botstein, D., Koller, D., & Friedman, N. (2003) · Nature Genetics · doi:10.1038/ng1165 · Cited by 1,686 · see citing works
Fast R Functions for Robust Correlations and Hierarchical Clustering
Langfelder, P., & Horvath, S. (2012) · Journal of Statistical Software · doi:10.18637/jss.v046.i11 · Cited by 1,297 · see citing works
ArrayExpress--a public database of microarray experiments and gene expression profiles
Parkinson, H., Kapushesky, M., Shojatalab, M., Abeygunawardena, N., Coulson, R., Farne, A., Holloway, E., Kolesnykov, N., Lilja, P., Lukk, M., et al. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl995 · Cited by 754 · see citing works
Beinlich, I. A., Suermondt, H. J., Chavez, R. M., et al. (1989) · Lecture notes in medical informatics · doi:10.1007/978-3-642-93437-7_28 · Cited by 736 · see citing works
Spectral Biclustering of Microarray Data: Coclustering Genes and ConditionsPreprint
Kluger, Y., Basri, R., Chang, J. T., & Gerstein, M. (2003) · Genome Research · doi:10.1101/gr.648603 · Cited by 726 · see citing works
Mochizuki, N., Brusslan, J. A., Larkin, R. M., Nagatani, A., & Chory, J. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.4.2053 · Cited by 651 · see citing works
Combining multiple microarray studies and modeling interstudy variation
Choi, J. K., Yu, U.-S., Kim, S. S., & Yoo, O. J. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg1010 · Cited by 415 · see citing works
EXTRACTING CONSERVED GENE EXPRESSION MOTIFS FROM GENE EXPRESSION DATA
Murali, T. M., & Kasif, S. (2002) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789812776303_0008 · Cited by 385 · see citing works
Adaptive Probabilistic Networks with Hidden Variables
Binder, J., Koller, D., Russell, S., et al. (1997) · Machine Learning · doi:10.1023/a:1007421730016 · Cited by 382 · see citing works
Samac, D. A., Hironaka, C. M., Yallaly, P. E., et al. (1990) · PLANT PHYSIOLOGY · doi:10.1104/pp.93.3.907 · Cited by 304 · see citing works
PLEXdb: gene expression resources for plants and plant pathogens
Dash, S., Hemert, J. V., Hong, L., Wise, R. P., & Dickerson, J. A. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr938 · Cited by 258 · see citing works
Bayesian correlated clustering to integrate multiple datasets
Kirk, P., Griffin, J. E., Savage, R. S., Zoubin Ghahramani, A., & Wild, D. L. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts595 · Cited by 228 · see citing works
Jensen, P. E., Gilpin, M., Knoetzel, J., et al. (2000) · Journal of Biological Chemistry · doi:10.1074/jbc.m000550200 · Cited by 164 · see citing works
Damkjær, J. T., Kereı̈che, S., Johnson, M. P., et al. (2009) · The Plant Cell · doi:10.1105/tpc.108.064006 · Cited by 152 · see citing works
Consensus clustering and functional interpretation of gene-expression data
Swift, S., Tucker, A., Vinciotti, V., Martin, N., Orengo, C. A., Liu, X., & Kellam, P. (2004) · Genome biology · doi:10.1186/gb-2004-5-11-r94 · Cited by 148 · see citing works
Self-citationObayashi, T., Nishida, K., Kasahara, K., et al. (2011) · Plant and Cell Physiology · doi:10.1093/pcp/pcq203 · Cited by 146 · see citing works
Llorente, F. R., López-Cobollo, R., Catalá, R., Martínez-Zapater, J. M., & Salinas, J. (2002) · The Plant Journal · doi:10.1046/j.1365-313x.2002.01398.x · Cited by 145 · see citing works
Graphical Independence Networks with thegRainPackage forR
jsgaard, S. H. (2012) · Journal of Statistical Software · doi:10.18637/jss.v046.i10 · Cited by 144 · see citing works
Light-harvesting Complex II Binds to Several Small Subunits of Photosystem I
Zhang, S., & Scheller, H. V. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m311640200 · Cited by 127 · see citing works
Wientjes, E., & Croce, R. (2010) · Biochemical Journal · doi:10.1042/bj20101538 · Cited by 124 · see citing works
Andersson, U., Heddad, M., & Adamska, I. (2003) · PLANT PHYSIOLOGY · doi:10.1104/pp.102.019281 · Cited by 119 · see citing works
Suppression of reactive oxygen species by glyceraldehyde-3-phosphate dehydrogenase
Baek, D., Jin, Y., Jeong, J. C., et al. (2007) · Phytochemistry · doi:10.1016/j.phytochem.2007.07.027 · Cited by 91 · see citing works
Ihalainen, J. A., Jensen, P. E., Haldrup, A., et al. (2002) · Biophysical Journal · doi:10.1016/s0006-3495(02)73979-9 · Cited by 84 · see citing works
Baalmann, E., Scheibe, R., Cerff, R., & Martin, W. F. (1996) · Plant Molecular Biology · doi:10.1007/bf00019102 · Cited by 78 · see citing works
Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability
Zhang, J., Lü, K., Xiang, Y., Islam, M. M., Kotian, S., Kais, Z., Lee, C., Arora, M., Liu, H.-W., Parvin, J. D., et al. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002656 · Cited by 78 · see citing works
Marri, L., Trost, P., Trivelli, X., Gonnelli, L., Pupillo, P., & Sparla, F. (2007) · Journal of Biological Chemistry · doi:10.1074/jbc.m705650200 · Cited by 76 · see citing works
Bang, W. Y., Jeong, I. S., Kim, D., Im, C. H., Chen, J., Hwang, S. M., Kim, S. W., Son, Y. S., Jeong, J., Shiina, T., et al. (2008) · Plant and Cell Physiology · doi:10.1093/pcp/pcn111 · Cited by 76 · see citing works
Marri, L. (2004) · Journal of Experimental Botany · doi:10.1093/jxb/eri020 · Cited by 75 · see citing works
Sengupta, U., Ukil, S., Dimitrova, N., & Agrawal, S. (2009) · PLoS ONE · doi:10.1371/journal.pone.0008100 · Cited by 74 · see citing works
Kwon, Y., Kim, J. H., Nguyen, N. H., et al. (2013) · Journal of Experimental Botany · doi:10.1093/jxb/ert223 · Cited by 63 · see citing works
Grigorova, B., Vaseva, I., Demirevska, K., et al. (2011) · Acta Physiologiae Plantarum · doi:10.1007/s11738-011-0733-9 · Cited by 56 · see citing works
Transcriptional Profiling of Rapidly Growing Cucumber Fruit by 454-Pyrosequencing Analysis
Ando, K., & Grumet, R. (2010) · Journal of the American Society for Horticultural Science · doi:10.21273/jashs.135.4.291 · Cited by 53 · see citing works
Learning in Probabilistic Expert Systems
Spiegelhalter, D. J., & Cowell, R. G. (1992) · doi:10.1093/oso/9780198522669.003.0025 · Cited by 53 · see citing works
Light-Dependent and Tissue-Specific Expression of the H-Protein of the Glycine Decarboxylase Complex
Srinivasan, R., & Oliver, D. J. (1995) · PLANT PHYSIOLOGY · doi:10.1104/pp.109.1.161 · Cited by 49 · see citing works
Infanger, S., Bischof, S., Hiltbrunner, A., Agne, B., Baginsky, S., & Kessler, F. (2011) · Molecular Plant · doi:10.1093/mp/ssq071 · Cited by 39 · see citing works
Steele, E., & Tucker, A. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.01.011 · Cited by 35 · see citing works
Self-citationConstruction of gene regulatory networks using biclustering and bayesian networks
Alakwaa, F., Solouma, N. H., & Kadah, Y. M. (2011) · Theoretical Biology and Medical Modelling · doi:10.1186/1742-4682-8-39 · Cited by 24 · see citing works
Isolation and Characterization of a Type II Peroxiredoxin Gene from Panax ginseng C. A. Meyer
Kim, Y., Lee, J., Lee, O.-R., et al. (2010) · Journal of Ginseng Research · doi:10.5142/jgr.2010.34.4.296 · Cited by 19 · see citing works
Assessing the functional coherence of modules found in multiple-evidence networks from Arabidopsis
Lysenko, A., Platel, M. D., Hassani‐Pak, K., Taubert, J., Hodgman, C. T., Rawlings, C., & Saqi, M. A. S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-203 · Cited by 18 · see citing works
Self-citation[Transcription factor database].
Kanamori, M. (2004) · PubMed · Cited by 11 · see citing works
The identification of informative genes from multiple datasets with increasing complexity
Anvar, S. Y., Hoen, P. A. C. ’., & Tucker, A. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-32 · Cited by 7 · see citing works
Self-citationBo, V., Lysenko, A., Saqi, M. A. S., et al. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-41398-8_10 · Cited by 2 · see citing works
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Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
- Authors
- Bo, V., Curtis, T., Lysenko, A., Saqi, M., Swift, S., & Tucker, A.
- Affiliations
- Brunel University of London (GB)
- Rothamsted Research (GB)
- Corresponding author
- Bo, Valeria
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-09-05
- Volume
- 9
- Issue
- 9
- Pages
- e106524
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4156366
- OpenAlex
- W2096904336
- Cited by
- 10
- References
- 62
- Citations by year
- 2024: 3 · 2023: 1 · 2019: 1 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 1
- FWCI
- 0.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rothamsted Repository (Rothamsted Repository) (submittedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by)
- Funders
- Biotechnology and Biological Sciences Research Council (BBS/E/C/00005205, BB/I020918/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- gene regulatory networks, microarray data integration, graphical lasso, sparse precision matrix estimation, wheat, Fusarium, plant-pathogen interaction
- MeSH terms
- Fusarium, Triticum, Cluster Analysis, Computational Biology, Gene Expression Profiling, Gene Regulatory Networks, Datasets as Topic
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