Prior knowledge driven Granger causality analysis on gene regulatory network discovery

Yao, Shun; Yoo, Shinjae; Yu, Dantong

doi:10.1186/s12859-015-0710-1

Title: Prior knowledge driven Granger causality analysis on gene regulatory network discovery

Journal Article · Fri Aug 28 00:00:00 EDT 2015 · BMC Bioinformatics

DOI:https://doi.org/10.1186/s12859-015-0710-1· OSTI ID:1259268

Yao, Shun ^[1]; Yoo, Shinjae ^[2]; Yu, Dantong ^[2]

Stony Brook Univ., NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Brookhaven National Lab. (BNL), Upton, NY (United States)

Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE

OSTI ID:: 1259268

Journal Information:: BMC Bioinformatics, Vol. 16, Issue 1; ISSN 1471-2105

Publisher:: BioMed CentralCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 12 works

Citation information provided by
Web of Science

References (48)

A decade’s perspective on DNA sequencing technology Mardis, Elaine R. Nature, Vol. 470, Issue 7333, p. 198-203 https://doi.org/10.1038/nature09796	journal	February 2011
Bioinformatics challenges of new sequencing technology Pop, Mihai; Salzberg, Steven L. Trends in Genetics, Vol. 24, Issue 3 https://doi.org/10.1016/j.tig.2007.12.006	journal	March 2008
Gene regulatory network inference: Data integration in dynamic models—A review Hecker, Michael; Lambeck, Sandro; Toepfer, Susanne Biosystems, Vol. 96, Issue 1 https://doi.org/10.1016/j.biosystems.2008.12.004	journal	April 2009
NCBI GEO: archive for functional genomics data sets—update Barrett, Tanya; Wilhite, Stephen E.; Ledoux, Pierre Nucleic Acids Research, Vol. 41, Issue D1 https://doi.org/10.1093/nar/gks1193	journal	November 2012
Inferring gene regulatory networks from time series data using the minimum description length principle Zhao, W.; Serpedin, E.; Dougherty, E. R. Bioinformatics, Vol. 22, Issue 17 https://doi.org/10.1093/bioinformatics/btl364	journal	July 2006
Boolean network inference from time series data incorporating prior biological knowledge Haider, Saad; Pal, Ranadip BMC Genomics, Vol. 13, Issue Suppl 6 https://doi.org/10.1186/1471-2164-13-S6-S9	journal	January 2012
Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data Kim, Sunyong; Imoto, Seiya; Miyano, Satoru Biosystems, Vol. 75, Issue 1-3 https://doi.org/10.1016/j.biosystems.2004.03.004	journal	July 2004
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data Zou, M.; Conzen, S. D. Bioinformatics, Vol. 21, Issue 1 https://doi.org/10.1093/bioinformatics/bth463	journal	August 2004
Characterizing Dynamic Changes in the Human Blood Transcriptional Network Zhu, Jun; Chen, Yanqing; Leonardson, Amy S. PLoS Computational Biology, Vol. 6, Issue 2 https://doi.org/10.1371/journal.pcbi.1000671	journal	February 2010
Granger causality vs. dynamic Bayesian network inference: a comparative study Zou, Cunlu; Feng, Jianfeng BMC Bioinformatics, Vol. 10, Issue 1 https://doi.org/10.1186/1471-2105-10-122	journal	April 2009
Fast Bayesian inference for gene regulatory networks using ScanBMA Young, William; Raftery, Adrian E.; Yeung, Ka BMC Systems Biology, Vol. 8, Issue 1 https://doi.org/10.1186/1752-0509-8-47	journal	January 2014
Reverse engineering of regulatory networks in human B cells Basso, Katia; Margolin, Adam A.; Stolovitzky, Gustavo Nature Genetics, Vol. 37, Issue 4 https://doi.org/10.1038/ng1532	journal	March 2005
TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach Zoppoli, Pietro; Morganella, Sandro; Ceccarelli, Michele BMC Bioinformatics, Vol. 11, Issue 1 https://doi.org/10.1186/1471-2105-11-154	journal	January 2010
Inference of gene regulatory networks from time series by Tsallis entropy Lopes, Fabrício Martins; de Oliveira, Evaldo A.; Cesar, Roberto M. BMC Systems Biology, Vol. 5, Issue 1 https://doi.org/10.1186/1752-0509-5-61	journal	May 2011
Experimental assessment of static and dynamic algorithms for gene regulation inference from time series expression data Lopes, Miguel; Bontempi, Gianluca Frontiers in Genetics, Vol. 4 https://doi.org/10.3389/fgene.2013.00303	journal	January 2013
Investigating Causal Relations by Econometric Models and Cross-spectral Methods Granger, C. W. J. Econometrica, Vol. 37, Issue 3 https://doi.org/10.2307/1912791	journal	August 1969
Testing for causality Granger, C. W. J. Journal of Economic Dynamics and Control, Vol. 2 https://doi.org/10.1016/0165-1889(80)90069-X	journal	January 1980
Causality and pathway search in microarray time series experiment Mukhopadhyay, N. D.; Chatterjee, S. Bioinformatics, Vol. 23, Issue 4 https://doi.org/10.1093/bioinformatics/btl598	journal	December 2006
Granger Causality Analysis of Human Cell-Cycle Gene Expression Profiles Nagarajan, Radhakrishnan; Upreti, Meenakshi Statistical Applications in Genetics and Molecular Biology, Vol. 9, Issue 1 https://doi.org/10.2202/1544-6115.1555	journal	January 2010
Application of Granger causality to gene regulatory network discovery Tam, Gary Hak Fui; Chang, Chunqi; Hung, Yeung Sam 2012 IEEE 6th International Conference on Systems Biology (ISB) https://doi.org/10.1109/ISB.2012.6314142	conference	August 2012
Grouped graphical Granger modeling for gene expression regulatory networks discovery Lozano, A. C.; Abe, N.; Liu, Y. Bioinformatics, Vol. 25, Issue 12 https://doi.org/10.1093/bioinformatics/btp199	journal	May 2009
Reconstructing gene-regulatory networks from time series, knock-out data, and prior knowledge Geier, Florian; Timmer, Jens; Fleck, Christian BMC Systems Biology, Vol. 1, Issue 1 https://doi.org/10.1186/1752-0509-1-11	journal	February 2007
Identification of Genes Periodically Expressed in the Human Cell Cycle and Their Expression in Tumors Whitfield, Michael L.; Sherlock, Gavin; Saldanha, Alok J. Molecular Biology of the Cell, Vol. 13, Issue 6 https://doi.org/10.1091/mbc.02-02-0030	journal	June 2002
Logic of the Yeast Metabolic Cycle: Temporal Compartmentalization of Cellular Processes Tu, B. P. Science, Vol. 310, Issue 5751 https://doi.org/10.1126/science.1120499	journal	November 2005
Ridge Regression: Biased Estimation for Nonorthogonal Problems Hoerl, Arthur E.; Kennard, Robert W. Technometrics, Vol. 12, Issue 1 https://doi.org/10.1080/00401706.1970.10488634	journal	February 1970
Regression Shrinkage and Selection Via the Lasso Tibshirani, Robert Journal of the Royal Statistical Society: Series B (Methodological), Vol. 58, Issue 1 https://doi.org/10.1111/j.2517-6161.1996.tb02080.x	journal	January 1996
The lasso problem and uniqueness Tibshirani, Ryan J. Electronic Journal of Statistics, Vol. 7, Issue 0 https://doi.org/10.1214/13-EJS815	journal	January 2013
Regularization Paths for Generalized Linear Models via Coordinate Descent Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert Journal of Statistical Software, Vol. 33, Issue 1 https://doi.org/10.18637/jss.v033.i01	journal	January 2010
Regression Modeling Strategies Harrell, Frank E. Springer Series in Statistics https://doi.org/10.1007/978-1-4757-3462-1	book	January 2001
Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks Yan, K. -K.; Fang, G.; Bhardwaj, N. Proceedings of the National Academy of Sciences, Vol. 107, Issue 20 https://doi.org/10.1073/pnas.0914771107	journal	May 2010
Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data Cheng, Chao; Yan, Koon-Kiu; Hwang, Woochang PLoS Computational Biology, Vol. 7, Issue 11 https://doi.org/10.1371/journal.pcbi.1002190	journal	November 2011
Architecture of the human regulatory network derived from ENCODE data Gerstein, Mark B.; Kundaje, Anshul; Hariharan, Manoj Nature, Vol. 489, Issue 7414 https://doi.org/10.1038/nature11245	journal	September 2012
Regularization and variable selection via the elastic net Zou, Hui; Hastie, Trevor Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 67, Issue 2 https://doi.org/10.1111/j.1467-9868.2005.00503.x	journal	April 2005
A MATLAB toolbox for Granger causal connectivity analysis Seth, Anil K. Journal of Neuroscience Methods, Vol. 186, Issue 2 https://doi.org/10.1016/j.jneumeth.2009.11.020	journal	February 2010
minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information Meyer, Patrick E.; Lafitte, Frédéric; Bontempi, Gianluca BMC Bioinformatics, Vol. 9, Issue 1 https://doi.org/10.1186/1471-2105-9-461	journal	October 2008
Reverse engineering gene networks using singular value decomposition and robust regression Yeung, M. K. S.; Tegner, J.; Collins, J. J. Proceedings of the National Academy of Sciences, Vol. 99, Issue 9 https://doi.org/10.1073/pnas.092576199	journal	April 2002
Singular value decomposition and least squares solutions Golub, G. H.; Reinsch, C. Numerische Mathematik, Vol. 14, Issue 5 https://doi.org/10.1007/BF02163027	journal	April 1970
Genetic reconstruction of a functional transcriptional regulatory network Hu, Zhanzhi; Killion, Patrick J.; Iyer, Vishwanath R. Nature Genetics, Vol. 39, Issue 5 https://doi.org/10.1038/ng2012	journal	April 2007
An Improved, Bias-Reduced Probabilistic Functional Gene Network of Baker's Yeast, Saccharomyces cerevisiae Lee, Insuk; Li, Zhihua; Marcotte, Edward M. PLoS ONE, Vol. 2, Issue 10 https://doi.org/10.1371/journal.pone.0000988	journal	October 2007
High-resolution DNA-binding specificity analysis of yeast transcription factors Zhu, C.; Byers, K. J. R. P.; McCord, R. P. Genome Research, Vol. 19, Issue 4 https://doi.org/10.1101/gr.090233.108	journal	January 2009
Cytoscape 2.8: new features for data integration and network visualization Smoot, M. E.; Ono, K.; Ruscheinski, J. Bioinformatics, Vol. 27, Issue 3 https://doi.org/10.1093/bioinformatics/btq675	journal	December 2010
SGD: Saccharomyces Genome Database Cherry, J. Nucleic Acids Research, Vol. 26, Issue 1 https://doi.org/10.1093/nar/26.1.73	journal	January 1998
The Shannon sampling theorem—Its various extensions and applications: A tutorial review Jerri, A. J. Proceedings of the IEEE, Vol. 65, Issue 11 https://doi.org/10.1109/PROC.1977.10771	journal	January 1977
The Forkhead transcription factor Hcm1 regulates chromosome segregation genes and fills the S-phase gap in the transcriptional circuitry of the cell cycle Pramila, T. Genes & Development, Vol. 20, Issue 16 https://doi.org/10.1101/gad.1450606	journal	August 2006
Ontology-driven integrative analysis of omics data through Onassis Galeota, Eugenia; Kishore, Kamal; Pelizzola, Mattia Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-57716-1	journal	January 2020
Challenges and opportunities for strain verification by whole-genome sequencing Gallegos, Jenna E.; Hayrynen, Sergei; Adames, Neil R. Scientific Reports, Vol. 10, Issue 1 https://doi.org/10.1038/s41598-020-62364-6	journal	April 2020
Single-Cell Transcriptomic Atlas of the Human Endometrium During the Menstrual Cycle Wang, Wanxin; Vilella, Felipe; Alama, Pilar Obstetrical & Gynecological Survey, Vol. 77, Issue 2 https://doi.org/10.1097/ogx.0000000000001009	journal	February 2022
Rootstock-regulated gene expression patterns associated with fire blight resistance in apple Jensen, Philip J.; Halbrendt, Noemi; Fazio, Gennaro BMC Genomics, Vol. 13, Issue 1 https://doi.org/10.1186/1471-2164-13-9	journal	January 2012

Cited By (2)

Computational dynamic approaches for temporal omics data with applications to systems medicine Liang, Yulan; Kelemen, Arpad BioData Mining, Vol. 10, Issue 1 https://doi.org/10.1186/s13040-017-0140-x	journal	June 2017
Prophetic Granger Causality to infer gene regulatory networks Carlin, Daniel E.; Paull, Evan O.; Graim, Kiley PLOS ONE, Vol. 12, Issue 12 https://doi.org/10.1371/journal.pone.0170340	journal	December 2017

Similar Records

Exploring Granger causality between global average observed time series of carbon dioxide and temperature

Journal Article · Fri Jan 01 00:00:00 EST 2010 · Theoretical and Applied Climatology · OSTI ID:1259268

Kodra, Evan A; Chatterjee, Snigdhansu; Ganguly, Auroop R

Cardinality-Regularized Hawkes-Granger Model

Conference · Mon Dec 06 00:00:00 EST 2021 · OSTI ID:1259268

Ide, Tsuyoshi; Kollias, Georgios; Phan, Dzung T; +1 more

Information-incorporated gene network construction with FDR control

Journal Article · Sat Mar 02 00:00:00 EST 2024 · Bioinformatics · OSTI ID:1259268

Wang, Hao; Qiu, Yumou; Guo, Hongqing; +3 more

Related Subjects

60 APPLIED LIFE SCIENCES
Time series
Gene expression data
Granger causality
Gene regulatory networks

Title: Prior knowledge driven Granger causality analysis on gene regulatory network discovery

Citation Formats

References (48)

Cited By (2)

Similar Records

Related Subjects