FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks

PLoS Computational Biology

Volumn 12, Issue 2, 2016, Pages

  Wang, Ting ^a   Ren, Zhao ^b   Ding, Ying ^c   Fang, Zhou ^c   Sun, Zhe ^c   MacDonald, Matthew L ^d   Sweet, Robert A ^d,e   Wang, Jieru ^a   Chen, Wei ^a,c  

^a CHILDREN S HOSPITAL OF PITTSBURGH   (United States)

^b UNIVERSITY OF PITTSBURGH   (United States)

^c UNIVERSITY OF PITTSBURGH GRADUATE SCHOOL OF PUBLIC HEALTH   (United States)

^d UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

^e VETERANS AFFAIRS MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; GAUSSIAN DISTRIBUTION; GRAPHIC METHODS; INFERENCE ENGINES;

BIOLOGICAL NETWORKS; CONDITIONAL DEPENDENCE; DEPENDENCE STRUCTURES; DIFFERENTIAL NETWORK; GAUSSIAN GRAPHICAL MODELS; HIGH DIMENSIONAL DATA; HUMAN DISEASE; P-VALUES; PROBABILITY MODELLING; SINGLE VARIABLE;

CLUSTERING ALGORITHMS;

ACCURACY; ALGORITHM; ALZHEIMER DISEASE; ARTICLE; ASTHMA; CONFIDENCE INTERVAL; CONTROLLED STUDY; DATA BASE; FASTGGM ALGORITHM; GAUSSIAN GRAPHICAL MODEL; GENE INTERACTION; HUMAN; HUMAN TISSUE; PROTEIN INTERACTION; PROTEOMICS; SIMULATION; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE; SYSTEMS BIOLOGY; TRANSCRIPTOMICS; BIOLOGICAL MODEL; BIOLOGY; CHILD; CLUSTER ANALYSIS; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORK; GENETICS; METABOLISM; NORMAL DISTRIBUTION; PROCEDURES;

ALGORITHMS; ASTHMA; CHILD; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENE REGULATORY NETWORKS; HUMANS; MODELS, BIOLOGICAL; NORMAL DISTRIBUTION;

EID: 84959544902     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004755     Document Type: Article

References (36)

1. Ren Z, Sun T, Zhang C-H, Zhou HH, Asymptotic normality and optimalities in estimation of large Gaussian graphical models. The Annals of Statistics. 2015; 43: 991–1026.
2. Barabasi AL, Oltvai ZN, Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004; 5: 101–113. 14735121
3. Ideker T, Krogan NJ, Differential network biology. Mol Syst Biol. 2012; 8: 565. doi: 10.1038/msb.2011.9922252388
4. Yuan M, Lin Y, Model selection and estimation in the Gaussian graphical model. Biometrika. 2007; 94: 19–35.
5. Meinshausen N, Bühlmann P, High-dimensional graphs and variable selection with the lasso. The Annals of Statistics. 2006: 1436–1462.
6. Sun T, Zhang C-H, Sparse matrix inversion with scaled lasso. The Journal of Machine Learning Research. 2013; 14: 3385–3418.
7. Liu W, Gaussian graphical model estimation with false discovery rate control. The Annals of Statistics. 2013; 41: 2948–2978.
8. Jankova J, van de Geer S. Confidence intervals for high-dimensional inverse covariance estimation. arXiv preprint arXiv:14036752. 2014.
9. Gu Q, Cao Y, Ning Y, Liu H. Local and Global Inference for High Dimensional Gaussian Copula Graphical Models. arXiv preprint arXiv:150202347. 2015.
10. Ning Y, Liu H. A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models. arXiv preprint arXiv:14128765. 2014.
11. Lauritzen SL, (1996) Graphical models: Oxford University Press.
12. Wang YR, Huang H, Review on statistical methods for gene network reconstruction using expression data. Journal of theoretical biology. 2014; 362: 53–61. doi: 10.1016/j.jtbi.2014.03.04024726980
13. Sun T, Zhang C, Comment: Minimax estimation of large covariance matrices under l1 norm. Statist Sinica. 2012; 22: 1354–1358.
14. Chen M, Ren Z, Zhao H, Zhou H, Asymptotically Normal and Efficient Estimation of Covariate-Adjusted Gaussian Graphical Model. Journal of the American Statistical Association. 2015; just-accepted: 00–00.
15. Fu WJ, Penalized regressions: the bridge versus the lasso. Journal of computational and graphical statistics. 1998; 7: 397–416.
16. Daubechies I, Defrise M, De Mol C, An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on pure and applied mathematics. 2004; 57: 1416–1457.
17. Friedman J, Hastie T, Tibshirani R, Regularization paths for generalized linear models via coordinate descent. Journal of statistical software. 2010; 33: 1. 20808728
18. Eddelbuettel D, François R, Allaire J, Chambers J, Bates D, et al. Rcpp: Seamless R and C++ integration. Journal of Statistical Software. 2011; 40: 1–18.
19. Honorio J, Jaakkola TS. Inverse covariance estimation for high-dimensional data in linear time and space: Spectral methods for riccati and sparse models. arXiv preprint arXiv:13096838. 2013.
20. Zhao T, Liu H, Roeder K, Lafferty J, Wasserman L, The huge package for high-dimensional undirected graph estimation in R. The Journal of Machine Learning Research. 2012; 13: 1059–1062.
21. Liang L, Morar N, Dixon AL, Lathrop GM, Abecasis GR, et al. A cross-platform analysis of 14,177 expression quantitative trait loci derived from lymphoblastoid cell lines. Genome research. 2013; 23: 716–726. doi: 10.1101/gr.142521.11223345460
22. Brazma A, Parkinson H, Sarkans U, Shojatalab M, Vilo J, et al. ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 2003; 31: 68–71. 12519949
23. Brightling C, Berry M, Amrani Y, Targeting TNF-alpha: a novel therapeutic approach for asthma. J Allergy Clin Immunol. 2008; 121: 5–10; quiz 11–12. 18036647
24. Mizue Y, Ghani S, Leng L, McDonald C, Kong P, et al. Role for macrophage migration inhibitory factor in asthma. Proc Natl Acad Sci U S A. 2005; 102: 14410–14415. 16186482
25. Verheyen GR, Nuijten JM, Van Hummelen P, Schoeters GR, Microarray analysis of the effect of diesel exhaust particles on in vitro cultured macrophages. Toxicol In Vitro. 2004; 18: 377–391. 15046786
26. Kim SR, Lee YC, Endoplasmic reticulum stress and the related signaling networks in severe asthma. Allergy Asthma Immunol Res. 2015; 7: 106–117. doi: 10.4168/aair.2015.7.2.10625729617
27. MacDonald ML, Ding Y, Newman J, Hemby S, Penzes P, et al. Altered glutamate protein co-expression network topology linked to spine loss in the auditory cortex of schizophrenia. Biol Psychiatry. 2015; 77: 959–968. doi: 10.1016/j.biopsych.2014.09.00625433904
28. Schafer J, Strimmer K, An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics. 2005; 21: 754–764. 15479708
29. De La Fuente A, Bing N, Hoeschele I, Mendes P, Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics. 2004; 20: 3565–3574. 15284096
30. Kim K, Jiang K, Teng SL, Feldman LJ, Huang H, Using biologically interrelated experiments to identify pathway genes in Arabidopsis. Bioinformatics. 2012; 28: 815–822. doi: 10.1093/bioinformatics/bts03822271267
31. Cai TT, Li H, Liu W, Xie J, Covariate-adjusted precision matrix estimation with an application in genetical genomics. Biometrika. 2012: ass058.
32. Yin J, Li H, A Sparse Conditional Gaussian Graphical Model for Analysis of Genetical Genomics Data. Ann Appl Stat. 2011; 5: 2630–2650. 22905077
33. Yin J, Li H, Adjusting for High-dimensional Covariates in Sparse Precision Matrix Estimation by l-Penalization. J Multivar Anal. 2013; 116: 365–381. 23687392
34. Wang W, Baladandayuthapani V, Holmes CC, Do KA, Integrative network-based Bayesian analysis of diverse genomics data. BMC Bioinformatics. 2013; 14Suppl 13: S8. doi: 10.1186/1471-2105-14-S13-S824267288
35. Ni Y, Stingo FC, Baladandayuthapani V, Integrative bayesian network analysis of genomic data. Cancer Inform. 2014; 13: 39–48. doi: 10.4137/CIN.S1378625288878
36. Stingo FC, Chen YA, Vannucci M, Barrier M, Mirkes PE, A Bayesian Graphical Modeling Approach to Microrna Regulatory Network Inference. Ann Appl Stat. 2010; 4: 2024–2048. 23946863