Controlling false discoveries in high-dimensional situations: Boosting with stability selection

BMC Bioinformatics

Volumn 16, Issue 1, 2015, Pages

  Hofner, Benjamin ^a   Boccuto, Luigi ^b   Göker, Markus ^c  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b GREENWOOD GENETIC CENTER   (United States)

^c LEIBNIZ INSTITUTE DSMZ GERMAN COLLECTION OF MICROORGANISMS AND CELL CULTURES   (Germany)

Author keywords

Boosting; Error control; Stability selection; Variable selection

Indexed keywords

AMINO ACIDS; CLUSTERING ALGORITHMS; ERROR ANALYSIS; ERRORS; SAMPLING;

AMINO ACID PATHWAY; AUTISM SPECTRUM DISORDERS; BOOSTING; ERROR CONTROL; HIGH DIMENSIONAL DATA; OBSERVATIONAL STUDY; STABILITY SELECTIONS; VARIABLE SELECTION;

STABILITY;

BIOLOGICAL MARKER;

ALGORITHM; AUTISM; CASE CONTROL STUDY; COMPARATIVE STUDY; COMPUTER SIMULATION; GENE EXPRESSION PROFILING; GENETICS; HUMAN; LABORATORY DIAGNOSIS; METABOLISM; NONLINEAR SYSTEM; PHENOTYPE; STATISTICAL MODEL;

ALGORITHMS; BIOMARKERS; CASE-CONTROL STUDIES; CHILD DEVELOPMENT DISORDERS, PERVASIVE; COMPUTER SIMULATION; FALSE POSITIVE REACTIONS; GENE EXPRESSION PROFILING; HUMANS; MODELS, STATISTICAL; NONLINEAR DYNAMICS; PHENOTYPE;

EID: 84931262233     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0575-3     Document Type: Article

References (52)

1. Chaturvedi N, Goeman J, Boer J, van Wieringen W, de Menezes R. A test for comparing two groups of samples when analyzing multiple omics profiles. BMC Bioinformatics. 2014; 15(1):236.
2. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. 2009; 10(1):57-63.
3. Mallick P, Kuster B. Proteomics: a pragmatic perspective. Nat Biotechnol. 2010; 28(7):695-709.
4. Ludwig C, Günther UL. Metabolab: Advanced NMR data processing and analysis for metabolomics. BMC Bioinformatics. 2011; 12(1):366.
5. Lindon JC, Holmes E, Nicholson JK. So what's the deal with metabonomics?Anal Chem. 2003; 75:385-91.
6. Groth P, Weiss B, Pohlenz HD, Leser U. Mining phenotypes for gene function prediction. BMC Bioinformatics. 2008; 9(1):136.
7. Kneib T, Hothorn T, Tutz G. Variable selection and model choice in geoadditive regression models. Biometrics. 2009; 65:626-34.
8. Flack VF, Chang PC. Frequency of selecting noise variables in subset regression analysis: a simulation study. Am Statistician. 1987; 41:84-6.
9. Austin PC, Tu JV. Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. J Cli Epidemiol. 2004; 57:1138-46.
10. Austin PC. Bootstrap model selection had similar performance for selecting authentic and noise variables compared to backward variable elimination: a simulation study. J Cli Epidemiol. 2008; 61:1009-17.
11. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc: Series B (Stat Methodol). 1996; 58:267-88.
12. Efron B, Hastie T, Johnstone I, Tibshirani R. Least angle regression (with discussion). Ann Stat. 2004; 32:407-51.
13. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc: Series B (Stat Methodol). 2005; 67:301-20.
14. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting (with discussion). Ann Stat. 2000; 28:337-407.
15. Breiman L. Random forests. Mach Lear. 2001; 45:5-32.
16. Strobl C, Boulesteix AL, Zeileis A, Hothorn T. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC Bioinfor. 2007; 8:25.
17. Meinshausen N, Bühlmann P. Stability selection (with discussion). J R Stat Soc: Series B (Stat Methodol). 2010; 72:417-73.
18. Shah RD, Samworth RJ. Variable selection with error control: another look at stability selection. J R Stat Soc: Series B (Stat Methodol). 2013; 75:55-80.
19. Haury AC, Mordelet F, Vera-Licona P, Vert JP. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Syst Biol. 2012; 6(1):145.
20. Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, et al. Wisdom of crowds for robust gene network inference. Nat methods. 2012; 9(8):796-804.
21. He Q, Lin DY. A variable selection method for genome-wide association studies. Bioinformatics. 2011; 27(1):1-8.
22. Fellinghauer B, Bühlmann P, Ryffel M, von Rhein M, Reinhardt JD. Stable graphical model estimation with random forests for discrete, continuous, and mixed variables. Comput Stat Data Anal. 2013; 64:132-52.
23. Bühlmann P, Kalisch M, Meier L. High-dimensional statistics with a view toward applications in biology. Annu Rev Stat Appl. 2014; 1:255-78.
24. Hothorn T, Müller J, Schröder B, Kneib T, Brandl R. Decomposing environmental, spatial, and spatiotemporal components of species distributions. Ecol Monogr. 2011; 81:329-47.
25. 2 loss: regression and classification. J Am Stat Assoc. 2003; 98:324-39.
26. Bühlmann P, Hothorn T. Boosting algorithms: Regularization, prediction and model fitting. Stat Sci. 2007; 22:477-505.
27. Hofner B, Mayr A, Robinzonov N, Schmid M. Model-based boosting in R - A hands-on tutorial using the R package mboost. Comput Stat. 2014; 29:3-35.
28. Hofner B, Hothorn T, Kneib T, Schmid M. A framework for unbiased model selection based on boosting. J Comput Graph Stat. 2011; 20:956-71.
29. Schmid M, Hothorn T. Boosting additive models using component-wise P-splines. Comput Stat Data Anal. 2008; 53:298-311.
30. Hofner B, Müller J, Hothorn T. Monotonicity-constrained species distribution models. Ecology. 2011; 92:1895-1901.
31. Hofner B, Kneib T, Hothorn T. A unified framework of constrained regression. Stat Comput. 2014:1-14.
32. Fenske N, Kneib T, Hothorn T. Identifying risk factors for severe childhood malnutrition by boosting additive quantile regression. J Am Stat Assoc. 2011; 106:494-510.
33. Sobotka F, Kneib T. Geoadditive expectile regression. Comput Stat Data Anal. 2012; 56:755-67.
34. Huber PJ. Robust estimation of a location parameter. Ann Stat. 1964; 53:73-101.
35. Mayr A, Schmid M. Boosting the concordance index for survival data - A unified framework to derive and evaluate biomarker combinations. PloS one. 2014; 9(1):84483.
36. Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B. Model-based boosting 2.0. J Mach Lear Res. 2010; 11:2109-113.
37. Hastie T, Tibshirani R. Generalized additive models. Stat Sci. 1986; 1:297-310.
38. Hastie T, Tibshirani R. Generalized additive models. London: Chapman & Hall/CRC; 1990.
39. Fahrmeir L, Kneib T, Lang S. Penalized structured additive regression: A Bayesian perspective. Stat Sinica. 2004; 14:731-61.
40. Mayr A, Hofner B, Schmid M. The importance of knowing when to stop - A sequential stopping rule for component-wise gradient boosting. Meth Info Med. 2012; 51:178-86.
41. Manning-Courtney P, Murray D, Currans K, Johnson H, Bing N, Kroeger-Geoppinger K, et al. Autism spectrum disorders. Curr Probl Pediatr Adolesc Health Care. 2013; 43(1):2-11. Autism Spectrum Disorders.
42. Boccuto L, Chen CF, Pittman A, Skinner C, McCartney H, Jones K, et al. Decreased tryptophan metabolism in patients with autism spectrum disorders. Mol Autism; 4(1):16.
43. Bochner BR, Gadzinski P, Panomitros E. Phenotype microarrays for high throughput phenotypic testing and assay of gene function. Genome Res. 2001; 11:1246-55.
44. Göker M, with contributions by Hofner B, Vaas LAI, Sikorski J, Buddruhs N, Fiebig A. opm: Analysing Phenotype Microarray and Growth Curve Data. 2014. R package version 1.1-0. http://CRAN.R-project.org/package=opm .
45. Vaas LAI, Sikorski J, Hofner B, Buddruhs N, Fiebig A, Klenk HP. Visualization and curve-parameter estimation strategies for efficient exploration of phenotype microarray kinetics. PloS one. 2012; 7(4):e34846.
46. Vaas LAI, Sikorski J, Michael V, Göker M, Klenk HP. opm: An R package for analysing OmniLog®; phenotype microarray data. Bioinformatics. 2013; 29(14):1823-4.
47. Schmid M, Hothorn T, Krause F, Rabe C. A PAUC-based estimation technique for disease classification and biomarker selection. Stat Appl Genet Mol Biol. 2012; 11(5):Article 3. doi:10.1515/1544-6115.1792.
48. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci. 2001; 98(9):5116-121.
49. Hothorn T. Discussion: Stability selection. J R Stat Soc: Series B (Stat Meth). 2010; 72:463-4.
50. Hofner B, Hothorn T. stabs: stability selection with error control. 2015. R package version 0.5-1. http://CRAN.R-project.org/package=stabs .
51. R Development Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2014. R Foundation for Statistical Computing. ISBN 3-900051-07-0. http://www.R-project.org .
52. Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B. mboost: Model-Based Boosting. 2015. R package version 2.4-2. http://CRAN.R-project.org/package=mboost .