Deciphering the complex: Methodological overview of statistical models to derive OMICS‐based biomarkers
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Paolo Vineis | Benoit Liquet | Thibaut Jombart | Leonardo Bottolo | L. Bottolo | P. Vineis | R. Vermeulen | T. Jombart | B. Liquet | M. Chadeau-Hyam | L. Portengen | G. Campanella | Gianluca Campanella | Lutzen Portengen | Roel C.H. Vermeulen | Marc Chadeau‐Hyam
[1] H. Hotelling. Analysis of a complex of statistical variables into principal components. , 1933 .
[2] Stephen M Rappaport,et al. Environment and Disease Risks , 2010, Science.
[3] M. Stephens,et al. Bayesian variable selection regression for genome-wide association studies and other large-scale problems , 2011, 1110.6019.
[4] Hao Li,et al. Analysis of oligonucleotide array experiments with repeated measures using mixed models , 2004, BMC Bioinformatics.
[5] C. Wild,et al. The exposome: from concept to utility. , 2012, International journal of epidemiology.
[6] M. West,et al. Shotgun Stochastic Search for “Large p” Regression , 2007 .
[7] D. Balding. A tutorial on statistical methods for population association studies , 2006, Nature Reviews Genetics.
[8] Zhiwu Zhang,et al. Mixed linear model approach adapted for genome-wide association studies , 2010, Nature Genetics.
[9] Nicolas Chopin,et al. Sequential Monte Carlo on large binary sampling spaces , 2011, Statistics and Computing.
[10] James G. Scott,et al. Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction , 2022 .
[11] R. Tibshirani,et al. Generalized additive models for medical research , 1995, Statistical methods in medical research.
[12] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[13] Anne-Laure Boulesteix,et al. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data , 2006, Briefings Bioinform..
[14] M. Rantalainen,et al. Statistically integrated metabonomic-proteomic studies on a human prostate cancer xenograft model in mice. , 2006, Journal of proteome research.
[15] S. Wood. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models , 2011 .
[16] Thibaut Jombart,et al. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data , 2011, Bioinform..
[17] S. Dudoit,et al. Multiple Testing Procedures with Applications to Genomics , 2007 .
[18] Fredrik Barrenäs,et al. Identification of Novel Biomarkers in Seasonal Allergic Rhinitis by Combining Proteomic, Multivariate and Pathway Analysis , 2011, PloS one.
[19] Kristin E. Porter,et al. Global Gene Expression Profiling of a Population Exposed to a Range of Benzene Levels , 2010, Environmental health perspectives.
[20] Francis R. Bach,et al. Bolasso: model consistent Lasso estimation through the bootstrap , 2008, ICML '08.
[21] D. Reich,et al. Principal components analysis corrects for stratification in genome-wide association studies , 2006, Nature Genetics.
[22] S. Wood. Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models , 2004 .
[23] Seán G. Brady,et al. The Importance of Using Multiple Approaches for Identifying Emerging Invasive Species: The Case of the Rasberry Crazy Ant in the United States , 2012, PloS one.
[24] R. Clarke,et al. Theory and Applications of Correspondence Analysis , 1985 .
[25] T Jombart,et al. Genetic markers in the playground of multivariate analysis , 2009, Heredity.
[26] Yogendra P. Chaubey. Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment , 1993 .
[27] G. Casella,et al. The Bayesian Lasso , 2008 .
[28] Stephen M Rappaport,et al. Biomarkers intersect with the exposome , 2012, Biomarkers : biochemical indicators of exposure, response, and susceptibility to chemicals.
[29] Arthur E. Hoerl,et al. Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.
[30] Johan Trygg,et al. Integrated analysis of transcript, protein and metabolite data to study lignin biosynthesis in hybrid aspen. , 2009, Journal of proteome research.
[31] Oliver Stegle,et al. A Lasso multi-marker mixed model for association mapping with population structure correction , 2013, Bioinform..
[32] Alex Lewin,et al. A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments , 2004, Bioinform..
[33] Anne-Béatrice Dufour,et al. The ade4 Package: Implementing the Duality Diagram for Ecologists , 2007 .
[34] Kim-Anh Lê Cao,et al. A novel approach for biomarker selection and the integration of repeated measures experiments from two assays , 2012, BMC Bioinformatics.
[35] Scott C Schmidler,et al. BAYESIAN MODEL SEARCH AND MULTILEVEL INFERENCE FOR SNP ASSOCIATION STUDIES. , 2009, The annals of applied statistics.
[36] L. Cavalli-Sforza. Population structure and human evolution , 1966, Proceedings of the Royal Society of London. Series B. Biological Sciences.
[37] K. Lange,et al. Prioritizing GWAS results: A review of statistical methods and recommendations for their application. , 2010, American journal of human genetics.
[38] R. Tibshirani,et al. Sparsity and smoothness via the fused lasso , 2005 .
[39] Marc Chadeau-Hyam,et al. Metabolome-wide association study identifies multiple biomarkers that discriminate north and south Chinese populations at differing risks of cardiovascular disease: INTERMAP study. , 2010, Journal of proteome research.
[40] S. Keleş,et al. Sparse partial least squares regression for simultaneous dimension reduction and variable selection , 2010, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[41] Hyonho Chun,et al. Expression Quantitative Trait Loci Mapping With Multivariate Sparse Partial Least Squares Regression , 2009, Genetics.
[42] Fei Zou,et al. An Efficient Resampling Method for Assessing Genome-Wide Statistical Significance in Mapping Quantitative Trait Loci , 2004, Genetics.
[43] T. Fearn,et al. Bayes model averaging with selection of regressors , 2002 .
[44] David Tritchler,et al. Genome-wide sparse canonical correlation of gene expression with genotypes , 2007, BMC proceedings.
[45] Tommy Löfstedt,et al. OnPLS—a novel multiblock method for the modelling of predictive and orthogonal variation , 2011 .
[46] Y. Escoufier,et al. The Duality Diagram: A Means for Better Practical Applications , 1987 .
[47] John M. Walker,et al. Metabolic Profiling , 2011, Methods in Molecular Biology.
[48] David Reich,et al. Principal component analysis of genetic data , 2008, Nature Genetics.
[49] S. Wold,et al. Orthogonal projections to latent structures (O‐PLS) , 2002 .
[50] Satkartar K. Kinney,et al. Fixed and Random Effects Selection in Linear and Logistic Models , 2007, Biometrics.
[51] John D. Storey,et al. Significance analysis of time course microarray experiments. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[52] Simon C. Potter,et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls , 2007, Nature.
[53] R. Fisher. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .
[54] Jianhua Z. Huang,et al. Sparse principal component analysis via regularized low rank matrix approximation , 2008 .
[55] H. Kang,et al. Variance component model to account for sample structure in genome-wide association studies , 2010, Nature Genetics.
[56] Timothy M. D. Ebbels,et al. The evolution of partial least squares models and related chemometric approaches in metabonomics and metabolic phenotyping , 2010 .
[57] I. King Jordan,et al. On the presence and role of human gene-body DNA methylation , 2012, Oncotarget.
[58] John D. Storey. A direct approach to false discovery rates , 2002 .
[59] Sylvia Richardson,et al. Evolutionary Stochastic Search for Bayesian model exploration , 2010, 1002.2706.
[60] R. Tibshirani,et al. A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. , 2009, Biostatistics.
[61] Maria De Iorio,et al. Bayesian survival analysis in genetic association studies , 2008, Bioinform..
[62] Bjarni J. Vilhjálmsson,et al. An efficient multi-locus mixed model approach for genome-wide association studies in structured populations , 2012, Nature Genetics.
[63] D. Tritchler,et al. Sparse Canonical Correlation Analysis with Application to Genomic Data Integration , 2009, Statistical applications in genetics and molecular biology.
[64] Daniel Eriksson,et al. Data integration in plant biology: the O2PLS method for combined modeling of transcript and metabolite data. , 2007, The Plant journal : for cell and molecular biology.
[65] Meili Baragatti,et al. Bayesian Variable Selection for Probit Mixed Models Applied to Gene Selection , 2011, 1101.4577.
[66] John D. Storey. The positive false discovery rate: a Bayesian interpretation and the q-value , 2003 .
[67] Giuseppe Musumarra,et al. OPLS-DA as a suitable method for selecting a set of gene transcripts discriminating RAS- and PTPN11-mutated cells in acute lymphoblastic leukaemia. , 2011, Combinatorial chemistry & high throughput screening.
[68] S. Wold,et al. The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses , 1984 .
[69] S. Holm. A Simple Sequentially Rejective Multiple Test Procedure , 1979 .
[70] S. Wood,et al. Generalized Additive Models: An Introduction with R , 2006 .
[71] M. Clyde,et al. Mixtures of g Priors for Bayesian Variable Selection , 2008 .
[72] Sylvia Richardson,et al. Bayesian Detection of Expression Quantitative Trait Loci Hot Spots , 2011, Genetics.
[73] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[74] Manuele Bicego,et al. The Grapevine Expression Atlas Reveals a Deep Transcriptome Shift Driving the Entire Plant into a Maturation Program[W][OA] , 2012, Plant Cell.
[75] R. O’Hara,et al. A review of Bayesian variable selection methods: what, how and which , 2009 .
[76] Nicolai Meinshausen,et al. Relaxed Lasso , 2007, Comput. Stat. Data Anal..
[77] Y. Hochberg. A sharper Bonferroni procedure for multiple tests of significance , 1988 .
[78] Yongchao Ge. Resampling-based Multiple Testing for Microarray Data Analysis , 2003 .
[79] J. Trygg. O2‐PLS for qualitative and quantitative analysis in multivariate calibration , 2002 .
[80] Qing Li,et al. The Bayesian elastic net , 2010 .
[81] H. Tapp,et al. Patterns of DNA methylation in individual colonic crypts reveal aging and cancer-related field defects in the morphologically normal mucosa. , 2010, Carcinogenesis.
[82] Karl Pearson F.R.S.. LIII. On lines and planes of closest fit to systems of points in space , 1901 .
[83] R. Tibshirani,et al. Sparse Principal Component Analysis , 2006 .
[84] T. Hothorn,et al. Multiple Comparisons Using R , 2010 .
[85] P. Mendes,et al. The origin of correlations in metabolomics data , 2005, Metabolomics.
[86] Deepayan Sarkar,et al. Detecting differential gene expression with a semiparametric hierarchical mixture method. , 2004, Biostatistics.
[87] Robert Kohn,et al. Nonparametric regression using linear combinations of basis functions , 2001, Stat. Comput..
[88] Johan Trygg,et al. O2‐PLS, a two‐block (X–Y) latent variable regression (LVR) method with an integral OSC filter , 2003 .
[89] C. Hoggart,et al. Genome‐wide significance for dense SNP and resequencing data , 2008, Genetic epidemiology.
[90] F. Balloux,et al. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations , 2010, BMC Genetics.
[91] A. E. Hoerl,et al. Ridge Regression: Applications to Nonorthogonal Problems , 1970 .
[92] Marcos Dipinto,et al. Discriminant analysis , 2020, Predictive Analytics.
[93] A. Chess,et al. Gene Body-Specific Methylation on the Active X Chromosome , 2007, Science.
[94] M. McMullen,et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness , 2006, Nature Genetics.
[95] Y. Benjamini,et al. Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .
[96] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[97] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[98] Mark I. McCarthy,et al. Genome-Wide Association Study Reveals Multiple Loci Associated with Primary Tooth Development during Infancy , 2010, PLoS genetics.
[99] Philippe Besse,et al. Statistical Applications in Genetics and Molecular Biology A Sparse PLS for Variable Selection when Integrating Omics Data , 2011 .
[100] Y. Benjamini,et al. THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .
[101] Z. Šidák. Rectangular Confidence Regions for the Means of Multivariate Normal Distributions , 1967 .
[102] D. Bates,et al. Nonlinear mixed effects models for repeated measures data. , 1990, Biometrics.
[103] Marc Chadeau-Hyam,et al. Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification. , 2010, Journal of proteome research.
[104] Philippe Besse,et al. Sparse canonical methods for biological data integration: application to a cross-platform study , 2009, BMC Bioinformatics.
[105] R. Tibshirani,et al. Generalized Additive Models: Some Applications , 1987 .
[106] J. Copas. Regression, Prediction and Shrinkage , 1983 .
[107] J. Gilbert,et al. Complement Factor H Variant Increases the Risk of Age-Related Macular Degeneration , 2005, Science.
[108] Trevor Hastie,et al. Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. , 2011, Journal of statistical software.
[109] D. Zilberman,et al. Genome-Wide Evolutionary Analysis of Eukaryotic DNA Methylation , 2010, Science.
[110] Ian J. Brown,et al. Human metabolic phenotype diversity and its association with diet and blood pressure , 2008, Nature.
[111] F. Dudbridge,et al. Estimation of significance thresholds for genomewide association scans , 2008, Genetic epidemiology.
[112] Meïli C. Baragatti,et al. A study of variable selection using g-prior distribution with ridge parameter , 2011, Comput. Stat. Data Anal..
[113] C. Wild. Complementing the Genome with an “Exposome”: The Outstanding Challenge of Environmental Exposure Measurement in Molecular Epidemiology , 2005, Cancer Epidemiology Biomarkers & Prevention.
[114] Matthias Heinig,et al. New Insights into the Genetic Control of Gene Expression using a Bayesian Multi-tissue Approach , 2010, PLoS Comput. Biol..
[115] M. Stephens,et al. Genome-wide Efficient Mixed Model Analysis for Association Studies , 2012, Nature Genetics.
[116] Marc Chadeau-Hyam,et al. ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration , 2011, Bioinform..
[117] P. Bühlmann,et al. Estimation for High‐Dimensional Linear Mixed‐Effects Models Using ℓ1‐Penalization , 2010, 1002.3784.
[118] D. Reich,et al. Population Structure and Eigenanalysis , 2006, PLoS genetics.