
@phdthesis{boreal:75532,
 title = {Statistical contributrion to the analysis of metabonomic data in 1H-NMR spectroscopy},
 author = {Rousseau, Réjane},
 abstract = {This thesis provides a complete methodology to lead a metabonomic study from the data acquisition to the discovery of biomarkers. Metabonomics aims to discover biomarkers or the metabolites altered in biological reactions. 1H-NMR spectroscopy is used to generate spectral profiles describing the composition of metabolites in collected biofluid samples. Statistical comparisons of spectra obtained in various biological states permits a quantitative investigation of metabolites changes inherent to a studied biological reaction. The enormous amount of metabonomic data lets foreseeing opportunities for biological and pathological mechanisms discoveries as for clinical diagnosis and for solutions to speed up the drug discovery process. The use of statistics in metabonomics is commonly restricted to the search for biomarkers in the data. However, the success of a metabonomic study does not only depend on the choice of a statistical data analysis method but also on the information contained in the data. From this perspective, the methodology presented in this thesis brings statistical contributions to metabonomic on two axes. On one hand statistical tools are developed to improve the quality of the data. Databases and methods evaluating the variability of spectra are used to define an adequate mode of sample collection, preparation, 1H-NMR measurement. An automated 1H-NMR data pre-treatment procedure offers solutions to decrease instrumental and biological information out of interest. On the other hand, statistical methods to search for biomarkers in metabonomic data are developed and evaluated: a battery of six methods for two-class studies and a four steps methodology combining ICA and statistical modelling for two- and multiple-class as quantitative studies. The whole methodology is successfully applied to a real metabonomic study for the search of AMD disease biomarkers.},
 language = {Anglais},
 year = {2011},
 url = {http://hdl.handle.net/2078.1/75532},
 school = {UCL - SSH/LIDAM/ISBA - Institut de Statistique, Biostatistique et Sciences Actuarielles},
 url = {http://hdl.handle.net/2078.1/75532}}


@article{https://doi.org/10.1002/cem.2895,
author = {Thiel, Michel and Féraud, Baptiste and Govaerts, Bernadette},
title = {ASCA+ and APCA+: Extensions of ASCA and APCA in the analysis of unbalanced multifactorial designs},
journal = {Journal of Chemometrics},
volume = {31},
number = {6},
pages = {e2895},
keywords = {ASCA, ANOVA-PCA, general linear model, metabolomics, unbalanced design},
doi = {10.1002/cem.2895},
url = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/cem.2895},
eprint = {https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/cem.2895},
note = {e2895 cem.2895},
year = {2017}
}

@article{boreal:207565,
 title = {Comparison of PARAFASCA, AComDim, and AMOPLS approaches in the multivariate GLM modelling of multi-factorial designs},
 author = {Guisset, Séverine and Martin, Manon and Govaerts, Bernadette},
 abstract = {Data tables from experimental designs with multiple response variables and a large number of responses with respect to the number of observations are becoming more common, particularly in the omics and chemometrics fields. Traditional multivariate modelling approaches are not suitable for this type of data and more complex approaches like ANOVA Simultaneous Component Analysis (ASCA) and ANOVA Principal Component Analysis (APCA) have been developed to analyse them. They combine a matrix decomposition based on an ANOVA model with a multivariate step, Principal Component Analysis (PCA), which is applied to each decomposed matrix. This article compares ASCA and APCA to three other and less known techniques combining ANOVA and a multivariate step: PArallel Factor - Simultaneous Component Analysis (PARAFASCA), ANOVA Common Dimensions (AComDim), and ANOVA Multi-block Orthogonal Partial Least Squares (AMOPLS). The main advantages of these three methods are their ability to explore in a single and global procedure all (or a part) of the model effects, extract automatically the most important ones, interpret them visually and quantify their respective importance and significance. Theses approaches are presented in a common framework to make their comparison easier, some enhancements are introduced and they are also extended to unbalanced experimental designs thanks to a GLM version of the matrix decomposition. The three methods are applied to a 1H-NMR data set with a three-factor unbalanced experimental design. Quantitative and visual results are compared for the three techniques and show that they are all suitable for the analysis of a complex model, but differ in the type of outputs and the ease of interpretation of the results. This opens perspectives for the analysis of even more complex data, including models with random or quantitative effects.},
 Keywords = {ISBARP201904 , LIDAM:Statistics & Actuarial Sciences , LIDAM:Big Data , LIDAM:Health , LIDAMdiscipline:Biostat , ASCA , APCA , Multivariate , ANOVA , PARAFASCA , AComDim , AMOPLS , Experimental design},
 language = {Anglais},
 journal = {Chemometrics and Intelligent Laboratory Systems},
 volume = {184},
 pages = {44-63},
 issn = {0169-7439},
 doi = {10.1016/j.chemolab.2018.11.006},
 publisher = {Elsevier BV},
 year = {2019},
 url = {http://hdl.handle.net/2078.1/207565}}

 @inbook{boreal:230895,
 title = {The Essentials on Linear Regression, ANOVA, General Linear and Linear Mixed Models for the Chemist},
 author = {Govaerts, Bernadette and Francq, Bernard G. and Marion, Rebecca and Martin, Manon and Thiel, Michel},
 abstract = {This article provides a brief and accessible guide for implementing general, ANOVA and linear mixed models for the analysis of real world data from chemistry, industrial, bio or life-sciences experiments. The reader is introduced to the main concepts and vocabulary of the subject and to the main statistical methods available for formalizing, estimating and performing statistical inference on these models. Mathematical notations are not avoided but kept accessible, methods are illustrated on several detailed applications and the use of appropriate computer software is discussed.},
 Keywords = {ISBARP202011 , LIDAM:Statistics & Actuarial Sciences , LIDAM:Big Data , LIDAM:Health , LIDAMdiscipline:Biostat , Analysis of Variance (ANOVA) , Experimental design analysis , General linear model (GLM) , Least squares estimators , Linear mixed model (LMM) , Linear regression , Maximum likelihood estimation , R Software , Random effect , SAS , Statistical modeling},
 language = {Anglais},
 booktitle = {Comprehensive Chemometrics, Chemical and Biochemical Data Analysis},
 editor = {2nd Edition},
 isbn = {9780444641663},
 publisher = {Elsevier},
 year = {2020},
 url = {http://hdl.handle.net/2078.1/230895}}

@article{boreal:230905,
 title = {LiMM‐PCA: Combining ASCA+ and linear mixed models to analyse high‐dimensional designed data},
 author = {Martin, Manon and Govaerts, Bernadette},
 abstract = {Nowadays, life science experiments—and especially “omics” fields—often imply a high volume of information from high throughput technologies that is gathered in the form of a wide and short multivariate response. These data are intrinsically correlated and generally produced by another multivariate set of factors or continuous variables, collected in what is defined as the design matrix. Such design factors usually involve the presence of a treatment, but other sources of biological or technical variability in the data are often measured as well. The ASCA framework, based on ANOVA and PCA, leads to promising results. By combining dimension reduction projection methods and classic statistical modelling, it enables to decipher the main sources of variability in the produced response and offers attractive graphical representations of the factors' effect. However, this approach has not yet been extended to more advanced designs involving random factors, being typically involved in longitudinal, hierarchical, or repeatability/reproducibility studies. This paper has its roots in the GLM version of ASCA, called ASCA+, that leads to unbiased estimators of the factors' effects for unbalanced data. It is here extended by replacing GLM by LMM and adapting the methodology. Taking into account the error structure of the data indeed leads to more accurate data modelling and more generalisable results. The suggested methodology is applied to two experimental case studies that highlight the benefits of this approach as it leads to a refined data analysis with interesting inferential properties, while keeping the powerful visualisation outputs produced by ASCA.},
 Keywords = {ISBARP202012 , LIDAM:Statistics & Actuarial Sciences , LIDAM:Big Data , LIDAM:Health , LIDAMdiscipline:Biostat , ASCA , chemometrics , linear mixed models , PCA , random effects},
 language = {Anglais},
 journal = {Journal of Chemometrics},
 volume = {34},
 issn = {1099-128X},
 doi = {10.1002/cem.3232},
 publisher = {Wiley},
 year = {2020},
 url = {http://hdl.handle.net/2078.1/230905}}

