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331 results on '"Vialaneix, Nathalie"'

1. CRAN Task Views: The Next Generation

Author

Zeileis, Achim, Bivand, Roger, Eddelbuettel, Dirk, Hornik, Kurt, and Vialaneix, Nathalie

Subjects
Statistics - Computation
Abstract

CRAN Task Views have been available on the Comprehensive R Archive Network since 2005. They provide guidance about which CRAN packages are relevant for tasks related to a certain topic, and can also facilitate automatic installation of all corresponding packages. Motivated by challenges from the growth of CRAN and the R community as a whole since 2005, all of the task views infrastructure and workflows were rethought and relaunched in 2021/22 in order to facilitate maintenance and to foster deeper interactions with the R community. The redesign encompasses the establishment of a group of CRAN Task View Editors, moving all task view sources to dedicated GitHub repositories, adopting well-documented workflows with a code of conduct, and leveraging R/Markdown files (rather than XML) for the content of the task views., Comment: 8 pages, 1 figure

Published
2023

2. Network analyses reveal negative link between changes in adipose tissue GDF15 and BMI during dietary-induced weight loss

Author

Imbert, Alyssa, Vialaneix, Nathalie, Marquis, Julien, Vion, Julie, Charpagne, Aline, Metairon, Sylviane, Laurens, Claire, Moro, Cedric, Boulet, Nathalie, Walter, Ondine, Lefebvre, Grégory, Hager, Jörg, Langin, Dominique, Saris, Wim, Astrup, Arne, Viguerie, Nathalie, and Valsesia, Armand

Subjects
Statistics - Applications
Abstract

Adipose tissue (AT) transcriptome studies provide holistic pictures of adaptation to weight and related bioclinical settings changes. Objective To implement AT gene expression profiling and investigate the link between changes in bioclinical parameters and AT gene expression during 3 steps of a 2-phase dietary intervention (DI). Methods AT transcriptome profiling was obtained from sequencing 1051 samples, corresponding to 556 distinct individuals enrolled in a weight loss intervention (8-week low-calorie diet (LCD) at 800 kcal/day) followed with a 6-month ad libitum randomized DI. Transcriptome profiles obtained with QuantSeq sequencing were benchmarked against Illumina RNAseq. Reverse transcription quantitative polymerase chain reaction was used to further confirm associations. Cell specificity was assessed using freshly isolated cells and THP-1 cell line. Results During LCD, 5 modules were found, of which 3 included at least 1 bioclinical variable. Change in body mass index (BMI) connected with changes in mRNA level of genes with inflammatory response signature. In this module, change in BMI was negatively associated with changes in expression of genes encoding secreted protein (GDF15, CCL3, and SPP1). Through all phases of the DI, change in GDF15 was connected to changes in SPP1, CCL3, LIPA and CD68. Further characterization showed that these genes were specific to macrophages (with LIPA, CD68 and GDF15 expressed in anti-inflammatory macrophages) and GDF15 also expressed in preadipocytes. Conclusion Network analyses identified a novel AT feature with GDF15 upregulated with calorie restriction induced weight loss, concomitantly to macrophage markers. In AT, GDF15 was expressed in preadipocytes and macrophages where it was a hallmark of anti-inflammatory cells.

Published
2022
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5. Applicability and Interpretability of Hierarchical Agglomerative Clustering With or Without Contiguity Constraints

Author

Randriamihamison, Nathanaël, Vialaneix, Nathalie, and Neuvial, Pierre

Subjects
Statistics - Methodology
Abstract

Hierarchical Agglomerative Classification (HAC) with Ward's linkage has been widely used since its introduction in Ward (1963). The present article reviews the different extensions of the method to various input data and the constrained framework, while providing applicability conditions. In addition, various versions of the graphical representation of the results as a dendrogram are also presented and their properties are clarified. While some of these results can sometimes be found in an heteroclite literature, we clarify and complete them all using a uniform background. In particular, this study reveals an important distinction between a consistency property of the dendrogram and the absence of crossover within it. Finally, a simulation study shows that the constrained version of HAC can sometimes provide more relevant results than its unconstrained version despite the fact that the latter optimizes the objective criterion on a reduced set of solutions at each step. Overall, the article provides comprehensive recommandations for the use of HAC and constrained HAC depending on the input data as well as for the representation of the results.

Published
2019

6. R-miss-tastic: a unified platform for missing values methods and workflows

Author

Mayer, Imke, Sportisse, Aude, Josse, Julie, Tierney, Nicholas, and Vialaneix, Nathalie

Subjects
Statistics - Methodology
Abstract

Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and visualization methods require complete data, and improper handling of missing data results in information loss or biased analyses. Since the seminal work of Rubin (1976), a burgeoning literature on missing values has arisen, with heterogeneous aims and motivations. This led to the development of various methods, formalizations, and tools. For practitioners, it remains nevertheless challenging to decide which method is most suited for their problem, partially due to a lack of systematic covering of this topic in statistics or data science curricula. To help address this challenge, we have launched the "R-miss-tastic" platform, which aims to provide an overview of standard missing values problems, methods, and relevant implementations of methodologies. Beyond gathering and organizing a large majority of the material on missing data (bibliography, courses, tutorials, implementations), "R-miss-tastic" covers the development of standardized analysis workflows. Indeed, we have developed several pipelines in R and Python to allow for hands-on illustration of and recommendations on missing values handling in various statistical tasks such as matrix completion, estimation and prediction, while ensuring reproducibility of the analyses. Finally, the platform is dedicated to users who analyze incomplete data, researchers who want to compare their methods and search for an up-to-date bibliography, and also teachers who are looking for didactic materials (notebooks, video, slides)., Comment: 36 pages, 9 figures

Published
2019

7. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics

Author

Ambroise, Christophe, Dehman, Alia, Neuvial, Pierre, Rigaill, Guillem, and Vialaneix, Nathalie

Subjects
Mathematics - Statistics Theory, Quantitative Biology - Quantitative Methods
Abstract

Motivation: Genomic data analyses such as Genome-Wide Association Studies (GWAS) or Hi-C studies are often faced with the problem of partitioning chromosomes into successive regions based on a similarity matrix of high-resolution, locus-level measurements. An intuitive way of doing this is to perform a modified Hierarchical Agglomerative Clustering (HAC), where only adjacent clusters (according to the ordering of positions within a chromosome) are allowed to be merged. A major practical drawback of this method is its quadratic time and space complexity in the number of loci, which is typically of the order of 10^4 to 10^5 for each chromosome. Results: By assuming that the similarity between physically distant objects is negligible, we propose an implementation of this adjacency-constrained HAC with quasi-linear complexity. Our illustrations on GWAS and Hi-C datasets demonstrate the relevance of this assumption, and show that this method highlights biologically meaningful signals. Thanks to its small time and memory footprint, the method can be run on a standard laptop in minutes or even seconds. Availability and Implementation: Software and sample data are available as an R package, adjclust, that can be downloaded from the Comprehensive R Archive Network (CRAN).

Published
2019

11. A random forest approach for interval selection in functional regression.

Author

Servien, Rémi and Vialaneix, Nathalie

Subjects
*RANDOM forest algorithms, *INTERVAL analysis, *FUNCTIONAL analysis, *DATA analysis, *AGRONOMY
Abstract

In this article, we focus on the problem of variable selection in a functional regression framework. This question is motivated by practical applications in the field of agronomy: In this field, identifying the temporal periods during which weather measurements have the greatest impact on yield is critical for guiding agriculture practices in a changing environment. From a methodological point of view, our goal is to identify consecutive measurement points in the definition domain of the functional predictors, which correspond to the most important intervals for the prediction of a numeric output from the functional variables. We propose an approach based on the versatile random forest method that benefits from its good performances for variable selection and prediction. Our method builds in three steps (interval creation, summary, and selection). Different variants for each of the steps are proposed and compared on both simulated and real‐life datasets. The performances of our method compared to alternative approaches highlight its usefulness to select relevant intervals while maintaining good prediction capabilities. All variants of our method are available in the R package SISIR. [ABSTRACT FROM AUTHOR]

Published
2024
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16. Interpretable sparse SIR for functional data

Author

Picheny, Victor, Servien, Rémi, and Villa-Vialaneix, Nathalie

Subjects
Mathematics - Statistics Theory, Statistics - Methodology
Abstract

This work focuses on the issue of variable selection in functional regression. Unlike most work in this framework, our approach does not select isolated points in the definition domain of the predictors, nor does it rely on the expansion of the predictors in a given functional basis. It provides an approach to select full intervals made of consecutive points. This feature improves the interpretability of the estimated coefficients and is desirable in the functional framework for which small shifts are frequent when comparing one predictor (curve) to another. Our method is described in a semiparametric framework based on Sliced Inverse Regression (SIR). SIR is an effective method for dimension reduction of high-dimensional data which computes a linear projection of the predictors in a low-dimensional space, without loss on regression information. We extend the approaches of variable selection developed for multidimensional SIR to select intervals rather than separated evaluation points in the definition domain of the functional predictors. Different and equivalent formulations of SIR are combined in a shrinkage approach with a group-LASSO-like penalty. Finally, a fully automated iterative procedure is also proposed to find the critical (interpretable) intervals. The approach is proved efficient on simulated and real data.

Published
2016

17. Random Forests for Big Data

Author

Genuer, Robin, Poggi, Jean-Michel, Tuleau-Malot, Christine, and Villa-Vialaneix, Nathalie

Subjects
Statistics - Machine Learning, Computer Science - Learning, Mathematics - Statistics Theory
Abstract

Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include online data and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like linear regression models, clustering methods and bootstrapping schemes. Based on decision trees combined with aggregation and bootstrap ideas, random forests were introduced by Breiman in 2001. They are a powerful nonparametric statistical method allowing to consider in a single and versatile framework regression problems, as well as two-class and multi-class classification problems. Focusing on classification problems, this paper proposes a selective review of available proposals that deal with scaling random forests to Big Data problems. These proposals rely on parallel environments or on online adaptations of random forests. We also describe how related quantities -- such as out-of-bag error and variable importance -- are addressed in these methods. Then, we formulate various remarks for random forests in the Big Data context. Finally, we experiment five variants on two massive datasets (15 and 120 millions of observations), a simulated one as well as real world data. One variant relies on subsampling while three others are related to parallel implementations of random forests and involve either various adaptations of bootstrap to Big Data or to "divide-and-conquer" approaches. The fifth variant relates on online learning of random forests. These numerical experiments lead to highlight the relative performance of the different variants, as well as some of their limitations.

Published
2015

18. BEST4+ cells in the intestinal epithelium.

Author

Malonga, Tania, Vialaneix, Nathalie, and Beaumont, Martin

Subjects
*INTESTINAL mucosa, *SMALL intestine, *INTESTINES, *GENE expression, *ION channels, *GENE expression profiling, *LARGE intestine, *TRANSCRIPTOMES
Abstract

The recent development of single-cell transcriptomics highlighted the existence of a new lineage of mature absorptive cells in the human intestinal epithelium. This subpopulation is characterized by the specific expression of Bestrophin 4 (BEST4) and of other marker genes including OTOP2, CA7, GUCA2A, GUCA2B, and SPIB. BEST4+ cells appear early in development and are present in all regions of the small and large intestine at a low abundance (<5% of all epithelial cells). Location-specific gene expression profiles in BEST4+ cells suggest their functional specialization in each gut region, as exemplified by the small intestine-specific expression of the ion channel CFTR. The putative roles of BEST4+ cells include sensing and regulation of luminal pH, tuning of guanylyl cyclase-C signaling, transport of electrolytes, hydration of mucus, and secretion of antimicrobial peptides. However, most of these hypotheses lack functional validation, notably because BEST4+ cells are absent in mice. The presence of BEST4+ cells in human intestinal organoids indicates that this in vitro model should be suitable to study their role. Recent studies showed that BEST4+ cells are also present in the intestinal epithelium of macaque, pig, and zebrafish and, here, we report their presence in rabbits, which suggests that these species could be appropriate animal models to study BEST4+ cells during the development of diseases and their interactions with environmental factors such as diet or the microbiota. In this review, we summarize the existing literature regarding BEST4+ cells and emphasize the description of their predicted roles in the intestinal epithelium in health and disease. NEW & NOTEWORTHY: BEST4+ cells are a novel subtype of mature absorptive cells in the human intestinal epithelium highlighted by single-cell transcriptomics. The gene expression profile of BEST4+ cells suggests their role in pH regulation, electrolyte secretion, mucus hydration, and innate immune defense. The absence of BEST4+ cells in mice requires the use of alternative animal models or organoids to decipher the role of this novel type of intestinal epithelial cells. [ABSTRACT FROM AUTHOR]

Published
2024
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20. Statistique et Big Data Analytics; Volum\'etrie, L'Attaque des Clones

Author

Besse, Philippe and Villa-Vialaneix, Nathalie

Subjects
Statistics - Other Statistics, Computer Science - Learning, Mathematics - Statistics Theory
Abstract

This article assumes acquired the skills and expertise of a statistician in unsupervised (NMF, k-means, SVD) and supervised learning (regression, CART, random forest). What skills and knowledge do a statistician must acquire to reach the "Volume" scale of big data? After a quick overview of the different strategies available and especially of those imposed by Hadoop, the algorithms of some available learning methods are outlined in order to understand how they are adapted to the strong stresses of the Map-Reduce functionalities, Comment: in French

Published
2014

21. On-line relational SOM for dissimilarity data

Author

Olteanu, Madalina, Villa-Vialaneix, Nathalie, and Cottrell, Marie

Subjects
Statistics - Machine Learning, Computer Science - Learning
Abstract

In some applications and in order to address real world situations better, data may be more complex than simple vectors. In some examples, they can be known through their pairwise dissimilarities only. Several variants of the Self Organizing Map algorithm were introduced to generalize the original algorithm to this framework. Whereas median SOM is based on a rough representation of the prototypes, relational SOM allows representing these prototypes by a virtual combination of all elements in the data set. However, this latter approach suffers from two main drawbacks. First, its complexity can be large. Second, only a batch version of this algorithm has been studied so far and it often provides results having a bad topographic organization. In this article, an on-line version of relational SOM is described and justified. The algorithm is tested on several datasets, including categorical data and graphs, and compared with the batch version and with other SOM algorithms for non vector data., Comment: WSOM 2012, Santiago : Chile (2012)

Published
2012

22. Neural Networks for Complex Data

Author

Cottrell, Marie, Olteanu, Madalina, Rossi, Fabrice, Rynkiewicz, Joseph, and Villa-Vialaneix, Nathalie

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Learning, Statistics - Machine Learning
Abstract

Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Universit\'e Paris 1

Published
2012
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23. Consistency of functional learning methods based on derivatives

Author

Rossi, Fabrice and Villa-Vialaneix, Nathalie

Subjects
Mathematics - Statistics Theory
Abstract

In some real world applications, such as spectrometry, functional models achieve better predictive performances if they work on the derivatives of order m of their inputs rather than on the original functions. As a consequence, the use of derivatives is a common practice in Functional Data Analysis, despite a lack of theoretical guarantees on the asymptotically achievable performances of a derivative based model. In this paper, we show that a smoothing spline approach can be used to preprocess multivariate observations obtained by sampling functions on a discrete and finite sampling grid in a way that leads to a consistent scheme on the original infinite dimensional functional problem. This work extends (Mas and Pumo, 2009) to nonparametric approaches and incomplete knowledge. To be more precise, the paper tackles two difficulties in a nonparametric framework: the information loss due to the use of the derivatives instead of the original functions and the information loss due to the fact that the functions are observed through a discrete sampling and are thus also unperfectly known: the use of a smoothing spline based approach solves these two problems. Finally, the proposed approach is tested on two real world datasets and the approach is experimentaly proven to be a good solution in the case of noisy functional predictors.

Published
2011
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24. Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach

Author

Rossi, Fabrice and Villa-Vialaneix, Nathalie

Subjects
Statistics - Machine Learning, Statistics - Applications
Abstract

This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produces topologically ordered graph clusterings that lead to faithful and readable graph representations based on clustering induced graphs. Topographic graph clustering provides an alternative to more classical solutions in which a standard graph clustering method is applied to build a simpler graph that is then represented with a graph layout algorithm. A comparative study on four real world graphs ranging from 34 to 1 133 vertices shows the interest of the proposed approach with respect to classical solutions and to self-organizing maps for graphs.

Published
2010
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27. Comprendre l'organisation spatiale de l'ADN à l'aide de la statistique

Author

Neuvial, Pierre, Foissac, Sylvain, Vialaneix, Nathalie, Vialaneix, Nathalie, Martina Knoop, Stéphane Blanc, Mokrane Bouzeghoub, Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Projet SCALES financé par l'appel « Osez l’interdisciplinarité ! », CNRS, Martina Knoop, Stéphane Blanc, and Mokrane Bouzeghoub

Subjects
Bioinformatique, [STAT.AP] Statistics [stat]/Applications [stat.AP], Modélisation, [SDV.BC.IC]Life Sciences [q-bio]/Cellular Biology/Cell Behavior [q-bio.CB], ADN, [SDV.BC.IC] Life Sciences [q-bio]/Cellular Biology/Cell Behavior [q-bio.CB], Statistique
Abstract

International audience

Published
2023

33. Multi-species annotation of transcriptome and chromatin structure in domesticated animals

Author

Foissac, Sylvain, Djebali, Sarah, Munyard, Kylie, Vialaneix, Nathalie, Rau, Andrea, Muret, Kevin, Esquerré, Diane, Zytnicki, Matthias, Derrien, Thomas, Bardou, Philippe, Blanc, Fany, Cabau, Cédric, Crisci, Elisa, Dhorne-Pollet, Sophie, Drouet, Françoise, Faraut, Thomas, Gonzalez, Ignacio, Goubil, Adeline, Lacroix-Lamandé, Sonia, Laurent, Fabrice, Marthey, Sylvain, Marti-Marimon, Maria, Momal-Leisenring, Raphaelle, Mompart, Florence, Quéré, Pascale, Robelin, David, Cristobal, Magali San, Tosser-Klopp, Gwenola, Vincent-Naulleau, Silvia, Fabre, Stéphane, Pinard-Van der Laan, Marie-Hélène, Klopp, Christophe, Tixier-Boichard, Michèle, Acloque, Hervé, Lagarrigue, Sandrine, and Giuffra, Elisabetta

Published
2019
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36. R-miss-tastic: a unified platform for missing values methods and workflows

Author

Mayer, Imke, Sportisse, Aude, Josse, Julie, Tierney, Nicholas, Vialaneix, Nathalie, Charité Center for Internal Medicine and Dermatology, Clinic for Psychosomatic Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany., Modèles et algorithmes pour l’intelligence artificielle (MAASAI), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Laboratoire Jean Alexandre Dieudonné (LJAD), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Médecine de précision par intégration de données et inférence causale (PREMEDICAL), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Desbrest de santé publique (IDESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Monash university, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute of Public Health (IPH), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Institut National de Recherche en Informatique et en Automatique (Inria), Institut Desbrest de santé publique (IDESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), University of Melbourne, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Centre d'Analyse et de Mathématique sociales (CAMS), École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Modélisation en pharmacologie de population (XPOP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Interdisciplinary Institute for Artificial Intelligence (3iA Côte d’Azur), and R Consortium project (2018/2020)

Subjects
FOS: Computer and information sciences, Statistics and Probability, [STAT.AP]Statistics [stat]/Applications [stat.AP], Numerical Analysis, bibliography, guided workflows, state of the art, Methodology (stat.ME), missing data, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], teaching material, Statistics, Probability and Uncertainty, [MATH]Mathematics [math], reproducibility, statistical analysis community, Statistics - Methodology
Abstract

Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and visualization methods require complete data, and improper handling of missing data results in information loss or biased analyses. Since the seminal work of Rubin (1976), a burgeoning literature on missing values has arisen, with heterogeneous aims and motivations. This led to the development of various methods, formalizations, and tools. For practitioners, it remains nevertheless challenging to decide which method is most suited for their problem, partially due to a lack of systematic covering of this topic in statistics or data science curricula. To help address this challenge, we have launched the "R-miss-tastic" platform, which aims to provide an overview of standard missing values problems, methods, and relevant implementations of methodologies. Beyond gathering and organizing a large majority of the material on missing data (bibliography, courses, tutorials, implementations), "R-miss-tastic" covers the development of standardized analysis workflows. Indeed, we have developed several pipelines in R and Python to allow for hands-on illustration of and recommendations on missing values handling in various statistical tasks such as matrix completion, estimation and prediction, while ensuring reproducibility of the analyses. Finally, the platform is dedicated to users who analyze incomplete data, researchers who want to compare their methods and search for an up-to-date bibliography, and also teachers who are looking for didactic materials (notebooks, video, slides)., Comment: 36 pages, 9 figures

Published
2023

39. Bagged Kernel SOM

Author

Mariette, Jérôme, Olteanu, Madalina, Boelaert, Julien, Villa-Vialaneix, Nathalie, Kacprzyk, Janusz, Series editor, Villmann, Thomas, editor, Schleif, Frank-Michael, editor, Kaden, Marika, editor, and Lange, Mandy, editor

Published
2014
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43. Identification par une approche de métabolomique des voies métaboliques associées à la fragilité dans une cohorte de personnes âgées

Author

Bougel, Céline, Servien, Rémi, Vialaneix, Nathalie, Canlet, Cécile, Debrauwer, Laurent, Demuth, Ilja, Norman, Kristina, Vetter, Valentin, Dardevet, Dominique, Polakof, Sergio, Unité de Nutrition Humaine (UNH), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Clermont Auvergne (UCA), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], UNH Space Science Center [Durham] (SSC), University of New Hampshire (UNH), and Région Auvergne-Rhône-Alpes

Subjects
prévention, biomarqueurs, métabolisme, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

International audience

Published
2022

45. Which Dissimilarity Is to Be Used When Extracting Typologies in Sequence Analysis? A Comparative Study

Author

Massoni, Sébastien, Olteanu, Madalina, Villa-Vialaneix, Nathalie, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Rojas, Ignacio, editor, Joya, Gonzalo, editor, and Gabestany, Joan, editor

Published
2013
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46. Characterization of human isogenic epithelial cell lines as a relevant tool to study colon carcinogenesis and interaction between genes and environment

Author

Tête, Arnaud, Arnaud, Liana C., Le Mentec, Hélène, Gallais, Isabelle, Poupin, Nathalie, Tournadre, Noémie, Duarte-Hospital, Carolina, Lippi, Yannick, Mathevet, Fanny, Lefort, Gaëlle, Burel, Agnes, Surya, R., Boutet-Robinet, Elisa, Shay, Jerry W., Vialaneix, Nathalie, Bortoli, Sylvie, Lagadic-Gossmann, Dominique, Huc, Laurence, Vialaneix, Nathalie, Toxicité environnementale, cibles thérapeutiques, signalisation cellulaire (T3S - UMR_S 1124), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Contaminants & Stress Cellulaire (ToxAlim-COMICS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), EHESP-Irset (EHESP-Irset), École des Hautes Études en Santé Publique [EHESP] (EHESP), Métabolisme et Xénobiotiques (ToxAlim-MeX), Université Paris Descartes, Sorbonne Paris Cité, Transcriptomic impact of Xenobiotics (E23 TRiX), Plateforme Génome & Transcriptome (GET), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-ToxAlim (ToxAlim), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Biosit : biologie, santé, innovation technologique (SFR UMS CNRS 3480 - INSERM 018), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Bina Nusantara University [Jakarta], University of Texas Southwestern Medical Center, and ITMO Cancer, METAhCOL

Subjects
[SDV.CAN] Life Sciences [q-bio]/Cancer, [SDV.CAN]Life Sciences [q-bio]/Cancer, digestive system diseases
Abstract

International audience; Colorectal cancer (CRC) is the fourth most common cause of death from cancer worldwide. CRC is a multistep and progressive disease where genetic factors are important in the initiation, the development and the progression of the disease. CRC can arise from sequential steps including the acquisition of mutations in the adenomatous polyposis coli (APC), followed by the mutational activation of oncogene KRAS and the inactivation of the tumor suppressor gene, TP53. The occurrence of colorectal cancer is largely influenced by the environment, including food contaminants, lifestyle and nutrition. However, the influence of mutations on the response to environmental pollutants is poorly evaluated. Environmental carcinogenesis lacks robust models to explore the interaction between genes and environment and to determine whether genetic mutations associated with colon carcinogenesis generate a particular susceptibility to the harmful effects of pollutants. Here, we outline the characterization of an innovative cellular model that consists in 6 human isogenic epithelial colon cell lines bearing mutations on driver genes of CRC. Altogether, these cell lines recapitulate colon carcinogenesis from the healthy, preneoplastic, adenoma and carcinoma stages in a simplified way. We aim to perform a phenotypic and metabolic description of these cell lines, with a particular focus on their ability to express the battery of detoxification enzymes, to have a differential sensitivity to genotoxicity and cell death stimuli, to display a flexible energy metabolism, to migrate and grow without anchorage. We show that these models of human isogenic colonic cells of CRC could be a powerful and relevant tool to study the effects of environmental pollutants on the colorectal carcinogenesis from the early to the late and metastatic stages and to evaluate gene-environment interactions in food toxicology.

Published
2022

47. Sélection d'intervalles pour des prédicteurs fonctionnels à partir de forêts aléatoires

Author

Servien, Rémi, Vialaneix, Nathalie, and Vialaneix, Nathalie

Subjects
[STAT.AP] Statistics [stat]/Applications [stat.AP], functional regression, interval selection, random forest
Abstract

Nous nous intéressons ici au problème de sélection de variables dans un cadre de régression fonctionnelle. Le but est de sélectionner des points de mesure consécutifs afin de déterminer les intervalles importants dans la prédiction de la variable cible. Pour cela nous nous basons sur les forêts aléatoires et évaluons des variantes possibles pour trois étapes de l'approche générale que nous proposons (création de groupes, définition des résumés, sélection) que nous comparons sur des données simulées et réelles. Mots-clés. Régression fonctionnelle, sélection d'intervalles, forêts aléatoires.

Published
2022

48. ASTERICS: A Tool for the ExploRation and Integration of omiCS data

Author

Maigné, Élise, Noirot, Céline, Mariette, Jérôme, Kesewaah, Yaa, Dejean, Sébastien, Guilmineau, Camille, Henry, Julien, Krebs, Arielle, Liaubet, Laurence, Mathevet, Fanny, Hyphen-Stat, Entreprise, Gaspin, Christine, Vialaneix, Nathalie, Vialaneix, Nathalie, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Plateforme Bio-Informatique - Génotoul, Plateforme de Biostatistique (Genotoul Biostat), Génopole Toulouse Midi-Pyrénées [Auzeville] (GENOTOUL), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Mathématiques de Toulouse UMR5219 (IMT), Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université Fédérale Toulouse Midi-Pyrénées-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Génétique Physiologie et Systèmes d'Elevage (GenPhySE ), Ecole Nationale Vétérinaire de Toulouse (ENVT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Inria, INRAE, Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRAE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-BioInfOmics, Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-École nationale supérieure agronomique de Toulouse (ENSAT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Région Occitanie (Grant 20008788 – ASTERICS)

Subjects
[STAT.AP]Statistics [stat]/Applications [stat.AP], [SDV.BA.MVSA]Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health, [STAT.AP] Statistics [stat]/Applications [stat.AP], [SDV.BA.MVSA] Life Sciences [q-bio]/Animal biology/Veterinary medicine and animal Health, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]
Abstract

International audience

Published
2022

49. A Functional Density-Based Nonparametric Approach for Statistical Calibration

Author

Hernández, Noslen, Biscay, Rolando J., Villa-Vialaneix, Nathalie, Talavera, Isneri, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Bloch, Isabelle, editor, and Cesar, Roberto M., Jr., editor

Published
2010
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50. Plasma 1H-NMR metabolic and amino acid profiles of newborn piglets from two lines divergently selected for residual feed intake.

Author

Liaubet, Laurence, Guilmineau, Camille, Lefort, Gaëlle, Billon, Yvon, Reigner, Sébastien, Bailly, Jean, Marty-Gasset, Nathalie, Gress, Laure, Servien, Rémi, Bonnet, Agnès, Gilbert, Hélène, Vialaneix, Nathalie, and Quesnel, Hélène

Subjects
PIGLETS, AMINO acids, UREA, SWINE breeding, NEWBORN infants, ENERGY metabolism
Abstract

Together with environmental factors, physiological maturity at birth is a major determinant for neonatal survival and postnatal development in mammalian species. Maturity at birth is the outcome of complex mechanisms of intra-uterine development and maturation during the end of gestation. In pig production, piglet preweaning mortality averages 20% of the litter and thus, maturity is a major welfare and economic concern. Here, we used both targeted and untargeted metabolomic approaches to provide a deeper understanding of the maturity in a model of lines of pigs divergently selected on residual feed intake (RFI), previously shown to have contrasted signs of maturity at birth. Analyses were conducted on plasma metabolome of piglets at birth and integrated with other phenotypic characteristics associated to maturity. We confirmed proline and myo-inositol, previously described for their association with delayed growth, as potential markers of maturity. Urea cycle and energy metabolism were found more regulated in piglets from high and low RFI lines, respectively, suggesting a better thermoregulation ability for the low RFI (with higher feed efficiency) piglets. [ABSTRACT FROM AUTHOR]

Published
2023
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