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1. A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events.

Author

Jia Li, Marie-Anne Poursat, Damien Drubay, Arnaud Motz, Zohra Saci, Antonin Morillon, Stefan Michiels, and Daniel Gautheret

Subjects
Biology (General), QH301-705.5
Abstract

We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome.

Published
2015
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2. An iterative algorithm for joint covariate and random effect selection in mixed effects models

Author

Maud Delattre, Marie-Anne Poursat, Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-AgroParisTech-Université Paris-Saclay, Statistique mathématique et apprentissage (CELESTE), 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)-Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Laboratoire de Mathématiques d'Orsay (LMO), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Mathématiques et Informatique Appliquées (MIA Paris-Saclay), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Statistics and Probability, FOS: Computer and information sciences, Mathematical optimization, Stepwise procedure, Iterative method, 030226 pharmacology & pharmacy, 01 natural sciences, Set (abstract data type), Methodology (stat.ME), 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Bayesian information criterion, Covariate, 0101 mathematics, Nonlinear mixedeffects models, Selection (genetic algorithm), Statistics - Methodology, Model selection, Probability and statistics, General Medicine, Random effects model, [STAT]Statistics [stat], Joint covariate and random effects selection, Nonlinear mixed effects models, Statistics, Probability and Uncertainty
Abstract

We consider joint selection of fixed and random effects in general mixed-effects models. The interpretation of estimated mixed-effects models is challenging since changing the structure of one set of effects can lead to different choices of important covariates in the model. We propose a stepwise selection algorithm to perform simultaneous selection of the fixed and random effects. It is based on Bayesian Information criteria whose penalties are adapted to mixed-effects models. The proposed procedure performs model selection in both linear and nonlinear models. It should be used in the low-dimension setting where the number of ovariates and the number of random effects are moderate with respect to the total number of observations. The performance of the algorithm is assessed via a simulation study, which includes also a comparative study with alternatives when available in the literature. The use of the method is illustrated in the clinical study of an antibiotic agent kinetics.

Published
2020

3. Multinomial Logistic Model for Coinfection Diagnosis Between Arbovirus and Malaria in Kedougou

Author

Abdourahmane Sow, Cheikh Loucoubar, Elisabeth Gassiat, Amadou A. Sall, Mor Absa Loum, Marie-Anne Poursat, Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Statistique mathématique et apprentissage (CELESTE), 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)-Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Arbovirus et Virus de Fièvres Hémorragiques [Dakar, Sénégal], Institut Pasteur de Dakar, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Biostatistique, Bio-informatique et Modélisation - Biostatistics, Bioinformatics and Modelling Group [Dakar], Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Réseau International des Instituts Pasteur (RIIP), Laboratoire de Mathématiques d'Orsay (LM-Orsay), and Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)

Subjects
FOS: Computer and information sciences, Male, Plasmodium, Databases, Factual, Multinomial logistic model, Disease, Antibodies, Viral, 01 natural sciences, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Multinomial logistic regression, [STAT.AP]Statistics [stat]/Applications [stat.AP], Endemic area, General Medicine, Senegal, 3. Good health, [STAT]Statistics [stat], Coinfection, Female, Statistics, Probability and Uncertainty, [STAT.ME]Statistics [stat]/Methodology [stat.ME], variable selection, Statistics and Probability, Arboviral disease, 030231 tropical medicine, malaria, Machine Learning (stat.ML), Mathematics - Statistics Theory, Statistics Theory (math.ST), Arbovirus Infections, Biostatistics, Statistics - Applications, Arbovirus, 03 medical and health sciences, random forest classification, Predictive Value of Tests, 0103 physical sciences, FOS: Mathematics, medicine, Humans, Applications (stat.AP), Computer Simulation, 010306 general physics, business.industry, variableselection, medicine.disease, coinfection, Logistic Models, arbovirus, Immunoglobulin M, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, multinomial logistic regression, Malaria, Demography
Abstract

In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The co-circulation of malaria parasites and arboviruses can explain the observation of coinfected cases. Indeed there is strong resemblance in symptoms between these diseases making problematic targeted medical care of coinfected cases. This is due to the fact that the origin of illness is not obviously known. Some cases could be immunized against one or the other of the pathogens, immunity typically acquired with factors like age and exposure as usual for endemic area. Thus, coinfection needs to be better diagnosed. Using data collected from patients in Kedougou region, from 2009 to 2013, we adjusted a multinomial logistic model and selected relevant variables in explaining coinfection status. We observed specific sets of variables explaining each of the diseases exclusively and the coinfection. We tested the independence between arboviral and malaria infections and derived coinfection probabilities from the model fitting. In case of a coinfection probability greater than a threshold value to be calibrated on the data, long duration of illness and age are mostly indicative of arboviral disease while high body temperature and presence of nausea or vomiting symptoms during the rainy season are mostly indicative of malaria disease.

Published
2019

4. High-resolution linkage map for two honeybee chromosomes: the hotspot quest

Author

Florence Mougel, Marie-Anne Poursat, Michel Solignac, Nicolas Beaume, and Dominique Vautrin

Subjects
Genetic Markers, Recombination, Genetic, Genetics, Genome, Genetic Linkage, FLP-FRT recombination, Non-allelic homologous recombination, Chromosome Mapping, General Medicine, Bees, Biology, Genetic recombination, chemistry.chemical_compound, chemistry, Evolutionary biology, Linear Models, Animals, Ectopic recombination, Nucleotide Motifs, Homologous recombination, Molecular Biology, Recombination, DNA
Abstract

Meiotic recombination is a fundamental process ensuring proper disjunction of homologous chromosomes and allele shuffling in successive generations. In many species, this cellular mechanism occurs heterogeneously along chromosomes and mostly concentrates in tiny fragments called recombination hotspots. Specific DNA motifs have been shown to initiate recombination in these hotspots in mammals, fission yeast and drosophila. The aim of this study was to check whether recombination also occurs in a heterogeneous fashion in the highly recombinogenic honeybee genome and whether this heterogeneity can be connected with specific DNA motifs. We completed a previous picture drawn from a routine genetic map built with an average resolution of 93 kb. We focused on the two smallest honeybee chromosomes to increase the resolution and even zoomed at very high resolution (3.6 kb) on a fragment of 300 kb. Recombination rates measured in these fragments were placed in relation with occurrence of 30 previously described motifs through a Poisson regression model. A selection procedure suitable for correlated variables was applied to keep significant motifs. These fine and ultra-fine mappings show that recombination rate is significantly heterogeneous although poorly contrasted between high and low recombination rate, contrarily to most model species. We show that recombination rate is probably associated with the DNA methylation state. Moreover, three motifs (CGCA, GCCGC and CCAAT) are good candidates of signals promoting recombination. Their influence is however moderate, doubling at most the recombination rate. This discovery extends the way to recombination dissection in insects.

Published
2013

5. A Dual Model for Prioritizing Cancer Mutations in the Non-coding Genome Based on Germline and Somatic Events

Author

Marie-Anne Poursat, Damien Drubay, Arnaud Motz, Stefan Michiels, Zohra Saci, Daniel Gautheret, Antonin Morillon, Jia Li, Institut de Biologie Intégrative de la Cellule (I2BC), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mathématiques d'Orsay (LM-Orsay), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Service de biostatistique et d'épidémiologie (SBE), Direction de la recherche clinique [Gustave Roussy], Institut Gustave Roussy (IGR)-Institut Gustave Roussy (IGR), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), Dynamique de l'information génétique : bases fondamentales et cancer (DIG CANCER), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), Méthodologie et épidémiologie clinique en oncologie moléculaire (U1018 (Équipe 2)), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Gustave Roussy (IGR), Institut de Biologie Intégrative de la Cellule ( I2BC ), Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Sud - Paris 11 ( UP11 ), Laboratoire de Mathématiques d'Orsay ( LM-Orsay ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Service de biostatistique et d'épidémiologie ( SBE ), Institut Gustave Roussy ( IGR ) -Institut Gustave Roussy ( IGR ), Centre de recherche en épidémiologie et santé des populations ( CESP ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ) -Université Paris-Sud - Paris 11 ( UP11 ) -Assistance publique - Hôpitaux de Paris (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale ( INSERM ), Dynamique de l'information génétique : bases fondamentales et cancer ( DIG CANCER ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -INSTITUT CURIE-Centre National de la Recherche Scientifique ( CNRS ), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay, Séquence, Structure et Fonction des ARN (SSFA), Département Biologie des Génomes (DBG), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Institut de Biologie Intégrative de la Cellule (I2BC), Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut Curie-Centre National de la Recherche Scientifique (CNRS), Institut Curie-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Paris-Sud - Paris 11 (UP11)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris-Sud - Paris 11 (UP11)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-Université Pierre et Marie Curie - Paris 6 (UPMC), and HAL UPMC, Gestionnaire

Subjects
Cancer genome sequencing, Genome evolution, Mutation rate, RNA, Untranslated, QH301-705.5, [SDV]Life Sciences [q-bio], Biology, medicine.disease_cause, Genome, Cellular and Molecular Neuroscience, Germline mutation, Neoplasms, [SDV.BDD] Life Sciences [q-bio]/Development Biology, Genetics, medicine, Humans, Biology (General), Molecular Biology, [SDV.BDD]Life Sciences [q-bio]/Development Biology, Ecology, Evolution, Behavior and Systematics, ComputingMilieux_MISCELLANEOUS, Mutation, Ecology, [ SDV ] Life Sciences [q-bio], Models, Genetic, Genome, Human, Computational Biology, Genome project, Sequence Analysis, DNA, 3. Good health, [SDV] Life Sciences [q-bio], Computational Theory and Mathematics, Modeling and Simulation, Human genome, Research Article
Abstract

We address here the issue of prioritizing non-coding mutations in the tumoral genome. To this aim, we created two independent computational models. The first (germline) model estimates purifying selection based on population SNP data. The second (somatic) model estimates tumor mutation density based on whole genome tumor sequencing. We show that each model reflects a different set of constraints acting either on the normal or tumor genome, and we identify the specific genome features that most contribute to these constraints. Importantly, we show that the somatic mutation model carries independent functional information that can be used to narrow down the non-coding regions that may be relevant to cancer progression. On this basis, we identify positions in non-coding RNAs and the non-coding parts of mRNAs that are both under purifying selection in the germline and protected from mutation in tumors, thus introducing a new strategy for future detection of cancer driver elements in the expressed non-coding genome., Author Summary Cancer cells undergo a mutation/selection process that resembles that of any living cell. Most mutations in cancer cell DNA occur in the so-called "non-coding" regions that represent 98.5% of the genome length. Pinning down which of these mutations contribute to the fitness of cancer cells would be important for identifying new "cancer drivers", which may in turn lead to future treatments. Unfortunately, predicting the impact of a non-coding DNA alteration remains extremely difficult. In this study, we analyze millions of non-coding cancer mutations and show cancer-specific mutational patterns can be used to predict non-coding regions that are preserved from mutations and may thus be important for cancer cell survival. Combining this information with population data, we propose a new scoring system that should help prioritize important non-coding mutations in future studies.

Published
2015

6. Statistical Tools for Nonlinear Regression : A Practical Guide with S-PLUS Examples

Author

Sylvie Huet, Anne Bouvier, Marie-Anne Poursat, Emmanuel Jolivet, Sylvie Huet, Anne Bouvier, Marie-Anne Poursat, and Emmanuel Jolivet

Subjects
Regression analysis, Nonlinear theories, Parameter estimation
Abstract

If you need to analyze a data set using a parametric nonlinear regression model, if you are not on familiar terms with statistics and software, and if you make do with S-PLUS, this book is for you. In each chapter we start by presenting practical examples. We then describe the problems posed by these examples in terms of statistical problems, and we demonstrate how to solve these problems. Finally, we apply the proposed methods to the example data sets. You will not find any mathematical proofs here. Rather, we try when possible to explain the solutions using intuitive arguments. This is really a cook book. Most of the methods proposed in the book are derived from classical nonlinear regression theory, but we have also made attempts to provide you with more modern methods that have proved to perform well in practice. Although the theoretical grounds are not developed here, we give, when appropriate, some technical background using a sans serif type style. You can skip these passages if you are not interested in this information. The first chapter introduces several examples, from experiments in agron­ omy and biochemistry, to which we will return throughout the book. Each example illustrates a different problem, and we show how to methodically handle these problems by using parametric nonlinear regression models.

Published
2013

7. A note on BIC in mixed-effects models

Author

Marc Lavielle, Marie-Anne Poursat, Maud Delattre, Mathématiques et Informatique Appliquées (MIA-Paris), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Modélisation en pharmacologie de population (POPIX), 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), Laboratoire de Mathématiques d'Orsay (LM-Orsay), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Innovative Medicines Initiative Joint [115156], European Union, EFPIA, Academic and SME partners, FMJH, and labex LMH [ANR-10-CAMP-0151-02, ANR-11-LABX-0056-LMH]

Subjects
SELECTION, Statistics and Probability, Mixed model, Mathematical optimization, Feature selection, Bayesian Information Criterion, BIC, mixed effects model, variable selection, CONDITIONAL AKAIKE INFORMATION, LONGITUDINAL DATA, APPROXIMATION, CONVERGENCE, ALGORITHM, Bayesian information criterion, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Convergence (routing), Statistics, 62J02, Mathematics, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Random effects model, Expression (mathematics), Statistics::Computation, Deviance information criterion, Sample size determination, 62J12, Statistics, Probability and Uncertainty
Abstract

International audience; The Bayesian Information Criterion (BIC) is widely used for variable selection in mixed effects models. However, its expression is unclear in typical situations of mixed effects models, where simple definition of the sample size is not meaningful.We derive an appropriate BIC expression that is consistent with the random effect structure of the mixed effects model. We illustrate the behavior of the proposed criterion through a simulation experiment and a case study and we recommend its use as an alternative to various existing BIC versions that are implemented in available software.

Published
2014

8. Influence function for robust phylogenetic reconstructions

Author

Avner Bar-Hen, Marie-Anne Poursat, Philippe Vandenkoornhuyse, Mahendra Mariadassou, Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), GIS Génomique Marine Fondation Total Programme ANR ECCO, Ecole thématique Génomique Environnementale, Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Centre National de la Recherche Scientifique (CNRS), Mathématiques Appliquées à Paris 5 ( MAP5 - UMR 8145 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National des Sciences Mathématiques et de leurs Interactions-Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Mathématiques d'Orsay ( LMO ), Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Ecosystèmes, biodiversité, évolution [Rennes] ( ECOBIO ), Université de Rennes 1 ( UR1 ), and Université de Rennes ( UNIV-RENNES ) -Université de Rennes ( UNIV-RENNES ) -INEE-Observatoire des Sciences de l'Univers de Rennes ( OSUR ) -Centre National de la Recherche Scientifique ( CNRS )

Subjects
Statistics as Topic, Context (language use), Biology, Bioinformatics, Network topology, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 01 natural sciences, 010104 statistics & probability, 03 medical and health sciences, tree stability, [ SDU.ENVI ] Sciences of the Universe [physics]/Continental interfaces, environment, Genetics, Statistical inference, 0101 mathematics, maximum likelihood, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Molecular Biology, Phylogeny, Ecology, Evolution, Behavior and Systematics, Topology (chemistry), 030304 developmental biology, Influence function, Likelihood Functions, 0303 health sciences, Phylogenetic tree, business.industry, Fungi, Pattern recognition, Biological Evolution, Data set, Tree (data structure), Data Interpretation, Statistical, Outlier, Artificial intelligence, phylogenetic, business, Software, [ SDV.BID.SPT ] Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy
Abstract

Based on the computation of the influence function, a tool to measure the impact of each piece of sampled data on the statistical inference of a parameter, we propose to analyze the support of the maximum-likelihood (ML) tree for each site. We provide a new tool for filtering data sets (nucleotides, amino acids, and others) in the context of ML phylogenetic reconstructions. Because different sites support different phylogenic topologies in different ways, outlier sites, that is, sites with a very negative influence value, are important: they can drastically change the topology resulting from the statistical inference. Therefore, these outlier sites must be clearly identified and their effects accounted for before drawing biological conclusions from the inferred tree. A matrix containing 158 fungal terminals all belonging to Chytridiomycota, Zygomycota, and Glomeromycota is analyzed. We show that removing the strongest outlier from the analysis strikingly modifies the ML topology, with a loss of as many as 20% of the internal nodes. As a result, estimating the topology on the filtered data set results in a topology with enhanced bootstrap support. From this analysis, the polyphyletic status of the fungal phyla Chytridiomycota and Zygomycota is reinforced, suggesting the necessity of revisiting the systematics of these fungal groups. We show the ability of influence function to produce new evolution hypotheses.

Published
2008

9. Statistical Tools for Nonlinear Regression : A Practical Guide With S-PLUS and R Examples

Author

Sylvie Huet, Anne Bouvier, Marie-Anne Poursat, Emmanuel Jolivet, Sylvie Huet, Anne Bouvier, Marie-Anne Poursat, and Emmanuel Jolivet

Subjects
Statistics, Biometry
Abstract

Statistical Tools for Nonlinear Regression, Second Edition, presents methods for analyzing data using parametric nonlinear regression models. The new edition has been expanded to include binomial, multinomial and Poisson non-linear models. Using examples from experiments in agronomy and biochemistry, it shows how to apply these methods. It concentrates on presenting the methods in an intuitive way rather than developing the theoretical backgrounds. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Its main advantages are to make the model building, estimation and validation tasks, easy to do. More precisely, Complex models can be easily described using a symbolic syntax. The regression function as well as the variance function can be defined explicitly as functions of independent variables and of unknown parameters or they can be defined as the solution to a system of differential equations. Moreover, constraints on the parameters can easily be added to the model. It is thus possible to test nested hypotheses and to compare several data sets. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap. Some graphical tools are proposed for visualizing the fitted curves, the residuals, the confidence regions, and the numerical estimation procedure.

Published
2004

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