ZüKoSt: Seminar on Applied Statistics

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Spring Semester 2013

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* 21 February 2013
16:15-17:15

Mark van de Wiel
VU University Medical Center, Amsterdam

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ZueKoSt: Seminar on Applied Statistics

Title	Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors
Speaker, Affiliation	Mark van de Wiel, VU University Medical Center, Amsterdam
Date, Time	21 February 2013, 16:15-17:15
Location	HG G 19.2
Abstract	Next generation sequencing is quickly replacing microarrays as a technique to probe different molecular levels of the cell, such as DNA or mRNA. The technology has the advantage to provide higher resolution, while reducing biases, in particular at the lower end of the spectrum. mRNA sequencing (RNAseq) data consist in counts of pieces of RNA called tags. This type of data imposes new challenges for statistical analysis. We present a novel approach to model and analyze these data. Method and softwares for differential expression analysis usually use a generalization of the Poisson or Binomial distribution that accounts for overdispersion. A popular choice is the negative binomial (i.e. Poisson-Gamma) model. However, there is no consensus on what model fits best to RNAseq data, and this may depend on the technology used. With RNAseq, the number of features vastly exceeds the sample size. This implies that shrinkage of variance-related parameters may lead to more stable estimates and inference. Methods to do so are available, but only for a single parameter and in the context of restrictive study designs, e.g. two-group comparisons or fixed-effect designs. We present a Bayesian framework that allows for a) various count models b) flexible designs c) random effects and d) multi-parameter shrinkage. The latter is implemented using Empirical Bayes principles by several procedures that estimate hyper-parameters of (mixture) priors or nonparametric priors. Moreover, the framework provides Bayesian multiplicity correction, thereby providing solid inference. In data-based simulations, we show that our method outperforms other popular methods (edgeR, DESeq, baySeq, NOISeq). Moreover, we illustrate our approach on three data sets. The first is a CAGE data set containing 25 samples representing five regions of the human brain from seven individuals. The design is incomplete and a batch effect is present. The data motivates use of the zero-inflated negative binomial as a powerful alternative to the negative binomial, because it leads to less bias of the overdispersion parameter and improved detection power for the low-count tags. The second is a large, standard two-sample RNAseq data set that we repeatedly split into a small data set and its large complement. Compared to other methods, our results from the small sample data sets validate much better on their large sample complements, illustrating the importance of the type of shrinkage. The methodology and these results are available in Van de Wiel et al. (2012). The framework is not restricted to RNAseq data nor to differential expression analysis. It is currently being extended towards analysis of proteomics, microRNAs, methylation, and high-throughput screening data. In addition, we currently study multivariate, graphical applications using Bayesian ridge regression. If time permits, some of these extensions will be discussed. The R software package, termed `ShrinkBayes', is build upon INLA, which provides the machinery for computing marginal posteriors in a variety of models. Co-authors: Gwenael Leday (i), Luba Pardo (iii), Havard Rue (iv), Aad van der Vaart (ii), Wessel van Wieringen (i,ii) Affiliations: i. Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam ii. Department of Mathematics, VU University, Amsterdam iii. Department of Clinical Genetics, VU University Medical Center, Amsterdam iv. Department of Mathematical Sciences, Norwegian University for Science and Technology, Trondheim, Norway Reference: Van de Wiel MA, Leday GGR, Pardo L, Rue H, Van der Vaart AW, Van Wieringen WN (2012). Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priors. Biostatistics, 14, 113-128

Bayesian analysis of RNA sequencing data by estimating multiple shrinkage priorsread_more

HG G 19.2

28 February 2013
16:15-17:15

Steve Smith
University of Oxford

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ZueKoSt: Seminar on Applied Statistics

Title	Brain network modelling from resting-state Functional MRI data: More than just correlations?
Speaker, Affiliation	Steve Smith, University of Oxford
Date, Time	28 February 2013, 16:15-17:15
Location	HG G 19.1
Abstract	For nearly 20 years researchers have been studying spontaneous correlations in the brain with resting-state Functional MRI. However, most work has simply characterised "spatial maps" of correlation, rather than attempting to precisely model brain networks. More recently there has been increased interest in attempting to define functional network nodes, estimate what are the direct connections between these (as opposed to potentially indirect correlations), and even to estimate causality (directionality). Approaches such as Bayes Nets (graphical models), Granger causality and neurobiological Bayesian modelling have been proposed. Some of these approaches seem reasonable, while others seem doomed to failure. I will discuss the issues of network modelling specific to such data, discuss some of the various methods that are being developed for brain network modelling, and present some exciting new data, coming out of the Human Connectome Project.

Brain network modelling from resting-state Functional MRI data: More than just correlations?read_more

HG G 19.1

11 April 2013
16:15-17:15

Thomas Kneib
Universität Göttingen

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ZueKoSt: Seminar on Applied Statistics

Title	Beyond Mean Regression
Speaker, Affiliation	Thomas Kneib, Universität Göttingen
Date, Time	11 April 2013, 16:15-17:15
Location	HG G 19.1
Abstract	Usual exponential family regression models focus on only one designated quantity of the response distribution, namely the mean.While this entails easy interpretation of the estimated regression effects,it may often lead to incomplete analyses when more complex relationships are indeed present and also bears the risk of false conclusions about the significance / importance of covariates. We will therefore give an overview on extended types of regression models that allow us to go beyond mean regression. More specifically, we will study generalized additive models for location, scale and shape as well as semiparametric quantile and expectile regression.

Beyond Mean Regressionread_more

HG G 19.1

25 April 2013
16:15-17:15

David Ginsbourger
Universität Bern

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ZueKoSt: Seminar on Applied Statistics

Title	Gaussian field models for the adaptive design of costly experiments
Speaker, Affiliation	David Ginsbourger, Universität Bern
Date, Time	25 April 2013, 16:15-17:15
Location	HG G 19.1
Abstract	Gaussian field models have become commonplace in the design and analysis of costly experiments. Thanks to convenient properties of associated conditional distributions (Gaussianity, interpolation in the case of deterministic responses, etc.), Gaussian field models not only allow predicting black-box responses for untried input configurations, but can also be used as a basis for evaluation strategies dedicated to optimization, inversion, uncertainty quantification, probability of failure estimation, and more. After an introduction to Gaussian field modeling and some of its popular applications in adaptive design of deterministic numerical experiments, we will present two recent contributions. First, an extension of the Expected Improvement criterion dedicated to Monte-Carlo simulations with controlled precision will be presented, with application to an online resource allocation problem in safety engineering. Second, we will focus on a high-dimensional application of Gaussian field modeling to an inversion problem in water sciences, where an original non-stationary covariance kernel relying on fast proxy simulations is used.

Gaussian field models for the adaptive design of costly experimentsread_more

HG G 19.1

16 May 2013
16:15-17:15

Carolin Strobl
Universität Zürich

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ZueKoSt: Seminar on Applied Statistics

Title	Detecting Differential Item Functioning in Psychological Tests
Speaker, Affiliation	Carolin Strobl, Universität Zürich
Date, Time	16 May 2013, 16:15-17:15
Location	HG G 19.1
Abstract	The main aim of educational and psychological testing is to provide a means for objective and fair comparisons between the test takers. However, in practice a phenomenon called differential item functioning (DIF) can lead to an unfair advantage or disadvantage for certain groups of test takers. A variety of statistical methods has been suggested for detecting DIF in the Rasch model, that is used increasingly in educational and psychological testing. However, most of these methods are designed for the comparison of pre-specified focal and reference groups, such as males and females, whereas the actual groups of advantaged or disadvantaged test takers may be formed by (complex interactions of) several covariates, as in the case of females up to a certain age. In this talk a new method for DIF detection based on model-based recursive partitioning is presented that can detect groups of test takers exhibiting DIF in a data-driven way. The talk outlines the statistical methodology behind the new approach as well as its practical application by means of an illustrative example.

Detecting Differential Item Functioning in Psychological Testsread_more

HG G 19.1

23 May 2013
16:15-17:15

Yves Rozenholc
University Paris Descartes, Paris

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ZueKoSt: Seminar on Applied Statistics

Title	Laplace deconvolution in Regression - Application to angiogenosis follow-up in cancer
Speaker, Affiliation	Yves Rozenholc, University Paris Descartes, Paris
Date, Time	23 May 2013, 16:15-17:15
Location	HG G 19.1
Abstract	In the context of anti-angiogenic cancer treatments, a major issue is to follow the drug effect. If parametric models have been developed to achieve this goal, they suffer from being tissue-related and, moreover, if their pertinence is already questionable in heathy tissue, they are certainly wrong in tumors where the cell growth changes the nature of the tissue. In order to face these problems, nonparametric modeling of the blood flow exchanges has been imagined early in the 80's and started to be used in the second half of the 90's with the availability of high-frequency imaging techniques. Unfortunately, to date the estimation in such nonparametric models is highly unstable due to high level of ill-posedness. After recalling the medical context which has motivated our study and describing the associated models, I will present two new nonparametric estimators for Laplace deconvolution in the regression setting. The first estimator is derived from the statistical analysis of Volterra equations of the first type intimately linked to Laplace deconvolution. This point-wised estimate is shown to be adaptive in the sense that it achieves optimal rates of convergence up to the regularity of the unknown function even if this regularity is also unknown. Because this estimator needs the knowledge of the roots of a polynomial, it remains hardly usable from a practical point of view. The second estimator relies on a decomposition of the functions of interest on the basis of the Laplace functions. This global estimator tuned by model selection satisfies an oracle inequality and is easily implementable. This theoretical study is completed by simulations which show the proper behavior of this two estimators. Collaboration with Charles-A. Cuénod (MD-PhD), Felix Abramovich, Fabienne Comte and Marianna Pensky.

Laplace deconvolution in Regression - Application to angiogenosis follow-up in cancerread_more

HG G 19.1

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Organisers: Peter Bühlmann, Reinhard Furrer, Leonhard Held, Markus Kalisch, Hans Rudolf Künsch, Marloes Maathuis, Martin Mächler, Lukas Meier, Mark D. Robinson, Werner Stahel, Carolin Strobl, Sara van de Geer

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