A hierarchical Bayesian model for inference of copy number variants and their association to gene expression

A number of statistical models have been successfully developed for the analysis of high-throughput data from a single source, but few methods are available for integrating data from different sources. Here we focus on integrating gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. We specify a measurement error model that relates the gene expression levels to latent copy number states which, in turn, are related to the observed surrogate CGH measurements via a hidden Markov model. We employ selection priors that exploit the dependencies across adjacent copy number states and investigate MCMC stochastic search techniques for posterior inference. Our approach results in a unified modeling framework for simultaneously inferring copy number variants (CNV) and identifying their significant associations with mRNA transcripts abundance. We show performance on simulated data and illustrate an application to data from a genomic study on human cancer cell lines.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS705 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Reflexões sobre política de formação e valorização do professor na contemporaneidade brasileira

Este artigo resulta de uma pesquisa em nível de doutorado que teve por objetivo investigar a problemática da meritocracia na profissão docente. O tema da política de formação e valorização docente no Brasil contemporâneo deve ser tratado como um objeto complexo que exige uma observação e análise panorâmica ancorado em teorias fundantes, do contrário, a análise do tema desloca-se para um ponto cego, transformando um debate específico em matrizes gerais de explicação, que no limite cumpre o papel formal, mas não delineia apontamentos significantes. A pesquisa fez uso de levantamento bibliográfico que buscou refletir sobre os determinismos econômicos para o campo da educação utilizando para isso o conceito de tirania de Bourdieu (2001). Adentrando para o campo específico da determinação econômica para o campo da formação docente foi utilizado o conceito de refração do mesmo autor. E num último momento far-se-á uma análise do tema valorização docente no Brasil contemporâneo na perspectiva teórica do mérito

A Bayesian Nonparametric model for textural pattern heterogeneity

Cancer radiomics is an emerging discipline promising to elucidate lesion phenotypes and tumor heterogeneity through patterns of enhancement, texture, morphology, and shape. The prevailing technique for image texture analysis relies on the construction and synthesis of Gray-Level Co-occurrence Matrices (GLCM). Practice currently reduces the structured count data of a GLCM to reductive and redundant summary statistics for which analysis requires variable selection and multiple comparisons for each application, thus limiting reproducibility. In this article, we develop a Bayesian multivariate probabilistic framework for the analysis and unsupervised clustering of a sample of GLCM objects. By appropriately accounting for skewness and zero-inflation of the observed counts and simultaneously adjusting for existing spatial autocorrelation at nearby cells, the methodology facilitates estimation of texture pattern distributions within the GLCM lattice itself. The techniques are applied to cluster images of adrenal lesions obtained from CT scans with and without administration of contrast. We further assess whether the resultant subtypes are clinically oriented by investigating their correspondence with pathological diagnoses. Additionally, we compare performance to a class of machine-learning approaches currently used in cancer radiomics with simulation studies.Comment: 45 pages, 7 figures, 1 Tabl

A Bayesian nonparametric approach for the analysis of multiple categorical item responses

We develop a modeling framework for joint factor and cluster analysis of datasets where multiple categorical response items are collected on a heterogeneous population of individuals. We introduce a latent factor multinomial probit model and employ prior constructions that allow inference on the number of factors as well as clustering of the subjects into homogeneous groups according to their relevant factors. Clustering, in particular, allows us to borrow strength across subjects, therefore helping in the estimation of the model parameters, particularly when the number of observations is small. We employ Markov chain Monte Carlo techniques and obtain tractable posterior inference for our objectives, including sampling of missing data. We demonstrate the effectiveness of our method on simulated data. We also analyze two real-world educational datasets and show that our method outperforms state-of-the-art methods. In the analysis of the real-world data, we uncover hidden relationships between the questions and the underlying educational concepts, while simultaneously partitioning the students into groups of similar educational mastery

A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data

In this paper we propose a unified, probabilistically coherent framework for the analysis of task-related brain activity in multi-subject fMRI experiments. This is distinct from two-stage “group analysis” approaches traditionally considered in the fMRI literature, which separate the inference on the individual fMRI time courses from the inference at the population level. In our modeling approach we consider a spatiotemporal linear regression model and specifically account for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject nonparametric variable selection prior. For posterior inference, in addition to Markov chain Monte Carlo sampling algorithms, we develop suitable variational Bayes algorithms. We show on simulated data that variational Bayes inference achieves satisfactory results at more reduced computational costs than using MCMC, allowing scalability of our methods. In an application to data collected to assess brain responses to emotional stimuli our method correctly detects activation in visual areas when visual stimuli are presented

Time-varying ℓ0\ell_0 optimization for Spike Inference from Multi-Trial Calcium Recordings

Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying $\ell_0$ penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate function across trials due to learning

CURRÍCULO E DISTINÇÃO DOCENTE: UM ESTUDO SOBRE OS PROJETOS VENCEDORES DO PRÊMIO PROFESSORES DO BRASIL COMO PRÁTICA CURRICULAR DE DISTINÇÃO DOCENTE

Este trabalho é fruto de pesquisa que buscou investigar quarenta projetos vencedores do Prêmio Professores do Brasil, prêmio este atribuído pelo Ministério da Educação – órgão regulador oficial da educação – anualmente aos professores da rede pública, conferindo distinção a estes. Tal investigação buscou problematizar como o trabalho docente considerado digno de distinção é aquele que consegue “criativa e heroicamente” criar um currículo próprio a ser seguido pelo professor durante o ano letivo. Os trabalhos foram pesquisados de quatro formas; em primeiro lugar por meio do site oficial do prêmio onde registra os resumos dos trabalhos; em segundo lugar mediante documento impresso oficial do próprio Ministério da Educação; em terceiro lugar, em fita VHS, onde os ganhadores relatam sobre seus projetos e finalmente por intermédio de recortes de jornais das cidades dos professores premiados que relataram sobre os prêmios. Em suma, a distinção atribuída pelo Ministério da Educação aos professores premiados com o Prêmio Professores do Brasil está baseada na realização de projetos desenvolvidos em condições limite. Condições estas como: falta de estrutura, escasso material e recurso de trabalhos, mas que os professores premiados de maneira “criativa” desenvolvem suas práticas pedagógicas, definidas oportunamente pela organização do Prêmio como “pedagogia do possível”. A “pedagogia do possível” se confunde com proposta curricular e a cada ano de premiação firma-se como “saída” para o trabalho docente. A relevância deste trabalho está em contribuir para a discussão de políticas públicas observando o currículo e a prática docente como eixo que se retroalimenta