Biomedical subjects

Marina Vannucci

Publications and source records attributed to Marina Vannucci.

9 recordsLinked to original sources

PubMed2025

BISON: bi-clustering of spatial omics data with feature selection.

MOTIVATION: The advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes (DGs) that elucidate the clustering result, referred to as spatial domain-specific DGs. Existing methods for identifying these genes typically rely on a two-stage approach, which can lead to the phenomenon known as double-dipping. RESULTS: To address the challenge, we propose a unified Bayesian latent block model that simultaneously detects a list of DGs contributing to spatial domain identification while clustering these DGs and spatial locations. The efficacy of our proposed method is validated through a series of simulation experiments, and its capability to identify DGs is demonstrated through applications to benchmark SRT datasets. AVAILABILITY AND IMPLEMENTATION: The R/C++ implementation of BISON is available at https://github.com/new-zbc/BISON.

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PubMed2006

Detecting protein dissimilarities in multiple alignments using Bayesian variable selection.

MOTIVATION: We present an application of Bayesian variable selection to the novel detection of sequence elements that confer negative design to protein structure and function. As an illustration, we analyze the different dimer interfaces between the CXCL8 chemokine family with the CCL4 and CCL2 chemokine families to discover the changes that disfavor CXCL8 of quaternary structure. RESULTS: In comparison with known experimental results, our method identifies evolutionarily conserved sequence changes in the CC families that inhibit CXCL8 quaternary structure. Therefore, we find positive selection of negative design elements. Furthermore, our approach predicts that a two-residue deletion conserved in the CCL4 chemokine family disfavors CXCL8 dimerization. AVAILABILITY: The Matlab code for the Bayesian variable selection is freely available at http://stat.tamu.edu/~mvannucci/webpages/codes.html

Algorithms↗

PubMed2006

Bayesian variable selection for the analysis of microarray data with censored outcomes.

MOTIVATION: A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identification of relevant markers by jointly assessing sets of genes. We consider accelerated failure time (AFT) models with log-normal and log-t distributional assumptions. A data augmentation approach is used to impute the failure times of censored observations and mixture priors are used for the regression coefficients to identify promising subsets of variables. The proposed method provides a unified procedure for the selection of relevant genes and the prediction of survivor functions. RESULTS: We demonstrate the performance of the method on simulated examples and on several microarray datasets. For the simulation study, we consider scenarios with large number of noisy variables and different degrees of correlation between the relevant and non-relevant (noisy) variables. We are able to identify the correct covariates and obtain good prediction of the survivor functions. For the microarray applications, some of our selected genes are known to be related to the diseases under study and a few are in agreement with findings from other researchers. AVAILABILITY: The Matlab code for implementing the Bayesian variable selection method may be obtained from the corresponding author. CONTACT: mvannucci@stat.tamu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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PubMed2005

Wavelet thresholding with bayesian false discovery rate control.

The false discovery rate (FDR) procedure has become a popular method for handling multiplicity in high-dimensional data. The definition of FDR has a natural Bayesian interpretation; it is the expected proportion of null hypotheses mistakenly rejected given a measure of evidence for their truth. In this article, we propose controlling the positive FDR using a Bayesian approach where the rejection rule is based on the posterior probabilities of the null hypotheses. Correspondence between Bayesian and frequentist measures of evidence in hypothesis testing has been studied in several contexts. Here we extend the comparison to multiple testing with control of the FDR and illustrate the procedure with an application to wavelet thresholding. The problem consists of recovering signal from noisy measurements. This involves extracting wavelet coefficients that result from true signal and can be formulated as a multiple hypotheses-testing problem. We use simulated examples to compare the performance of our approach to the Benjamini and Hochberg (1995, Journal of the Royal Statistical Society, Series B57, 289-300) procedure. We also illustrate the method with nuclear magnetic resonance spectral data from human brain.

Bayes Theorem↗

PubMed2004

Identification of DNA regulatory motifs using Bayesian variable selection.

MOTIVATION: Understanding the mechanisms that determine gene expression regulation is an important and challenging problem. A common approach consists of identifying DNA-binding sites from a collection of co-regulated genes and their nearby non-coding DNA sequences. Here, we consider a regression model that linearly relates gene expression levels to a sequence matching score of nucleotide patterns. We use Bayesian models and stochastic search techniques to select transcription factor binding site candidates, as an alternative to stepwise regression procedures used by other investigators. RESULTS: We demonstrate through simulated data the improved performance of the Bayesian variable selection method compared to the stepwise procedure. We then analyze and discuss the results from experiments involving well-studied pathways of Saccharomyces cerevisiae and Schizosaccharomyces pombe. We identify regulatory motifs known to be related to the experimental conditions considered. Some of our selected motifs are also in agreement with recent findings by other researchers. In addition, our results include novel motifs that constitute promising sets for further assessment. AVAILABILITY: The Matlab code for implementing the Bayesian variable selection method may be obtained from the corresponding author.

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PubMed2004

Bayesian variable selection in multinomial probit models to identify molecular signatures of disease stage.

Here we focus on discrimination problems where the number of predictors substantially exceeds the sample size and we propose a Bayesian variable selection approach to multinomial probit models. Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. We apply our methodology to a problem in functional genomics using gene expression profiling data. The aim of the analysis is to identify molecular signatures that characterize two different stages of rheumatoid arthritis.

Arthritis, Rheumatoid↗

PubMed2004

Making sense of molecular signatures in the immune system.

The development of Functional Genomics technologies has opened new avenues to investigate the complexity of the immune system. Microarray technology has been particularly successful because of its relatively low cost and high genome coverage. Consequently to our ability to monitor the expression of a significant proportion of an organism genome, our understanding of the molecular dynamics behind cell differentiation and cell response has greatly improved. Molecular signatures associated to immune cells have provided important tools to investigate the molecular basis of diseases and have been often associated to diagnostic and prognostic markers. The availability of such large collection of data has stimulated the application of complex machine learning techniques in the attempt to link molecular signatures and cell physiology. Here we review the most recent developments in the analysis of molecular signatures in the immune system.

Animals↗

PubMed2003

Investigating the evolution and structure of chemokine receptors.

Chemokine receptors represent a prime target for the development of novel therapeutic strategies in a variety of disease processes, including inflammation, allergy and neoplasia. Here we use maximum likelihood methods and bootstrap methods to investigate both the phylogenetic relationships in a large set of human chemokine receptor sequences and the relationships between chemokine receptors and their nearest neighbors. We found that CCR and CXCR families are not homogeneous. We also provide evidences that angiotensin receptors are the closest neighbors. Other close neighbors include opioid, somatostatin and melanin-concentrating hormone receptors. The phylogenetic analysis suggests ancient paralogous relationships and establishes a link between immune, metabolic and neural systems modulation. We complement our findings with a structural analysis based on wavelet methods of the major branches of chemokine receptors phylogeny. We hypothesize that receptors very close in the tree can form heterodimers. Our analyses reveal different characteristics of amino acid hydrophobicity and volume propensity in the different subfamilies. We also found that the second extra-cytoplasmic loop has higher rates of evolution than the internal loops and transmembrane segments, suggesting that selection, shifting, reassignments and broadening of receptor binding specificities involve mainly this loop.

Evolution, Molecular↗

PubMed2003

Gene selection: a Bayesian variable selection approach.

UNLABELLED: Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Bayesian mixture prior to perform the variable selection. We control the size of the model by assigning a prior distribution over the dimension (number of significant genes) of the model. The posterior distributions of the parameters are not in explicit form and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the parameters from the posteriors. The Bayesian model is flexible enough to identify significant genes as well as to perform future predictions. The method is applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes. The method is also applied successfully to the leukemia data. SUPPLEMENTARY INFORMATION: http://stat.tamu.edu/people/faculty/bmallick.html.

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