Browsing by Author "Baladandayuthapani, Veerabhadran"

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    Bayesian graphical models for modern biological applications
    (Springer Nature, 2022) Ni, Yang; Baladandayuthapani, Veerabhadran; Vannucci, Marina; Stingo, Francesco C.
    Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.
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    Bayesian graphical models for modern biological applications
    (Springer Nature, 2021) Ni, Yang; Baladandayuthapani, Veerabhadran; Vannucci, Marina; Stingo, Francesco C.
    Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.
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    Prediction Oriented Marker Selection (PROMISE) for High Dimensional Regression with Application to Personalized Medicine
    (2015-10-27) Kim, Soyeon; Scott, David W.; Lee, J.Jack; Baladandayuthapani, Veerabhadran; Ensor, Katherine B; Nakhleh, Luay K
    In personalized medicine, biomarkers are used to select therapies with the highest likelihood of success based on a patients individual biomarker profile. Two important goals of biomarker selection are to choose a small number of important biomarkers that are associated with treatment outcomes and to maintain a high-level of prediction accuracy. These goals are challenging because the number of candidate biomarkers can be large compared to the sample size. Established methods for variable selection based on penalized regression methods such as the lasso and the elastic net have yielded promising results. However, selecting the right amount of penalization is critical to maintain the desired properties for both variable selection and prediction accuracy. To select the regularization parameter, cross-validation (CV) is most commonly used. It tends to provide high prediction accuracy as well as a high true positive rate, at the cost of a high false positive rate. Resampling methods such as stability selection (SS) conversely maintains a good control of the false positive rate, but at the cost of yielding too few true positives. We propose prediction oriented marker selection (PROMISE), which combines SS with CV to include the advantages of both methods. We applied PROMISE to (1) the lasso and (2) the elastic net for individual marker selection, (3) the group lasso for pathway selection, and (4) the combination of the group lasso with the lasso for individual marker selection within the selected pathways. Data analysis show that PROMISE produces a more sparse solution than CV, reducing the false positives compared to CV, while giving similar prediction accuracy and true positives. In our simulation and real data analysis, SS does not work well for variable selection and prediction. PROMISE can be applied in many fields to select regularization parameters when the goals are to minimize both type I and type II errors and to maximize prediction accuracy.
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    SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data
    (Frontiers Media S.A., 2023) Osher, Nathaniel; Kang, Jian; Krishnan, Santhoshi; Rao, Arvind; Baladandayuthapani, Veerabhadran
    Introduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions.Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features.Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes.Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN.