Statistical methods for data integration

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Preamble: The nature of genomics research and application has changed drastically in the last two decades.***Increasingly inexpensive new technologies are being used to produce large biological data with the aim of gaining fundamental understanding of molecular biology, elucidating etiology of diseases and assessing the potential predictive utility of genomic information in clinical medicine as well as public health areas. The various genomic data sets that are being generated may contain both independent and redundant information. High throughput biological data also tend to be noisy.******Hypothesis: Novel sparse statistical methods that can capture essential characteristics of noisy data, and integrative statistical analyses that allow synthesis of information across data sets, will improve our ability to estimate effect sizes reliably, provide more power in detecting associations and lead to more accurate predictions. Furthermore, improved results will be obtained by considering measures of relative importance in the integrative analyses.*** ***AIM 1: Integrative Methods for Heterogeneous Data. We will develop kernel based statistical methods for supervised and unsupervised integration. We will provide a unified conceptual and methodological framework for integrating heterogeneous data types. This will include approaches for integrating genomic data with environmental, clinical and laboratory measurements. We will compare and contrast methods using simulations, and empirically evaluate using data from our collaborators as well as data available in the public domain.***AIM 2: Integrative Multivariate Methods for the Analysis of Microbiome Data***We will develop multivariate methods for the analysis of microbiome data that incorporate sparse representation and extend an integrative framework to filter out noisy features. Finally we will develop methods for jointly analyzing multiple correlated phenotypes.***Significance: Using multi-pronged integrative approaches and with appropriate sparse statistical methodologies, critical information will be generated, accurate assessment of genetic and clinical variability can be determined, and precise and valid assessment of outcomes will be maximized. Highly qualified personnel (HQPs) will be trained and software based on our work will be made freely available to the wider scientific community.**