Biostatistics Seminar Series - Data Integration for Multi-Study Data: Adventures from Epidemiology to Genomics.

Speaker: Roberta De Vito, PhD Biostatistics and computational biology are increasingly facing the urgent challenge of efficiently dealing with a large amount of experimental data. High-throughput assays are transforming the study of biology, as they generate a rich, complex, and diverse collection of high-dimensional data sets. The increased availability of ensembles of studies on related clinical populations, technologies, and genomic features poses four categories of important multi-study statistical questions: 1) To what extent is biological signal reproducibly shared across different studies? 2) How can this global signal be extracted? 3) How can we detect and quantify local signals that may be masked by strong global signals? 4) How do these global and local signals manifest differently in different data types? We will answer these four questions by introducing a novel class of factor analysis methodologies for the joint analysis of different studies. The goal is to separately identify and estimate 1) common factors reproduced across multiple studies, and 2) study-specific factors. We present different medical and biological applications, going from genomic to nutritional epidemiological data. In all the cases, we clarify the benefits of using our multi-study methods compared to the standard techniques.