23rd Summer Institute in Statistical Genetics (SISG)

Module 19: Pathway & Network Analysis for Omics Data

Session 6: Wed Jul 25 to Fri Jul 27

Module dates/times: Wednesday, July 25, 1:30-5 p.m.; Thursday, July 26, 8:30 a.m.-5 p.m., and Friday, July 27, 8:30 a.m.-5 p.m.

Networks represent the interactions among components of biological systems. In the context of high dimensional omics data, relevant networks include gene regulatory networks, protein-protein interaction networks, and metabolic networks.

These networks provide a window into biological systems as well as complex diseases, and can be used to understand how biological functions are implemented and how homeostasis is maintained. On the other hand, pathway-based analyses can be used to leverage biological knowledge available from literature, gene ontologies or previous experiments in order to identify the pathways associated with disease or an outcome of interest.

In this module, various statistical learning methods for reconstruction and analysis of networks from omics data are discussed, as well as methods of pathway enrichment analysis. Particular attention is paid to omics datasets with a large number of variables, e.g. genes, and a small number of samples, e.g. patients.

The techniques discussed will be demonstrated in R. This course assumes familiarity with R or other command-line programming languages.

Ali Shojaie is Associate Professor of Biostatistics at the University of Washington. He is interested in developing statistical methods for analysis of large, complex systems, particularly biological and social sys- tems. His research focuses on statistical methods for high-dimensional networks, statistical machine learning methods for estimation and inference in high-dimensional problems. He recently published “Using Twitter for demographic and social science research: Tools for data collection and processing.” Sociological Methods and Research 46:390-421, 2017.

Alison Motsinger-Reif is Associate Professor of Statistics at North Carolina State University. The primary goal of her research is the development of computational methods to detect genetic risk factors of common, complex traits in human populations. She focuses on the development of methods to detect complex predictive models in high-throughput genomic data. She recently published “Metabolic network failures in Alzheimer’s disease: A biochemical road map.” Alzheimers and Dementia 13:965-984, 2017.

Access 2017 Course Materials (2018 materials will be uploaded to this page prior to the start of the module)