Module dates/times: Monday, July 23; 8:30 a.m. -5 p.m.; Tuesday, July 24, 8:30 a.m.-5 p.m., and Wednesday, July 25, 8:30 a.m.-Noon
This course is full. If you wish to be placed on the waiting list, email firstname.lastname@example.org.
This module emphasizes how the theory and application of transcriptomics can be extended to include other types of omic analysis, and then integrated using statistical and machine learning tools.
It starts with the statistical basis of hypothesis testing covering the central role of normalization strategies and the specifics of differential expression analysis. Students will be given the opportunity to work examples using open source R code that is in standard use for RNASeq data.
The module then discusses options for downstream processing by clustering and module detection/comparison; extensions to methylation profiling, proteomics, and metabolomics; eQTL analysis including fine mapping of regulatory variation; and finally, integrative methodologies addressing the relationship between genomic, meta-genomic, and phenotypic variation.
This module deals primarily with upstream data processing methods that lead to the delineation of networks and pathways that are then considered in Module 19: Pathway & Network Analysis for Omics Data.
Greg Gibson is Professor and Director of the Center for Integrative Genomics at Georgia Tech. He conducts research on genomic approaches to human genetics; variability of gene expression; systems biology of disease; theory of canalization and biological robustness. He recently published “Constraints on eQTL fine mapping in the presence of multisite local regulation of gene expression.” G3-Genes,Genomes, Genetics7:2532-2544, 2017.
Joseph Powell is Associate Professor and Scientific Director of the Garvan Weizmann Centre for Cellular Genomics at the Garvan Institute, Sydney. His lab develops and applies computational and statistical genomics approaches to investigate the genetic control of genome regulation and its role in contributing to the susceptibility to human disease. Specifically, his research involves the use of large-scale transcriptomic and DNA sequence data from both bulk tissues and single cells, focusing on understanding the genetic mechanisms by which heritable variants contribute to disease susceptibility at a cellular level, and ultimately achieve therapeutic and diagnostic outcomes. He has recently published “Genetic correlations reveal the shared genetic architecture of transcription in human peripheral blood.” Nature Communications 8: Article 483.
Access 2017 Course Materials (2018 materials will be uploaded to this page prior to the start of the module)