Pathway analysis methods have become a standard approach for gaining insight into the underlying biology of genomic and proteomic datasets. They offer biologists a way to move from a gene-by-gene investigation of their data to a global exploration of their data in the context of biological processes, cellular and disease phenotypes, signaling pathways, and molecular networks.
The advantages of applying pathway analysis methods include:
- Ability to study groups of gene products
- Understand which pathways are particularly affected in your samples
- Generate hypotheses as to mechanisms of regulation of your genes
- Predict the downstream effects of those gene expression changes
- Identify potential drug targets and biomarkers
This 90-minute class will provide you with an overview of the most common methods (Overrepresentation Analysis, Functional Enrichment, Gene Set Enrichment, Network Analysis) and tools applied in the field of pathway analysis. We will discuss what these methods enable you to conclude about your data, software applications (open source and commercial) available for each method, pros and cons of each method, and recent publications demonstrating their application in analysis of large datasets.