For your final notebook check, please submit a Word or PDF document with some notes describing the advantages that pathway visualization has over standard differential gene expression analysis. How might a researcher use pathway visualizations after conducting differential gene expression analysis to further their research?
PreviousNextWhen working with transcriptomic data sets, the first step that most researchers will typically conduct is differential gene expression analysis. Although this is an essential first step, it is limited in the sense that it will only inform researchers which genes are up or down-regulated, relative to a control condition.
In some cases, it may be more intuitive to visualize differential expression analysis results in a more realistic context. Pathway maps are collections of enzymatic reactions, metabolites, and intermediates which have been compiled into a “map” to depict general cellular and metabolic processes.
There are many different repositories that have compiled such maps to be used with a broad array of different organisms, some of the most popular being the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Escher Visualizations.
In this assignment, we will demonstrate pathway visualizations of KEGG maps with differential expression data sets using a web tool called pathView – it also has an R-compatible package; however, we will use the web tool for simplicity, as this is not a computer science course.
The results from differential gene expression analysis can be used as input for path view and the user selects the respective pathway(s) which they would be interested in when conducting their own research. pathView supports visualization of gene expression data (the gene identifiers need to be compatible with the organism being studied) as well as metabolite data