This lab is offered as an opportunity for extra credit or for your project requirement (if applicable). This was presented by Dr. Luo during a guest lecture visit last year, so there is not “tech support” available for it this year. So the challenge is to figure out the tools from the tutorials, and (if possible) apply them to your count data from the vibrio data set. Due by the last day of class if you choose to do it.

For this lab, you will need to add a couple of R packages to your R installation. So instead of logging in as yourself on the lab computers, try logging in as “student” with the password on the whiteboard. The “student” accounts are supposed to have some extra privileges although you have no way to keep personal data stored there.

For the lab session, students should go through the gage tutorial:
Quick start with demo data (p1)
Basic analysis (p3-8)
Results presentation and Intepretation (p10-17)

gage package and tutorial:
http://bioconductor.org/packages/devel/bioc/html/gage.html
pathview and gageData:
http://bioconductor.org/packages/devel/bioc/html/pathview.html
http://bioconductor.org/packages/devel/data/experiment/html/gageData.html

Potential homework:
Applied GAGE analysis workflow to bmp6 (microarray) or hnrnp.cnts
(RNA-Seq) dataset in gageData package. you may follow the gage tutorial
as the reference. In addition, you should check out the other tutorial
of gage package: RNA-Seq Data Pathway and Gene-set Analysis Workflows,
for RNA-Seq data analysis.
Select significantly perturbed KEGG and GO, and output the selected gene
sets and statistics as tab-deliminated text files.
Visualize the expression perturbations of top selected KEGG pathways or
GO terms using heatmaps or pathview graphs. Check Figure 2-4 in the gage
tutorial for examples.
Describe your major biology insights from the gage analysis.