Overview

Introduction Biological network data Text mining Gene Ontology Expression data basics

Expression, text mining, and GO

Modules and complexes Domains and conclusion

Scenario

Ran a set of expression experiments to study a given disease state.

Need to put the results into a functional context.

Atherosclerosis

Most common fatal disease in the U.S., and not well-understood.

Microarray analysis

Analyzed 51 artery segments from the hearts from 22 heart transplant patients.

Classified segments by their disease pathology. Will assess the differences between Type I

(moderate) and Type V (severe) atherosclerosis.

Performed microarray analysis of each segment. Agilent expression array with probesets for 13,000

human genes.

SAM microarray statistic

For each gene i, contrasted expression in Type I and Type V lesions with SAM (Proc Natl Acad Sci USA 98: 5116-21, 2001). High positive SAM score: gene expressed more highly in

Type V lesions. Large negative SAM score: gene expressed more highly in

Type I lesions.

Analysis pipeline

1. Biomarker identification

For formal studies, use machine learning methods

For exploratory work, select several genes with extreme SAM scores.

Analysis pipeline, continued

2. Biomarker association Basic question: for this context,

what is common among the biomarker genes?

Approaches Exhaustive reading GO analysis Literature searching

pros and cons of this approach

Pro: associations are specific to this disease context

Pro: identifies relevant literature

Con: might not find associations on all of your biomarkers

Con: might find associations on other genes

Iterative literature searching

Perform an initial searchColor the network by SAM d-scoreIdentify any new “responsive”

genesAdd to biomarker listRepeat

Discussion topic

Why not use all genes with extreme SAM scores as biomarkers? Why

iterate?

Once you have a good network:

1. Use BiNGO to identify the enriched GO terms

2. Look at the genes corresponding to selected enriched terms

3. Check the literature search sentences for those genes

4. Choose one or two sentences, look at the abstracts.

5. Iterate if desired (or go to lunch)

Final points

No right or wrong answers, only plausible or novel hypotheses.

You can take any approach you wish.

“If it was easy, everyone would be doing it”.