Gene ontology & hypergeometric test

Gene ontology &
hypergeometric test
Simon Rasmussen
CBS - DTU

The DNA Microarray Analysis Pipeline
Array design
Probe design
Expression Index
Calculation
Question/hypothesis
Experimental Design
Sample Preparation
Hybridization
Image analysis
Normalization
Comparable
Gene Expression Data
Statistical Analysis
Fit to Model (time series)
Buy standard
Chip / Array
Advanced Data Analysis
Clustering PCA Gene Annotation Analysis Promoter Analysis
Classification Meta analysis Survival analysis Regulatory Network

Gene Ontology
• Gene Ontology (GO) is a collection of controlled
vocabularies describing the biology of a gene
product in any organism
• Very useful for interpreting biological function of
microarray data
• Organized in 3 independent sets of ontologies in
a tree structure
– Molecular function (MF), Biological process (BP),
Cellular compartment (CC)

Tree structure
• Controlled networked terms (total ~25.000)
– Parent / child network organized as a tree
– Terms get more detailed as you move down
the network

Relationship
• A gene can be
– present in any of the ontologies (MF / BP /
CC)
– a member of several GO terms
• True path rule
– If a gene is member of a term it is also
member of the terms parents

GO Tree example
•visit www.geneontology.org for more information

KEGG
• KEGG PATHWAYS:
– Manually drawn pathway maps representing our
knowledge on the molecular interaction and reaction
networks, for a large selection of organisms
• 1. Metabolism
• 2. Genetic Information Processing
• 3. Environmental Information Processing
• 4. Cellular Processes
• 5. Human Diseases
• 6. Drug Development
Other pathway database: Reactome

KEGG example

Using Gene ontology
• Input: Any list of genes; from microarray exp.
– Cluster of genes with similar expression
– Up/down regulated genes
• Question we ask:
– Are any GO terms overrepresented in the gene list,
compared to what would happen by chance?
• Method
– Hypergeometric testing

Hypergeometric test
• The hypergeometric distribution arises from
sampling from a fixed population.
20 white balls
out of
100 balls
10 balls
• We want to calculate the probability for drawing 7 or
more white balls out of 10 balls given the
distribution of balls in the urn

Example
• List of 80 significant genes from a microarray
experiment of yeast (~ 6000 genes)
• 10 of the 80 genes are in BP-GO term: DNA replication
– Total nr of yeast genes in GO term is 100
• What is the probability of this occurring by chance?
100 white balls
out of
6000 balls
10 x
70 x
Total 80 balls
p = 6.6 * 10 -7
The GO term DNA replication is overrepresented in our list

Fisher exact test
• List of 80 significant genes from a microarray experiment of
yeast (~ 6000 genes)
• 10 of the 80 genes are in BP-GO term: DNA replication
– Total nr of yeast genes in GO term is 100
Non-sig.
Signif.
-GO +GO
a b
c d
a+b
c+d
a+c b+d n
Non-sig.
Signif.
Fisher exact test:
p-value: 6.6 10 -07
-GO +GO
5830 90
70 10
5920
80
5900 100 6000

Exercise
http://www.cbs.dtu.dk/chipcourse/
Exercises/Ex_GO/GOexercise10.php