Gene ontology & hypergeometric test Simon Rasmussen CBS - DTU
The DNA Microarray Analysis Pipeline Question/hypothesis Experimental Design Array design Probe design Sample Preparation Hybridization Buy standard Chip / Array Image analysis Normalization Expression Index Calculation Comparable Gene Expression Data Statistical Analysis Fit to Model (time series) Advanced Data Analysis Clustering Classification PCA Gene Annotation Analysis Meta analysis Survival analysis Promoter 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? 10 x 100 white balls out of 6000 balls 70 x p = 6. 2 * 10 -8 Total 80 balls The GO term DNA replication is overrepresented in our list
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