Gene ontology & hypergeometric test Simon Rasmussen CBS - DTU.

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Presentation transcript:

Gene ontology & hypergeometric test Simon Rasmussen CBS - DTU

The DNA Microarray Analysis Pipeline Sample Preparation Hybridization Array design Probe design Experimental Design Buy standard Chip / Array Statistical Analysis Fit to Model (time series) Expression Index Calculation Advanced Data Analysis ClusteringPCAGene Annotation AnalysisPromoter Analysis ClassificationMeta analysisSurvival analysisRegulatory Network Comparable Gene Expression Data Normalization Image analysis Question/hypothesis

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 for more informationwww.geneontology.org

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

The hypergeometric distribution arises from sampling from a fixed population. 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 20 white balls out of 100 balls Hypergeometric test

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? The GO term DNA replication is overrepresented in our list 100 white balls out of 6000 balls 10 x 70 x Total 80 balls p = 6.2 * 10 -8