1. Nuria Lopez-Bigas Methods and tools in functional genomics (microarrays) BCO17

2. What are microarrays?

4. Microarray data analysis is the step that will allow us to extract biological meaning to high-throughput data generated with the experiment. Microarray data analysis

5. Microarray DATA Normalized data Data preprocession and normalization

6. Normalization and Noise: Normalization Some kind of normalization is usually required when comparing more than one microarray experiment. Adjust to account for differences in overall brightness of slides Normalize relative to housekeeping genes Noise Refers to variability and reproducibility of microarray experiments Intra and inter-microarray variations can significantly skew interpretation of data Sample collection is very important. If comparing two conditions you must control for all variables other than the one you are trying to measure Technical noise can result from imperfections in the chip. Both biological and technical replicates are required to measure and control these sources of noise Microarray data analysis

7. Differential expression Microarray DATANormalized data Data preprocession and normalization Data analysis

8. Microarray data analysis Differential expressionGO,KEGG…analysis Microarray DATANormalized data Data preprocession and normalization Data analysis

9. http://www.geneontology.org The Gene Ontology project provides a controlled vocabulary to describe gene and gene product attributes in any organism. The Ontologies Cellular component Biological process Molecular function BROWSER::AMIGO TOOLS Gene Ontology

12. Gene Ontology::Tools http://www.geneontology.org/GO.tools.shtml http://www.fatigo.org/ http://www.barleybase.org/funcexpression.php http://discover.nci.nih.gov/gominer/htgm.jsp FUNC-EXPRESSION

13. KEGG http://www.genome.jp/kegg/

14. Microarray data analysis Differential expression GO,KEGG…analysis Classification Microarray DATANormalized data Data preprocession and normalization Data analysis

15. Classification Support vectors machines Desition trees

16. Microarray data analysis Differential expression GO,KEGG…analysis Classification Clustering Microarray DATANormalized data Data preprocession and normalization Data analysis

17. Supervised versus Unsupervised: Supervised Analysis to determine genes that fit a predetermined pattern Usually used to find genes with expression levels that are significantly different between groups of samples or finding genes that accurately predict a characteristic of the sample Two popular supervised techniques would be nearest-neighbour analysis and support vector machines. Unsupervised Analysis to characterize the components of a data set without a priori input or knowledge of a training signal Try to find internal structure or relationships in data without trying to predict some ‘correct answer’. Three classes: 1. Feature determination: Look for genes with interesting patterns Eg. Principal-components analysis 2. Cluster determination: Determine groups of genes with similar expression patterns eg. Nearest-neighbour clustering, self-organizing maps, k-means clustering, 2d hierarchical clustering 3. Network determination: Determine graphs representing gene-gene or gene-phenotype interactions. Eg. Boolean networks, Bayesian networks, relevance networks Clustering & Classification

18. Cooper Breast Cancer Res 2001 3 :158

19. Microarray data analysis Differential expression GO,KEGG…analysis Clustering Classification Promoter analysis Microarray DATANormalized data Data preprocession and normalization Data analysis

20. Promoter analysis::TFBS TRANSFAC

21. Promoter analysis::Tools http://www.cisreg.ca/

22. Microarray data analysis Differential expression GO,KEGG…analysis Clustering Classification Promoter analysis Reverse engineering Microarray DATANormalized data Data preprocession and normalization Data analysis

23. Reverse engineering

24. Microarray data analysis Differential expression GO,KEGG…analysis Clustering Classification Promoter analysis Reverse engineering Microarray DATANormalized data Data preprocession and normalization Data analysis