1. Biological Interpretation of Microarray Data Helen Lockstone DTC Bioinformatics Course 9 th February 2010

2. Overview Interpreting microarray results –Gene lists to biological knowledge The Gene Ontology Consortium –Defined terms to describe gene function Functional analysis tools –Methods –DAVID/GSEA

3. Microarray Pipeline Design and perform experiment Process and normalise data Statistical analysis Differentially expressed genes Biological interpretation

4. Biological Interpretation An obvious way to gain biological insight is to assess the differentially expressed genes in terms of their known function(s) Required an automated and objective (statistical) approach Functional profiling or pathway analysis

5. Early functional analyses Manually annotate list of differentially expressed (DE) genes Extremely time-consuming, not systematic, user- dependent Group together genes with similar function Conclude functional categories with most DE genes important in disease/condition under study BUT may not be the right conclusion

6. GO and functional analysis Immune response category contains 40% of all significant genes - by far the largest category. Reasonable to conclude that immune response may be important in the condition being studied?

7. However …. What if 40% of the genes on the array were involved in immune response? Only detected as many significant immune response genes as expected by chance Need to consider not only the number of significant genes for each category, but also total number on the array

8. Same example, relative to array Expected number of significant genes for category X = (num sig genes ÷ total genes on array)*(num genes in category X on array)

9. Same example, relative to array Now, transcription and neurotransmission categories appear more interesting as many more significant genes were observed than expected by chance Largest categories are not necessarily the most interesting!

10. Major bioinformatic developments Requires annotating entire set of genes The Gene Ontology Consortium (www.geneontology.org)www.geneontology.org Automated, statistical approaches for annotating gene lists and performing functional profiling

11. The Gene Ontology Consortium

12. GO Consortium Developed three structured and controlled vocabularies (ontologies) that describe gene products in terms of their associated biological processes, cellular components and molecular functions in a species- independent manner Has become a major resource for microarray data interpretation

13. The Gene Ontology Molecular Function: basic activity or task Biological Process: broad objective or goal Cellular Component: location or complex

14. The Gene Ontology Molecular Function: basic activity or task –e.g. catalytic activity, calcium ion binding Biological Process: broad objective or goal –e.g. signal transduction, immune response Cellular Component: location or complex –e.g. nucleus, mitochondrion

15. GO Structure Hierarchical tree Annotated with most specific annotation, forming path to top of tree Genes annotated with all relevant terms Annotations based on published studies and also electronic inferences

16. GO Terms GO ID: GO:0007268 GO term: synaptic transmission Ontology: biological process Definition: The process of communication from a neuron to a target (neuron, muscle, or secretory cell) across a synapse

18. Graphical view

19. http://www.ncbi.nlm.nih.gov/sites/entrez

20. Functional Profiling Tools

21. Functional profiling tools Identify GO categories with significantly more DE genes than expected by chance (i.e. over- represented among DE genes relative to representation on array as a whole) Correct for testing multiple GO categories Hypergeometric Distribution or Fisher’s Exact Test

22. Khatri and Draghici. Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics (2005) 21(18):3587-95 Functional profiling tools

23. Freely-available stand-alone/web-based tools –User-friendly graphical interface and simple to use –Extensive documentation, plus tutorials/technical support Reduces a large number of DE genes to a smaller number of significantly enriched GO categories –more easily interpreted in biological context Considering sets of genes increases power –individual genes could be false positives but a set of functionally related genes all showing significant changes is more robust

25. DAVID Results

26. Advantages Increasingly support data (probe IDs) from different microarray platforms Accept various probe/gene identifiers Web-based tools automatically retrieve most up-to- date GO annotations Most automatically map from probe IDs to a gene ID - multiple significant probes for one gene could otherwise skew results

27. Further considerations Reference list must be appropriate for accurate statistical analysis Up/down regulated genes can be submitted separately or as a combined list Unannotated genes cannot be used in the analysis; gene ontology evolving; well-studied systems over-represented

28. Gene set enrichment analysis Majority of tools based on idea of identifying GO categories significantly enriched in list of differentially expressed genes Requires some threshold to define genes as ‘significant’ Recent tool called GSEA takes a different approach by considering all assayed genes

29. GSEA: Key Features Ranks all genes on array based on their differential expression Identifies gene sets whose member genes are clustered either towards top or bottom of the ranked list (i.e. up- or down regulated) Enrichment score calculated for each category Permutation test to identify significantly enriched categories Extensive gene sets provided via MolSig DB – GO, chromosome location, KEGG pathways, transcription factor or microRNA target genes

30. GSEA Each gene category tested by traversing ranked list Enrichment score starts at 0, weighted increment when a member gene encountered, weighted decrement otherwise Enrichment score – point where most different from zero Most significantly up-regulated genes Unchanged genes Most significantly down-regulated genes Disease Control

31. GSEA algorithm

32. Null distribution of enrichment scores Actual ES GSEA: Permutation Test Randomise data (groups), rank genes again and repeat test 1000 times Null distribution of 1000 ES for geneset FDR q-value computed – corrected for gene set size and testing multiple gene sets

33. Biological Interpretation Due to GO hierarchy, several related categories may contain a subset of genes that is driving the significant enrichment score so will all be significant Interpretation still requires substantial work –search literature and public databases –likely functional consequences of the changes –are the genes identified as significant within each GO category up- or down-regulated? –genes within a category can have opposite effects e.g. apoptosis would include genes that induce or repress apoptosis

34. Biological Interpretation Too many categories found significant –Size filter –More stringent significance threshold –Related categories (redundancy) No significant categories –Relax significance level slightly –e.g. 0.25 recommended by GSEA as exploratory analysis No significant genes –GSEA most suitable

35. Commercial Tool Suites Ingenuity Pathway Analysis (Ingenuity Systems, CA) –Developed own extensive ontology over past 10 years –Includes gene interactions, disease/drug information –PhD-level curators mining the literature –Used by many pharmaceutical companies

36. For more information Gene Ontology: http://www.geneontology.orghttp://www.geneontology.org Affymetrix: http://www.affymetrix.comhttp://www.affymetrix.com DAVID: http://david.abcc.ncifcrf.govhttp://david.abcc.ncifcrf.gov GSEA: http://www.broad.mit.edu/gsea/http://www.broad.mit.edu/gsea/ Ingenuity: http://www.ingenuity.com/products/pathways_analysis.ht ml http://www.ingenuity.com/products/pathways_analysis.ht ml NCBI: http://www.ncbi.nlm.nih.gov/http://www.ncbi.nlm.nih.gov/