Gene Expression Analysis using Microarrays

1. Gene Expression Analysis using Microarrays Anne R. Haake, Ph.D.

2. Figure by Lawrence Berkeley Lab Human Genome Center, Berkeley, California, USA

3. Post-Genomic Age ? A switch in focus from sequencing to understanding how genomes function

4. How do we relate gene identity to cell physiology, disease & drug discovery? • Functional Genomics =“development and application of global (genome-wide or system-wide) experimental approaches to assess gene function by making use of the information and reagents provided by structural genomics”

5. Gene Expression Analysis • What is gene expression? • What can we learn from expression analysis? • How is the analysis accomplished? • What are the challenges for bioinformatics?

6. Gene Expression Flow of Information

7. Individual cells in an organism have the same genes (DNA) but…. • It is the expression of thousands of genes and their products (RNA, proteins), functioning in a complicated and orchestrated way, that make that organism what it is.

8. Differential Gene Expression A Few Examples: • Cell type specific -e.g. skin cell vs. brain cell • Developmental stage -e.g. embryonic skin cell vs. adult skin cell • Disease state -e.g. normal skin cell vs. skin tumor cell • Environment-specific -e.g. skin cell untreated vs. treated drugs, toxins

9. What can we learn by analyzing complex patterns of gene expression? • Classifications: for diagnosis, prediction… Cell-type, stage-specific, disease- related, treatment-related patterns of gene expression? • Gene Networks/Pathways: Functional roles of genes in cellular processes? Gene regulation and gene interactions

10. Gene Networks

11. http://industry.ebi.ac.uk/~brazma/Genenets

12. Gene Expression Analysis Need efficient ways to study these complex patterns. 1) Techniques of Biochemistry/Molecular Biology Resolution of the patterns = expression data (RNA or protein) 2) Management of complex data sets 3) Mining of the data to gain useful information

13. Gene Expression Analysis High-Throughput Techniques • Microarray or Gene Chip = cDNA arrays or oligo arrays (Affymetrix) • Filter Arrays • Differential Display • SAGE

14. Gene Chip technology • DNA microarrays = hundreds to thousands of different DNA sequences spotted onto glass microscope slide • Compare binding (base-pairing) of two different sets of expressed gene sequences to the template DNA microarray • Allows simultaneous analysis of thousands of genes: Is the gene expressed? At what level? *expression levels are relative

15. Flash Animation available at: http://www.bio.davidson.edu/Biology/Courses/genomics/chip/chip.html

16. The Full Yeast Genome on a Chip 6116 Yeast Genes96 Intergenic regions+ lots of control samples • Primers purchased from Research Genetics • Total spots printed: 707,520 • Total Arrays: 110 • Actual Time to print: 52 hours • Credits: Dr. Patrick O. Brown laboratory: pbrown@cmgm.stanford.edu

17. Outcomes of Microarray Analysis • Size and complexity of the problem • Example: 20,000 genes from 10 samples under 20 different conditions - 4,000,000 pieces of data  challenges for Bioinformatics

18. Outcomes of Microarray Analysis • Large, complex data sets • Wide availability of technology large number of distributed databases Current state: data scattered among many independent sites (accessible via Internet) or not publicly available at all.

19. Current Problems Facing Bioinformatics • Standardization & Quality Control In the Experiments (data quality at several levels) • Management of the Data -Standardization of the databases -Public access to the databases • Information from the Data -Need for data mining algorithms customized for gene expression analysis

20. Microarray Databases • Need public repository with standardized annotation Issues : - difficulty in describing expression experiments; remember that measurements are relative (complicates comparisons) • Structure of the database itself • Internet-based tools for searching and using semantic context to allow comparisons

21. Public Microarry Repositories 4 Major Efforts: GeneX at US National Center for Genome Resources http://www.ncgr.org/research/genex/ ArrayExpress at European Bioinformatics Institute http://www.ebi.ac.uk/arrayexpress/

22. Public Repositories • Stanford University Database http://genome-www4.stanford.edu/MicroArray/SMD/index.html

23. Public Repositories Gene Expression Omnibus at US National Center for Biotechnology Information Example at: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM39

24. Mining of the Expression Databases • A gene expression pattern derived from a single microarray experiment is simply a snapshot (one experimental sample vs reference) • Usually want to understand a process or changes in expression over a collection of samples  gene expression profile Example?

25. Mining of the Expression Databases General Approaches • Raw data from multiple experiments converted to a gene expression matrix - Rows: Different genes - Columns: Different samples - Numerical values encoded by color (red=positive green=negative blue=n.a.)

26. Typical approach Look for similarities (or differences) in patterns e.g. Compare rows to find evidence for co-regulation of genes 1) Need ways to measure similarity (distance) among the objects being compared 2) Then, group together objects (genes or samples) with similar properties.

27. Cluster Analysis • Partitions biological samples into groups based on their statistical behavior. - Unsupervised Analysis - Supervised Analysis: classification rules

28. Analytic Approaches • Clustering Algorithms • Hierarchical • K-mean • Self-organizing maps • Others

29. Eisen et al. http://www.pnas.org/cgi/content/full/95/25/14863

30. Success StoryGene Clustering Approach • Yeast genome • Complete set of genes used to study diauxic shift time course • Cluster analysis of data identified group of genes with similar expression profiles • Upstream regulatory sites of these genes compared to identify transcription factor binding sites (see Brazma & Vilo reference)

31. Example-Sample Clustering Classification of cancers • Comparing 2 acute leukemias (AML and ALL) Biological/Clinical Problems: • Previously, no single reliable test to distinguish • Differ greatly in clinical course & response to treatments. http://waldo.wi.mit.edu/MPR/figures_ALL_AML.html

34. The prediction of a new sample is based on 'weighted votes' of a set of informative genes.

35. Analytic Approach: 1) Class discovery = classification by clustering of microarray data using tumors of known type Found 1100 of 6817 genes correlated with class distinction 2) Formation of a class predictor = 50 most informative genes  class discovery of unknown tumors

37. Analytic Approaches Limitation of cluster analysis: similarity in expression pattern suggests co-regulation but doesn’t reveal cause-effect relationships • Bayesian Networks • Represent the dependence structure between multiple interacting quantities (e.g. expression levels of genes) • gene interactions & models of causal influence • Others? many

38. Check the Web: Free Software Available Some useful links: • Expression Profiler http://ep.ebi.ac.uk/ • GeneX (NCGR) http://genex.sourceforge.net/ www.ncgr.org/research/genex/other_tools.html • http://www.kdnuggets.com/software/suites.html

39. Additional References: • R. Ekins and F.W. Chu :Microarrays: their origins and applications. Trends in Biotechnology, 17: 217-218, 1999. • Brazma et al., One-stop shop for microarray data. Nature 403: 699 – 700, 2000. • Brazma A. and Vilo, J:Minireview. Gene expression data analysis. FEBS Letters, 480:17-24, 2000.