An Introduction to DNA Microarrays

1. An Introduction to DNA Microarrays Jack Newton University of Alberta newton@cs.ualberta.ca

2. Overview • Introduction to DNA Microarrays • DNA Microarray Analysis • DNA Microarray Data Characteristics

3. What are DNA Microarrays? • A recent technology that can measure the expression level of thousands of genes at once • A large number of DNA fragments are attached in a systematic way to a solid substrate

4. Gene GTF4 Downregulated Upregulated DNA Microarray Image Reference cDNA Experimental cDNA

5. … … Gene TEF4: T A A G T C G T A A G T C G T G A G T C T G A T G T T G A C T G C C A T G C A A T G How DNA Microarrays Work A G T C

6. How DNA Microarrays Work • DNA microarrays measure gene expression levels by measuring mRNA abundance Cambell, Neil (1996). Biology. Benjamin/Cummings Publishing.

7. DNA Microarrays and Gene Expression • Traditional view of gene expression: • A single gene codes for a single protein • Modern view of gene expression: • A single gene may code for several proteins as a result of alternate splicing of mRNA and post-translational modifications • Genes act in concert, not in isolation • Thus, we need to observe genes acting together, not in isolation

8. From Gene Science to Genome Science • DNA Microarrays allow us to see the gene expression levels for tens of thousands of genes at once. • A microarray of 50,000 unique cDNAs allows the expression monitoring of the entire human genome in a single hybridization.

9. DNA Microarray Analysis What kinds of questions do we want to ask? • What genes have similar function? • What regulatory pathways exist? • Can we subdivide experiments or genes into meaningful classes? • Can we correctly classify an unknown experiment or gene into a known class? • Can we make better treatment decisions for a cancer patient based on his or her gene expression profile?

10. Characteristics of DNA Microarray Data • Extremely high dimensionality • Experiment = (gene1, gene2, …, geneN) • Gene = (experiment1, experiment2, …, experimentM) • N is often on the order of 104 • M is often on the order of 101 • Noisy data • Normalization and thresholding are important • Missing data • For some experiments a given gene may have failed hybridizing

11. Characteristics of DNA Microarray Data (Continued) Experiment 1 Experiment 2 Experiment 3 Experiment 4 Experiment 5 Experiment 6 Experiment 7 Experiment 8 Experiment 9 Experiment 10 > 8.0 > 4.8 Gene 1 > 2.8 > 1.7 Gene 2 1:1 Gene 3 > 1.7 Gene 4 > 2.8 > 4.8 Gene 5 > 8.0 … … Gene 10,000

12. Application: Clustering • Eisen et al. formulated a method to group genes with similar patterns of expression together. • Provides scientists with an invaluable tool to visualize and interpret DNA microarray data. • A recently published article in Nature applied this technique to breast cancer research.

13. Application: Clustering Perou, Charles M., et al. Nature, 406, 747-752 , 2000.

14. Application: Clustering • Clustering analysis identified four distinct tumor types that had not previously been reported. • Previous studies examining the same genes one at a time did not reveal that certain groups of genes play an important role in tumor development. • “When you look at one gene at a time, you can't see relationships between genes and groups of genes.” – M. Perou, co-author of study.