1. Microarrays and Gene Expression Analysis

2. 2 Gene Expression Data Microarray experiments Applications Data analysis Gene Expression Databases

3. 3 DNA Microarray First introduced in 1987 A microarray is a tool for analyzing gene expression in genomic scale. The microarray consists of a small membrane or glass slide containing samples of many genes arranged in a regular pattern.

4. 4 Chips or Microarrays Two types of microarray technologies 1.Spotted Microarray Traditionally called DNA microarrays. 2.Affymetrix- Developed at Affymetrix, Inc. Historically called DNA chips..

5. 5 Spotted Microarray probe cDNA (50~5,000 bases long) is immobilized to a solid surface such as glass using robot spotting and exposed to a set of targets either separately or in a mixture.

6. 6

7. 7 http://occawlonline.pearsoned.com/bookbind/pubbooks/bc_mcampbell_genomics_1/medialib/method/chip/chip.html

8. 8 Experimental Protocol 1.Identify RNA/DNA sequences of interest –Design probes that are sequence-specific 2.Extract molecules from cell environment –Label molecules with fluorescent dye 3.Pour solution onto microarray –Then wash off excess molecules 4.Shine laser light onto array –Scan for presence of fluorescent dye

9. 9 Microarray Images Original ImageSummary One gene or mRNA One tissue or condition

10. 10 Cy3Cy5 Cy3 Cy5 log 2 Cy3 The ratio of expression is indicated by the intensity of the color Red= High mRNA abundance in the experiment sample Green= High mRNA abundance in the control sample

11. 11 Expression Data Format normal hot cold uch1 -2.0 0.0 0.924 gut2 0.398 0.402 -1.329 fip1 0.225 0.225 -2.151 msh1 0.676 0.685 -0.564 vma2 0.41 0.414 -1.285 meu26 0.353 0.286 -1.503 git8 0.47 0.47 -1.088 sec7b 0.39 0.395 -1.358 apn1 0.681 0.636 -0.555 wos2 0.902 0.904 -0.149 Conditions Genes / mRNAs

12. 12 Microarray Applications Identify genes whose function is related –Similar expression in group in many cases Find genes expressed in specific tissues –Different expression in different cells Find genes affected by environment –Different expression under different conditions Distinguish different forms of a disease –Different expression in different patients

13. 13 Microarray Applications Specific Examples Cancer Research Ramaswamy et al, 2003 Nat Genet 33:49-54 Hundreds of genes that differentiate between cancer tissues in different stages of the tumor were found. These different stages were not detected by histological or other clinical parameters.

14. 14 Microarray Analysis GOALS Sample classification -What are the set of genes that differentiate between two or more groups of treatments Gene Classification - What is the set of genes that have the same expression profile along a set of treatments

15. 15 Microarray Analysis Unsupervised -Partion Methods K-means SOM (Self Organizing Maps -Hierarchical Clustering Supervised Methods -Analysis of variance -Discriminate analysis -Support Vector Machine (SVM)

16. 16 Clustering Grouping genes together according to their expression profiles. Hierarchical clustering: generate a tree –Each gene is a leaf on the tree –Distances reflect similarity of expression –Internal nodes represent functional groups –Similar approach to phylogenetic trees k-means clustering: generate k groups –Number k is chosen in advance –Each group represents similar expression

17. 17 Hierarchical Clustering Example Five separate clusters are indicated by colored bars and by identical coloring of the corresponding region of the dendrogram. The sequence-verified named genes in these clusters contain multiple genes involved in (A) cholesterol biosynthesis, (B) the cell cycle, (C) the immediate-early response, (D) signaling and angiogenesis, and (E) wound healing and tissue remodeling. These clusters also contain named genes not involved in these processes and numerous uncharacterized genes.

18. 18 Expression Correlation Similar expression between genes –One gene controls the other in a pathway –Both genes are controlled by another –Both genes required at the same time in cell cycle –Both genes have similar function Clusters can help identify regulatory motifs –Search for motifs in upstream promoter regions of all the genes in a cluster

19. 19 Support Vector Machine(SVM) As applied to gene expression data, an SVM would begin with a set of genes that have a common function, for example, genes coding for components of the proteasome (positive set). In addition, a separate set of genes that are known not to be members of the functional class (negative set) is specified. Using this training set, an SVM would learn to discriminate between the members and non- members of a given functional class based on expression data. Having learned the expression features of the class, the SVM could recognize new genes as members or as non-members of the class based on their expression data.

20. 20 How do SVM’s work ? Knowing the label of each example, the SVM tries to separates all training examples correctly and maximizes the distance between the points of each class If this is not possible in the input space a it searches for A hyperplane in a higher dimension space kernel ?

21. 21 Probe Selection Probe on DNA chip is shorter than target –Choice of which section to hybridize Select a region which is unstructured –RNA folding, DNA stem-and-loop Choose region which is target-specific –Avoid cross-hybridization with other DNA Avoid regions containing variation –Minimize presence of SNP sites

22. 22 Probe Design Two main factors to optimize Sensitivity –Strength of interaction with target sequence –Requires knowledge of target only Specificity –Weakness of interaction with other sequences –Requires knowledge of ‘background’

23. 23 Sensitivity Basic measure: best gapless alignment of entire probe against part of target sequence: AGTGCAAGTCCGATATGCCGTAATGCTATCA -2+6=+4 CTACACGA -7+1=-6 CTACACGA CTACACGA -6+2=-4 CTACACGA -8 Better: +3 for C–G, +2 for A–T, etc… -6+2=-4 CTACACGA

24. 24 Sources of Inaccuracy Some sequences bind better than others –Cross-hybridization, A–T versus G–C Scanning of microarray images –Scratches, smears, cell spillage Effects of experimental conditions –Point in cell cycle, temperature, density

25. 25 Gene Expression Databases and Resources on the Web GEO Gene Expression Omnibus - http://www.ncbi.nlm.nih.gov/geo/ List of gene expression web resources –http://industry.ebi.ac.uk/~alan/MicroArray/ Another list with literature references –http://www.gene-chips.com/ Cancer Gene Anatomy Project –http://cgap.nci.nih.gov/ Stanford Microarray Database –http://genome-www.stanford.edu/microarray/