1. DNA Microarray

2. Microarray Printing 96-well-plate (PCR Products) 384-well print-plate Microarray

4. Differential Expression Each cell contains a complete copy of the organism’s genome Cells are of many different types and state e.g. blood, nerve, skin cells, etc What makes the cells different ? Differential gene expression, i.e., when, where and in what quantity each gene is expressed On average, 40% of our genes are expressed at any given time

5. Functional genomics The various genome projects have yielded the complete DNA sequences of many organisms. e.g. human, mouse, yeast, fruitfly, etc. Human: 3 billion base-pairs, 30-40 thousand genes. Challenge: go from sequence to function, i.e., define the role of each gene and understand how the genome functions as a whole.

6. Central Dogma The expression of the genetic information stored in the DNA Molecule occurs in two stages: --transcription, during which DNA is transcribed into mRNA; --translation, during which mRNA is translated to produce a protein. DNA mRNA Protein cDNA Arrays Tissue Arrays

10. The Central Dogma of Molecular Biology

11. Microarray Hybridization

12. Microarray Gene Expression Image

13. A Better Look

14. Cy3Cy5 Cy3 Cy5 Cy3 log 2 Genes Experiments 8 4 2 fold 2 4 8 Underexpressed Overexpressed Image Analysis & Data Visualization

15. New Data ScanAlyze/GenePix Database Data Selection Complete Data Table (cdt) Cluster SOM K-means SVD SpotList

16. Ovarian Tumor Study M. Schaner Samples that should Cluster together do not

18. Data Normalization

19. Pool of Cell LinesTumor Different amounts of starting material.

20. Different amounts of RNA in each channel

21. Differential labeling efficiency of dyes

22. Differential efficiency of hybridization over slide surface

23. Differential efficiency of scanning in each channel.

24. Such biases have consequences: Plotting the frequency of un-normalized intensities reveals the differential effect between the two c hannels.

25. How do we deal with this? Normalization: In general, an assumption is made that the average gene does not change. You must understand your experiment and data to judge whether that assumption is a good one. Usually true for gene expression experiments, but not necessarily for aCGH or chromatin IP. Generally true for large arrays, but not for small " boutique" arrays.

26. Data may have an intensity-dependent structure. Plot log2(R/G) vs. log10(R*G) to reveal this Reveals : variance in log ratios is greater at lower intensities. distribution may not be centered around zero. Normalization : The R-I Plot

27. R-I Plot, Raw DataR-I Plot Following Loess Normalization: Loess log10(R*G) log2(R/G)

28. Cluster Analysis Cell Cycle example( Spellman 1988)

29. Overview of the Cell Cycle Purpose: –To create two new cells by dividing one original cell

30. Cell Cycle: Key Concepts –All parts of original cell must be replicated and split between new cells –Each step must occur in precise manner and timing for successful cycle, and is strictly regulated –mRNA and proteins for cell cycle genes are found at varying levels at different points of the cycle –Mutations causing malfunction in regulation can result in cancer

31. Yeast Cell Cycle

32. Cell Cycle: Basic Description http://www.bmb.psu.edu/courses/biotc489/notes/cycle.jpg

35. Cells grow out of synchrony.