Bioinformatics, 201 2 .11. 15 Gene Expression Profiling by Microarray

1. Bioinformatics, 2012.11.15Gene Expression Profiling by Microarray Chun-Ju Chang, Ph.D. chunju@ntou.edu.tw Department of Food Science College of Life Sciences National Taiwan Ocean University

2. Griffin & Shockcor . Nature Reviews Cancer 2004, 4:551.

3. High Throughput Gene Discovery Sutliff J. Science 2001, 291:1224. Solution for genomics study

4. Gene chip (DNA chip, DNA microarray) Nucleic Acids Res. 1992, 20:1679. Microarray technology evolved from Southern blotting, where fragmented DNA is attached to a substrate and then probed with a known gene or fragment.

5. Estimation Functional enrichment Filtering Pathway analysis Clustering Data interpretation Biological verification Discrimination Schematic of microarray analysis Failed Quality measurement Pre-Processing Passed Analysis

6. Outline • Microarray platforms • Experimental design • Sources of variability • Sample size and replication • Data acquisition and preprocessing • Normalization • Quality control • Data analysis • Partitional clustering • Functional annotation • Pathway analysis • Gene expression databases

7. Microarray platforms 1

8. Microarray platforms 2

9. Affymetrix Miller & Tang. Microbiol Rev. 2009,22:611.

10. Illumina 250,000probes/bead

11. Experimental design 1 Sources of variation in a microarray experiment : • Manufacturing of arrays • Generation of biological sample • Genetic and environmental factors • Pooled or individual samples • Randomization • Technical variation • Preprocessing : RNA extraction, labeling, etc. • Protocolization of the processing steps • Processing of samples • Obtaining image • “Biological replicates”, “technical replicates”

12. Experimental design 2 Sample size and replication: • 4 types of experimental designs • Completely randomized treatment-control design: each measurement is considered independent • Matched-pairs design • Multiple treatment design having an independent treatment effect • Randomized block design

13. Data acquisition and preprocessing Common normalization strategies • Total intensity normalization • Normalization using regression techniques • Normalization using ratio statistics

14. Data acquisition and preprocessing Quality control • From the Microarray Gene Expression Data (MGED) Society; presently named Functional Genomics Data (FGED) Society • MIAME (Minimum Information About a Microarray Experiment) standards for data reporting • Spotted cDNA and oligonucleotide arrays • Experimental design: number of replicates, samples used • Preparation and labeling • Hybridization procedures and parameters • Measurement data and specifications • Microarray Gene Expression Markup Language (MAGE-ML) • ArrayExpress microarray database • Universal data-presentation platform

15. Functional Genomics Data (FGED) Society

16. MicroArray Quality Control (MAQC) project Ji H & Davis RW. Nat Biotechnol 2006, 24:1112-3.

17. Scatter plot Hierarchical Trees K-Means Venn diagram PiChart

18. Partitional clustering by K-Means  cluster centers, prototypes 反覆疊代

19. Functional annotationby GO(GeneOntology) 

28. EMBL-EBI

30. 30

31. Hands-on Practice