Gene Expression Data

1. Gene Expression Data Qifang Xu

2. Outline • cDNA Microarray Technology • Data Representation • Statistical Analysis of cDNA Microarray Data • Oligonucleotide Microarray Technology • Microarray Applications • Microarray Databases

3. cDNA Microarray • Measure the relative levels of expression • Parallel analysis • Competitive hybridization • Need cDNA library mRNA cDNA Reverse Transcription

4. PCR Amplification Samples Reverse Transcription Printing Labeling Hybridization Laser Scan Expression Data

6. Exponential Amplification of a Gene Return

7. Labeling and Hybridization of Sample cDNAs Return

8.   low expression level high Data Description • Ratios, why? Competitive hybridization  no absolute expression levels • Gene expression levels determined by intrinsic properties of each gene Gene B Gene A Sample 1 Sample 2

9. Data Normalization • Purpose Adjust bias from variation in microarray technology. E.g. differences between labeling, scanner setting, spatial positions • Within-array normalization logarithmic transformation of ratio, subtract by mean log ratio

10. Statistical Analysis • Differences in ratios due to • random variation • meaningful changes • Convention • ratio >= 2 or ratio <= ½ • Analysis of variance (ANOVA) • 4 and 10 replicates of each treatment • statistical significance

11. Oligonucleotide Microarray • Parallel analysis of gene expression • Unit of hybridization: oligonucleotides represent known or predicted open reading frames (ORF) • Noncompetitive hybridization • Data Representation average difference between perfect match and mismatch intensities • Difference in expression levels between two samples comparison across chips

12. Oligonucleotide Microarray

13. Construction of oligonucleotide arrays

14. Comparison between cDNA and Oligonucleotide Microarray • cDNA Microarray • cheaper  replicates • hybridization over kilobases (1 – 2 kb)  reduce cross-hybridization • genome sequence unknown • cDNA library • Competitive hybridization • Oligonucleotide Microarray • accommodate higher densities of genes  predicted genes not in cDNA library • lower variability • incorporate mismatch controls  reduce possibility of cross-hybridization • Sequence known • Noncompetitive hybridization

15. Microarray Applications • Drug Discovery – Identification of drug targets to treat diseases • Drug Development – rule out potential drug candidates before entering trials • Diagnostics and Disease Treatment – Screening and identification of patient samples • Model Organisms mouse brain model Life cycle of a fruitfly • Genetic Regulatory Network

16. Gene Expression Databases • Stanford Microarray Database (SMD) • http://genome-www5.stanford.edu/MicroArray/SMD/ • raw and normalized data, image files • The Gene Expression Database (GXD) • http://www.informatics.jax.org/mgihome/GXD/aboutGXD.shtml • Endogenous gene expression during mouse development. • ExpressDB • http://twod.med.harvard.edu/ExpressDB/ • Relational database for yeast and E. coli RNA expression data

17. Thank you !