Deanna Mendez July 9, 2004

1. A Statistical Framework for the Design of Microarray Experiments and Effective Detection of Differential Gene Expression by Shu-Dong Zhang, Timothy W. Gant Deanna Mendez July 9, 2004 SoCalBSI California State University at Los Angeles

2. A Statistical Framework for the Design of Microarray Experiments and Effective Detection of Differential Gene Expression • Introduction • Power • Microarrays • Model • Results • Conclusions

3. Power: Success rate of finding DGE • DGE- differential gene expression • Power is the ability to correctly identify DGEs (orange) and to not identify false DGEs (blue).

4. Microarrays • Very powerful • Many sources of error • Little replication • Random error • Systematic biases • Develop a statistical framework to help identify DGEs

5. Color Changes from Feature to Feature • Random error • Due to intrinsic fluorescence of certain oligonucleotides, there may be an excess of signal compared to other features. • To solve this problem compare each feature to its control on the same slide

6. Model: Dual Label Hybridizations • Systematic bias • Two color experiment • One fluorophore has a greater quantum yield • Remove the effect by doing both forward and reverse labelling.

7. Deriving the Statistics • G is the overall intensity, I is the expression level, A is the feature spot quality, D is the effect of the fluorescent label. • The subscripts are sample group, index, microarray, and color.

8. Reverse Labelling Experiment

9. Overall Statistic

10. Student’s t-Test • Statistic for the null hypothesis • Degrees of freedom • Test statistic estimate the standard deviation • Choose a p threshold

11. Determining Pth • To determine the threshold p value, an estimate of fraction of null genes in the data set must be made. • These authors offer

12. Table of predicted values • Storey and Tibshirani used a natural cubic spline to fit the data of ci.

13. Conclusions • Estimate of No/N is only good for large Ns • It performs better than the previous method in terms of approaching the true fraction and the size of the coefficient of variation • Assumes independence so it may not perform as well for an actual experiment

14. Future Work • Estimate the fraction of null genes with possibly strong inter-gene dependence

15. References • Zhang SD, Gant TW (2004) A Statistical Framework for the Design of Microarray Experiments and Effective Detection of Differential Gene Expression. Bioinformatics.