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 P th 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 c i.

13. Conclusions Estimate of N o /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.