5. Experiment Design for Affymetrix Microarray

7. Probe: A 25mer oligo complemetary to a sequence of interest, attached to a glace surface on the probe array Perfect Match: (PM) Probes that are complementary to the sequence of interest. Mismatch : (MM) Probes that are complementary to the sequence of interest except for homomeric base change (A-T or G-C) at the 13 th position Probe Pair: (PP) A combination of a PM and MM; 11-16 probe pairs/ probe set Probe Cell: A single feature; size can be 18X18 or 20X20u Affymetrix Terminology

11. Experimental Design Flow Pilot Study Simplified Data Analysis Full Scale Experiment Complete Analysis Bioinformatics Data Validation Publication

12. Advantages of a Pilot Study Estimate experimental variability Refine laboratory methods/techniques Refine experimental design Allows for rapid screening Provides preliminary data for project funding

13. Three Sources of Variability Biological : Differences between samples - The ultimate goal of the research Technical: Sample preparation - Protocols and operator System: Probe Array analysis - Arrays, instruments, reagents

14. Controlling Biological Variability Biological variability contributes more to experimental variability than technical variability. To mitigate biological variability:- - Consider all potential variables as part of the experiment design - Increase the number of biological replicates until Coefficient of Variation (CV) stabilizes

15. Examples of Biological Variability Cell Cycle Patterns- What time of day were the samples isolated? Circadian Rhythm- What is the time interval between time course samples? Nutrient- Media types will affect expression levels Tissue- Each cell type has different expression pattern Temperature- Growth room temperature may vary within a 24h period Disease- Defense genes will alter global gene expression pattern Germination time- Different seed batches will alter gene expression pattern

16. Practical Questions to Consider How much variability does your system have? - Understand and minimize variation What level of significance is needed? - More replicates needed for subtle changes How many treatments? How many controls? - Comparative analysis (one experimental condition) or serial analysis design (multiple experimental conditions)?

17. Percentage CV as Estimate of Variability CV% is a measure of variance amongst replicates of a single condition Defined as the standard deviation divided by the mean multiplied by 100 Example: 6 signal values representing 6 replicates - 230.4, 241.7, 252.9, 338.8, 178.9, 339.6 - Mean = 263.72; = 63.72; CV% = 24.16%

21. Experimental Replicates Technical replicates from the same sample reproduce the contribution from the bench effects to the overall variability Biological replicates: True replicates that reproduce biological conditions explored in the experimental design - Permit the use of formal statistical tests - Also allows the interrogation of technical variability

22. RNA Sample Pooling Can increase sample quantity A common variance mitigation strategy Can result in irreversible loss of information by introducing a bias If necessary pool a minimum of three or a maximum of five RNAs Equal pooling of RNA samples is essential

23. Data Normalization

24. Why Normalize ? To correct for systematic measurement error and bias in data Allows for data comparison