Presentation is loading. Please wait.

Presentation is loading. Please wait.

Experiment Design for Affymetrix Microarray.

Similar presentations


Presentation on theme: "Experiment Design for Affymetrix Microarray."— Presentation transcript:

1

2

3

4

5 Experiment Design for Affymetrix Microarray

6

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; probe pairs/ probe set Probe Cell: A single feature; size can be 18X18 or 20X20u Affymetrix Terminology

8

9

10

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 , 241.7, 252.9, 338.8, 178.9, Mean = ; = 63.72; CV% = 24.16%

18

19

20

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

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51


Download ppt "Experiment Design for Affymetrix Microarray."

Similar presentations


Ads by Google