experiment design for affymetrix microarray
TRANSCRIPT
Experiment Design forAffymetrix Microarray
A ffym etrixW afer and Chip Form at
1.28cm
18- 20 µm
18 - 20 µm
M illions of identical oligonucleotide
probes per feature
49 - 400 chips/wafer
up to ~ 500,000 features/chip
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 13th 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
Selection of Expression Probes
Probes
Sequence
Perfect Match
MismatchChip
5’ 3’
Procedures for Target Preparation
cDNA
Wash & Stain
Scan
Hybridise
(16 hours)
RNAAAAA
B B B B
Biotin-labeled transcripts Fragment
(heat, Mg2+)
Fragmented cRNA
B B
B
B
IVT(Biotin-UTPBiotin-CTP)
GeneChip® Expression AnalysisHybridization and Staining
Array
cRNA Target
Hybridized Array
Streptravidin-phycoerythrinconjugate
Experimental Design Flow
Pilot StudySimplified Data Analysis
Full Scale Experiment
Complete Analysis
BioinformaticsData Validation
Publication
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
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
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
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
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)?
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%
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
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
Data Normalization
Why Normalize ?
• To correct for systematic measurement error and bias in data
• Allows for data comparison