Transcript John Okyere`s TARGET talk

Experiment Design for
Affymetrix Microarray
Affymetrix Terminology
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
Selection of
Expression Probes
5’
3’
Sequence
Probes
Perfect Match
Mismatch
Chip
Procedures for Target Preparation
B
Biotin-labeled
transcripts
B
B
B
B
Fragment
(heat, Mg2+)
B
B
B
Fragmented cRNA
IVT
AAAA
RNA
(Biotin-UTP
Biotin-CTP)
Wash & Stain
Scan
cDNA
Hybridise
(16 hours)
GeneChip® Expression Analysis
Hybridization and Staining
Array
Hybridized Array
cRNA Target
Streptravidinphycoerythrin
conjugate
Experimental Design Flow
Simplified Data Analysis
Pilot Study
Full Scale Experiment
Publication
Data Validation
Bioinformatics
Complete Analysis
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