experiment design for affymetrix microarray

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