1 / 50

Experiment Design for Affymetrix Microarray

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.

simmonsr
Download Presentation

Experiment Design for Affymetrix Microarray

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Experiment Design for Affymetrix Microarray

  2. 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

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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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)?

  9. 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%

  10. 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

  11. 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

  12. Data Normalization

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

More Related