1. DNA Microarray Bioinformatics - #27612 Normalization and Statistical Analysis

2. DNA Microarray Bioinformatics - #27612 Sample Preparation Hybridization Array design Probe design Question Experimental Design Buy Chip/Array Statistical Analysis Fit to Model (time series) Expression Index Calculation Advanced Data Analysis ClusteringPCAClassification Promoter Analysis Meta analysisSurvival analysisRegulatory Network Comparable Gene Expression Data Normalization Image analysis The DNA Array Analysis Pipeline

3. DNA Microarray Bioinformatics - #27612 Sample Preparation Hybridization Comparable Gene Expression Data Normalization Image analysis The Simplified DNA Array Analysis Pipeline

4. DNA Microarray Bioinformatics - #27612 Gene-specific variation Spotting efficiency, –Spot size –Spot shape Cross-/unspecific hybridization Biological variation –Effect –Noise Global variation Amount of RNA in the sample Efficiencies of: –RNA extraction –Reverse transcription –amplification –Labeling –Photodetection Systematic Two kinds of variation Stochastic

5. DNA Microarray Bioinformatics - #27612 Gene-specific variation : Too random to be explicitly accounted for “noise” Global variation: Similar effect on many measurements Corrections can be estimated from data Normalization Statistical testing Sources of variation SystematicStochastic

6. DNA Microarray Bioinformatics - #27612 Intensities are not just mRNA concentrations Tissue contamination RNA degradation RNA purification Reverse transcription Amplification efficiency Dye effect (cy3/cy5) Spotting DNA-support binding Other issues related to array manufacturing ‘Background’ correction Image segmentation Hybridization efficiency and specificity Spatial effects

7. DNA Microarray Bioinformatics - #27612 Calibration = Normalization = Scaling

8. DNA Microarray Bioinformatics - #27612 Visualizing data MVA plot

9. DNA Microarray Bioinformatics - #27612 Linear normalization

10. DNA Microarray Bioinformatics - #27612 Nonlinear normalization

11. DNA Microarray Bioinformatics - #27612 MAS 5.0 Normalization (Affymetrix) Background: Weighted average of the lowest 2% Uses MM to calculate the ideal mismatch, then adjust the PM intensity Uses the Tukey’s biweight estimator to provide a robust mean Scale to make the means equale for all chips using trimmed mean

12. DNA Microarray Bioinformatics - #27612 The Quantile and Qspline method From the empirical distribution, a number of quantiles are calculated for each of the channels to be normalized (one channel shown in red) and for the reference distribution (shown in black) A QQ-plot is made and a normalization curve is constructed by fitting a cubic spline function As reference one can use an artificial “median array” for a set of arrays or use a log-normal distribution, which is a good approximation.

13. DNA Microarray Bioinformatics - #27612 Lowess Normalization One of the most commonly utilized normalization techniques is the LOcally Weighted Scatterplot Smoothing (LOWESS) algorithm. M A * * * * * * *

14. DNA Microarray Bioinformatics - #27612 Invariant set normalization (Li and Wong) A invariant set of probes is used -Probes that does does not change intensity rank between arrays -A piecewise linear median line is calculated -This curve is used for normalization

15. DNA Microarray Bioinformatics - #27612 Spatial bias estimate Spatial normalization After intensity normalization After spatial normalization Raw dataAfter intensity normalization After intensity normalization After spatial normalization After spatial normalization

16. DNA Microarray Bioinformatics - #27612 Sample Preparation Hybridization Statistical Analysis Fit to Model (time series) Comparable Gene Expression Data Normalization Image analysis The Simplified DNA Array Analysis Pipeline

17. DNA Microarray Bioinformatics - #27612 The t-test A test for whether two distrubutions has the same mean. Gives a p-value for each gene (need replications). You need to adjust your p-value for multiple testing. Bonferroni correction: P = 0.05/N