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Normalization For MicroArrays

Normalization For MicroArrays. A Tutorial Introduction David Hoyle University of Manchester. http://www.bioinf.man.ac.uk/microarray. Why Normalization ?. To remove systematic biases, which include,. Sample preparation Variability in hybridization Spatial effects Scanner settings

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Normalization For MicroArrays

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  1. Normalization For MicroArrays A Tutorial Introduction David Hoyle University of Manchester http://www.bioinf.man.ac.uk/microarray

  2. Why Normalization ? To remove systematic biases, which include, • Sample preparation • Variability in hybridization • Spatial effects • Scanner settings • Experimenter bias

  3. What Normalization Is & What It Isn’t • Methods and Algorithms • Applied after some Image Analysis • Applied before subsequent Data Analysis • Allows comparison of experiments • Not a cure for poor data.

  4. Sample Preparation Scanning + Image Analysis Hybridization Normalization Data Analysis Array Fabrication Where Normalization Fits In Normalization Subsequent analysis, e.g clustering, uncovering genetic networks Spot location, assignment of intensities, background correction etc.

  5. Choice of Probe Set • House keeping genes – e.g. Actin, GAPDH • Larger subsets – Rank invariant sets Schadt et al (2001) J. Cellular Biochemistry37 • Spiked in Controls • Chip wide normalization – all spots Normalization method intricately linked to choice of probes used to perform normalization

  6. Form of Data Working with logged values gives symmetric distribution Global factors such as total mRNA loading and effect of PMT settings easily eliminated.

  7. Mean & Median Centering • Simplist Normalization Procedure • Assume No overall change in D.E.  Mean log (mRNA ratio) is same between experiments. • Spot intensity ratios not perfect  log(ratio)  log(ratio) – mean(log ratio) or log(ratio)  log(ratio) – median(log ratio) more robust

  8. 0 0 Location & Scale Transformations Mean & Median centering are examples of location transformations

  9. 0 0 Location & Scale Transformations Scale Transformation = Multiply all values by a constant Scale transformations can also be applied where scale of experiments is believed to be comparable.This may or may not make biological sense

  10. Regression Methods • Compare two hybridizations (exp. and ref) – use scatter plot • If perfect comparability – straight line through 0, slope 1 • Normalization – fit straight line and adjust to 0 intercept and slope 1 • Various robust procedures exist

  11. log R M log G A M = log R – log G A = ½[ log R + log G ] M = Minus A = Add M-A Plots M-A plot is 45° rotation of standard scatter plot 45°

  12. M M A A M-A Plots Un-normalized Normalized Normalized M values are just heights between spots and the “general trend” (red line)

  13. Methods To Determine General Trend • Lowess (loess) Y.H. Yang et al, Nucl. Acid. Res. 30 (2002) • Local Average • Global Non-linear Parametric Fit e.g. Polynomials • Standard Orthogonal decompositions e.g. Fourier Transforms • Non-orthogonal decompositions e.g. Wavelets

  14. Lowess Gasch et al. (2000) Mol. Biol. Cell 11, 4241-4257

  15. M A Lowess Demo 1

  16. M A Lowess Demo 2

  17. M A Lowess Demo 3

  18. M A Lowess Demo 4

  19. M A Lowess Demo 5

  20. M A Lowess Demo 6

  21. M A Lowess Demo 7

  22. M A Lowess Demo 8 Kernel Too Narrow

  23. M A Lowess Demo 9 Kernel Too Wide

  24. M A Lowess Demo 10 Span f Span f  20% – 40%

  25. Lowess Demo 11

  26. Things You Can Do With Lowess (and other methods) Bias from different sources can be corrected sometimes by using independent variable. • Correct bias in MA plot for each print-tip • Correct bias in MA plot for each sector • Correct bias due to spatial position on chip

  27. Print Tip Normalization S. Dudoit et al (2002), Statistica Sinica12, 111-139

  28. Non-Local Intensity DependentNormalization

  29. Pros & Cons of Lowess • No assumption of mathematical form – flexible • Easy to use • Slow - unless equivalent kernel pre-calculated • Too flexible ? Parametric forms just as good and faster to fit.

  30. Paired Slide Normalization (Large Differential Expression) M, A from one hybridization M’, A’ from dye swap M’  -M, A’ A , but bias is intensity dependent same for A & A’ ½[M-M’] good normalized value at ½[A+A’]

  31. Paired Slide Normalization(General) • Paired Slide Normalization valid even if D.E. is not large • Reproducibility is greatest when using self-normalization using paired slides Dr. YongXiang Fang – unpublished • Dye swaps a good idea if you can afford them.

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