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Filtering and Normalization of Microarray Gene Expression Data

Filtering and Normalization of Microarray Gene Expression Data. Waclaw Kusnierczyk Norwegian University of Science and Technology Trondheim, Norway. Outline. Filtering: spots removal of spots based on quality measures Normalization compensation for measurement errors

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Filtering and Normalization of Microarray Gene Expression Data

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  1. Filtering and Normalizationof Microarray Gene Expression Data Waclaw Kusnierczyk Norwegian University of Science and TechnologyTrondheim, Norway

  2. Outline • Filtering: spots • removal of spots based on quality measures • Normalization • compensation for measurement errors • Examples of common problems

  3. Channel - channel plot (CC) Intensity - ratio plot (AM or IR) Useful plots

  4. Filtering: Spots • Criteria used to remove spots • spot area [pixels] • signal/noise ratio (spot intensity vs. background intensity) • other quality measures (e.g. based on quality scores from image analysis software) • morphological criteria • pixel-level variability

  5. Filtering: Spots • Spot area

  6. Filtering: Spots • Spot area based filtering • keep spots with area >threshold in both channels • problem: setting the appropriate threshold • dependent on the definition of the spot (image analysis software), and the distribution of the spot area • typical value: 10 pixels

  7. Filtering: Spots • Signal and background

  8. Filtering: Spots • Signal/noise based filtering • keep spots with signal / background > threshold in both channels • problem: setting the appropriate threshold • dependent on the spot and background definition (image analysis software) • typical value: sgn/bkg > 2 (or, equivalent, sgn - bkg > bkg)

  9. Filtering: Spots • Signal/noise based filtering (alternative) • flag spots if Sij< Bij+θσBij, where:Sij: ith spot intensity in jth channel (not corrected)Bij: ith spot background in jth channelσBij: ith spot background deviation in jth channelθ: user defined threshold

  10. Filtering: Spots (example)

  11. Filtering: Spots • Other criteria • Intensity threshold on background corrected intensity (for each channel separately) • Spot quality measures (pixelwise distributional properties of spot and background intensities, manual morphology-based spot flagging etc.) • Replicate-based spot filtering (adaptive threshold selection based on a repeatability coefficient, coefficient of variation etc.)

  12. Filtering: Spots • Total intensity (log2) threshold

  13. Filtering: Spots • Morphology based filtering

  14. Normalization • Analysis of systematic errors • adjustment for bias coming from variation in the technology rather than from biology • Different sources of non-linearity • Print-tip differences • Efficiency of dye incorporation (labelling) • Non-uniformity in hybridisation • Scanning • Between slide variation (print quality, ambient conditions)

  15. Normalization • Selection of elements • Housekeeping genes, spike controls, tip-dependence, raw data, between array normalization • Method • Constant subtraction (shift)(mean/median log2 ratio, iterative c estimation, ANOVA) • Locally weighted mean(intensity or location dependent) • Other recently proposed methods

  16. Normalization (example 1) • Intensity independent normalization with median ratio subtraction

  17. Normalization (example 1) • Intensity independent normalization with median ratio subtraction

  18. Normalization (example 1) • Intensity dependent normalization with locally weighted mean, global

  19. Normalization (example 1) • Intensity dependent normalization with locally weighted mean, print-tip dependent

  20. Normalization (example 1) • Intensity dependent normalization with locally weighted mean, global vs. print-tip dependent

  21. Normalization (example 2) • Intensity dependent normalization with locally weighted mean, print-tip dependent

  22. Normalization • Location dependent normalization with locally weighted mean (from SNOMAD web page)

  23. Common problems: examples

  24. Common problems: examples

  25. Common problems: examples

  26. Common problems: examples

  27. Common problems: examples

  28. Common problems: examples

  29. Common problems: examples

  30. Acknowledgments Mette Langaas Department of Mathematical Sciences, Norwegian Institute of Science and Technology Astrid Lægreid, Kristin Nørsett Department of Physiology and Biomedical Engineering, Norwegian Institute of Science and Technology Per Kristian Lehre Department of Computer and Information Science,Norwegian Institute of Science and Technology

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