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Normalization for cDNA Microarray Data

Normalization for cDNA Microarray Data

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Normalization for cDNA Microarray Data

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  1. Normalization for cDNA Microarray Data Yee Hwa Yang, Sandrine Dudoit, Percy Luu and Terry Speed. SPIE BIOS 2001, San Jose, CA January 22, 2001

  2. Normalization issues Within-slide • What genes to use • Location • Scale Paired-slides (dye swap) • Self-normalization Between slides

  3. Within-Slide Normalization • Normalization balances red and green intensities. • Imbalances can be caused by • Different incorporation of dyes • Different amounts of mRNA • Different scanning parameters • In practice, we usually need to increase the red intensity a bit to balance the green

  4. Methods? log2R/G -> log2R/G - c = log2R/ (kG) Standard Practice (in most software) c is a constant such that normalized log-ratios have zero mean or median. Our Preference: c is a function of overall spot intensity and print-tip-group. What genes to use? • All genes on the array • Constantly expressed genes (house keeping) • Controls • Spiked controls (e.g. plant genes) • Genomic DNA titration series • Other set of genes

  5. Experiment mRNA samples R = Apo A1 KO mouse liver G = Control mouse liver (All C57Bl/6) KO #8 Probes: ~6,000 cDNAs, including 200 related to lipid metabolism.

  6. M vs. A M = log2(R / G) A = log2(R*G) / 2

  7. Normalization - Median • Assumption: Changes roughly symmetric • First panel: smooth density of log2G and log2R. • Second panel: M vs. A plot with median set to zero

  8. Normalization - lowess • Global lowess • Assumption: changes roughly symmetric at all intensities.

  9. Normalisation - print-tip-group Assumption:For every print group, changes roughly symmetric at all intensities.

  10. M vs. A - after print-tip-group normalization

  11. Effects of Location Normalisation Before normalisation After print-tip-group normalisation

  12. Within print-tip-group box plots forprint-tip-group normalized M

  13. Taking scale into account Assumptions: • All print-tip-groups have the same spread. True ratio is mij where i represents different print-tip-groups, j represents different spots. Observed is Mij, where Mij = aimij Robust estimate of ai is MADi = medianj { |yij - median(yij) | }

  14. Effect of location + scale normalization

  15. Effect of location + scale normalization

  16. Comparing different normalisation methods

  17. Follow-up Experiment • 50 distinct clones with largest absolute t-statistics from the first experiment. • 72 other clones. • Spot each clone 8 times . • Two hybridizations: Slide 1, ttt -> redctl-> green. Slide 2, ttt -> greenctl->red.

  18. Follow-up Experiment

  19. Paired-slides: dye swap • Slide 1, M = log2 (R/G) - c • Slide 2, M’ = log2 (R’/G’) - c’ Combine bysubtract the normalized log-ratios: [ (log2 (R/G) - c) - (log2 (R’/G’) - c’) ] / 2  [ log2 (R/G) + (log2 (G’/R’) ] / 2  [ log2 (RG’/GR’) ] / 2 provided c = c’ Assumption: the separate normalizations are the same.

  20. Verify Assumption

  21. Result of Self-Normalization Plot of (M - M’)/2 vs. (A + A’)/2

  22. Summary Case 1: A few genes that are likely to change Within-slide: • Location: print-tip-group lowess normalization. • Scale: for all print-tip-groups, adjust MAD to equal the geometric mean for MAD for all print-tip-groups. Between slides (experiments) : • An extension of within-slide scale normalization (future work). Case 2: Many genes changing (paired-slides) • Self-normalization: taking the difference of the two log-ratios. • Check using controls or known information.

  23. http://www.stat.berkeley.edu/users/terry/zarray/Html/ Technical Reports from Terry’s group: http://www.stat.Berkeley.EDU/users/terry/zarray/Html /papersindex.html • Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data • Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. • Comparison of methods for image analysis on cDNA microarray data. • Normalization for cDNA Microarray Data Statistical software R http://lib.stat.cmu.edu/R/CRAN/

  24. Terry Speed Sandrine Dudoit Natalie Roberts Ben Bolstad Matt Callow (LBL) John Ngai’s Lab (UCB) Percy Luu Dave Lin Vivian Pang Elva Diaz Acknowledgments