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Normalization for cDNA Microarray DataPowerPoint Presentation

Normalization for cDNA Microarray Data

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### Normalization for cDNA Microarray Data

Yee Hwa Yang, Sandrine Dudoit, Percy Luu and Terry Speed.

SPIE BIOS 2001, San Jose, CA

January 22, 2001

Normalization issues

Within-slide

- What genes to use
- Location
- Scale
Paired-slides (dye swap)

- Self-normalization
Between slides

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

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

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.

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

Normalization - lowess

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

Normalisation - print-tip-group

Assumption:For every print group, changes roughly symmetric

at all intensities.

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

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) | }

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.

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.

Result of Self-Normalization

Plot of (M - M’)/2 vs. (A + A’)/2

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.

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/

Sandrine Dudoit

Natalie Roberts

Ben Bolstad

Matt Callow (LBL)

John Ngai’s Lab (UCB)

Percy Luu

Dave Lin

Vivian Pang

Elva Diaz

Acknowledgments
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