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.

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Normalization for cDNA Microarray Data

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Normalization for cdna microarray data

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

Normalization issues

Within-slide

  • What genes to use

  • Location

  • Scale

    Paired-slides (dye swap)

  • Self-normalization

    Between slides


Within slide normalization

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


Normalization for cdna microarray data

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


Normalization for cdna microarray data

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.


M vs a

M vs. A

M = log2(R / G)

A = log2(R*G) / 2


Normalization median

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

Normalization - lowess

  • Global lowess

  • Assumption: changes roughly symmetric at all intensities.


Normalisation print tip group

Normalisation - print-tip-group

Assumption:For every print group, changes roughly symmetric

at all intensities.


M vs a after print tip group normalization

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


Effects of location normalisation

Effects of Location Normalisation

Before normalisation

After print-tip-group

normalisation


Within print tip group box plots for print tip group normalized m

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


Taking scale into account

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) | }


Effect of location scale normalization

Effect of location + scale normalization


Effect of location scale normalization1

Effect of location + scale normalization


Comparing different normalisation methods

Comparing different normalisation methods


Follow up experiment

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.


Normalization for cdna microarray data

Follow-up Experiment


Paired slides dye swap

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.


Verify assumption

Verify Assumption


Result of self normalization

Result of Self-Normalization

Plot of (M - M’)/2 vs. (A + A’)/2


Summary

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.


Normalization for cdna microarray data

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/


Acknowledgments

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


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