Normalization of 2 color arrays
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Normalization of 2 color arrays
Alex Sánchez. Dept. Estadística
Universitat de Barcelona
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Quality Measurement
Biological verification and interpretation
Microarray experiment
Experimental design
Image analysis
Normalization
Biological question
TestingEstimation Discrimination
Analysis
Clustering
Failed
Pass
Quality Measurement
Biological verification and interpretation
Microarray experiment
Experimental design
Image analysis
Normalization
Biological question
TestingEstimation Discrimination
Analysis
Clustering
Failed
Pass
Microarray studies life cycle
Here we are
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What is normalization?
The word normalization describes techniques used to suitably transform the data before they are analysed.
Goal is to correct for systematic differencesbetween samples on the same slide, orbetween slides,
which do not represent true biological variation between samples.
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The origin of systematic differences
Systematic differences may be due to …Dye biases which vary with spot intensity, Location on the array, Plate origin, Printing quality which may vary between
Pins Time of printing
Scanning parameters,…
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How to know if it’s necessary?
Option 1: to perform self-self normalization If we hibridize a sample with itself instead of
sample vs control intensities should be the same in both channels
All deviations from this equality means there is systematic bias that needs correction
Option 2: Look at diagnostic plots for dye, slide or spatial effects
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False color overlay Boxplots within pin-groups Scatter (MA-)plots
Self-self hybridizations
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Some non self-self hybridizations
From the NCI60 data set Early Ngai lab, UC Berkeley
Early PMCRI, Melbourne AustraliaEarly Goodman lab, UC Berkeley
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Normalization methods & issues
MethodsGlobal adjustment Intensity dependent normalizationWithin print-tip group normalizationAnd many other…
Selection of spots for normalization
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Global normalization
Based on a global adjustment
log2 R/G log2 R/G - c = log2 R/(kg)
Choices for k or c = log2k are c = median or mean of log ratios for a
particular gene set All genes or control or housekeeping genes.
Total intensity normalization, where K = ∑Ri/ ∑Gi.
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Example: (Callow et al 2002)Global median normalization.
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Intensity-dependent normalization
Run a line through the middle of the MA plot, shifting the M value of the pair (A,M) by c=c(A), i.e. log2 R/G log2 R/G - c (A)
= log2 R/(k(A)G). One estimate of c(A) is made using the
LOWESS function of Cleveland (1979): LOcally WEighted Scatterplot Smoothing.
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Example: (Callow et al 2002)loess vs median normalization.
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Example: (Callow et al 2002)Global median normalization.
Global normalization performs a global correction but it cannot account for spatial effects See next slide
boxplots for the same situations in only one mouse, showing all sectors
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Global normalisation does not correct spatial bias (print-tip-sectors)
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Within print-tip group normalization
To correct for spatial bias produced by hybridization artefacts or print-tip or plate effects during the construction of arrays.
To correct for both print-tip and intensity-dependent bias perform LOWESS fits to the data within print-tip groups, i.e.Log2 R/G log2 R/G - ci(A) = log2 R/(ki(A)G),
where ci(A) is the LOWESS fit to the MA-plot for the ith grid only.
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Local print-tip normalisation corrects spatial bias (print-tip-sectors)
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Normalization, which spots to use?
LOWESS can be run through many different sets of points,
All genes on the array. Constantly expressed genes
(housekeeping). Controls. Spiked controls (genes from distant species). Genomic DNA titration series. Rank invariant set.
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Strategies for selecting a set of spots for normalization Use of a global LOWESS approach can
be justified by supposing that, when stratified by mRNA abundance,
a) Only a minority of genes expected to be differentially expressed,
b) Any differential expression is as likely to be up-regulation as down-regulation.
Pin-group LOWESS requires stronger assumptions: that one of the above applies within each pin-group.
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Summary
Microarray experiments have many “hot spots” where errors or systematic biases can apper
Visual and numerical quality control should be performed
Usually intensities will require normalisation At least global or intensity dependent normalisation
should be performed More sophisticated procedures rely on stronger
assumptions Must look for a balance