1. Getting the numbers comparable
Normalization
Getting the numbers comparable

2. The DNA Array Analysis Pipeline
Question
Experimental Design
Array design
Probe design
Sample Preparation
Hybridization
Buy Chip/Array
Image analysis
Normalization
Expression Index
Calculation
Comparable
Gene Expression Data
Statistical Analysis
Fit to Model (time series)
Advanced Data Analysis
Clustering PCA Classification Promoter Analysis
Meta analysis Survival analysis Regulatory Network

3. Intensities are not just mRNA concentrations
Tissue contamination
RNA degradation
RNA purification
Reverse transcription
Amplification efficiency
Dye effect (cy3/cy5)
Spotting
DNA-support binding
Other issues related to array manufacturing
‘Background’ correction
Image segmentation
Hybridization efficiency and specificity
Spatial effects

4. Gene-specific variation
Two kinds of variation
Global variation
Amount of RNA in the biopsy
Efficiencies of:
RNA extraction
Reverse transcription
amplification
Labeling
Photodetection
Gene-specific variation
Spotting efficiency,
Spot size
Spot shape
Cross-/unspecific hybridization
Biological variation
Effect
Noise
Systematic
Stochastic

5. Stochastic noise we use statistics to deal with
PCA Plot of 34 patients, 8973 dimensions (genes) reduced to 2

6. ...like we will see later
PCA for 100 most significant genes reduced to 2 dimensions

7. Gene-specific variation:
Sources of variation
Global variation:
Similar effect on many
measurements
Corrections can be estimated from data
Gene-specific variation:
Too random to be explicitly accounted for
“noise”
Systematic
Stochastic
Normalization
Statistical testing

8. Calibration = Normalization = Scaling

9. Nonlinear normalization

10. The Qspline method
From the empirical distribution, a number of quantiles are calculated for each of the channels to be normalized (one channel shown in red) and for the reference distribution (shown in black)
A QQ-plot is made and a normalization curve is constructed by fitting a cubic spline function
As reference one can use an artificial “median array” for a set of arrays or use a log-normal distribution, which is a good approximation.

11. Accumulating quantiles
Once again…qspline
Accumulating quantiles
When many microarrays are to be normalized to each other an average array can be used as target

12. Lowess Normalization M A * * * *
One of the most commonly utilized normalization techniques is the LOcally Weighted Scatterplot Smoothing (LOWESS) algorithm.

13. Invariant set normalization (Li and Wong)
A invariant set of probes is used
Probes that does does not change intensity rank between arrays
A piecewise linear median line is calculated
This curve is used for normalization

14. Spatial normalization
After intensity
normalization
After spatial
normalization
Raw data
After intensity
After intensity
Spatial bias
estimate
After spatial
After spatial
normalization
normalization
normalization
normalization

15. The DNA Array Analysis Pipeline
Question
Experimental Design
Array design
Probe design
Sample Preparation
Hybridization
Buy Chip/Array
Image analysis
Normalization
Expression Index
Calculation
Comparable
Gene Expression Data
Statistical Analysis
Fit to Model (time series)
Advanced Data Analysis
Clustering PCA Classification Promoter Analysis
Meta analysis Survival analysis Regulatory Network

16. Expression index value
Some microarrays have multiple probes addressing the expression of the same gene
Affymetrix chips have probe pairs pr. Gene
- Perfect Match (PM)
- MisMatch (MM)
PM: CGATCAATTGCACTATGTCATTTCT
MM: CGATCAATTGCAGTATGTCATTTCT
However for downstream analysis we often want to deal with only one value pr. gene.
Therefore we want to collapse the intensities from many probes into one value:
a gene expression index value

17. Expression index calculation
Simplest method?
Median
But more sophisticated methods exists:
dChip, RMA and MAS 5 (from Affymetrix)

18. dChip (Li & Wong) Model: PMij = qifj + eij Outlier removal:
Identify extreme residuals
Remove
Re-fit
Iterate
Distribution of errors eij assumed
independent of signal strength
(Li and Wong, 2001)

19. RMA
Robust Multi-array Average (RMA) expression measure (Irizarry et al., Biostatistics, 2003)
For each probe set, re-write PMij = qifj as:
log(PMij)= log(qi ) + log(fj)
Fit this additive model by iteratively re-weighted least-squares or median polish

20. MAS. 5 MicroArray Suite version 5 uses
MM* is an adjusted MM that is never bigger than PM
Tukey biweight is a robust average procedure with weights and outlier rejection

21. Std Dev of gene measures from 20 replicate arrays
Methods compared on expression variance
Std Dev of gene measures from 20 replicate arrays
Std Dev of gene measures from 20 replicate arrays
Expression level
Blue and Red: RMA; Black: dChip; Green: MAS5.0
From Terry speed

22. Robustness MAS5.0 MAS 5.0 Log fold change estimate from 20ug cRNA
(Irizarry et al., Biostatistics, 2003)
MAS 5.0
Log fold change estimate from 1.25ug cRNA
Log fold change estimate from 20ug cRNA

23. Robustness dChip dChip Log fold change estimate from 20ug cRNA
(Irizarry et al., Biostatistics, 2003)
dChip
Log fold change estimate from 20ug cRNA
Log fold change estimate from 1.25ug cRNA

24. Robustness RMA RMA Log fold change estimate from 20ug cRNA
(Irizarry et al., Biostatistics, 2003)
RMA
Log fold change estimate from 20ug cRNA
Log fold change estimate from 1.25ug cRNA

25. All of this is implemented in…R
In the BioConductor packages ‘affy’
(Gautier et al., 2003).

26. References
Li and Wong, (2001). Model-based analysis of oligonucleotide arrays: Model validation, design issues and standard error application.
Genome Biology 2:1–11.
Irizarry, Bolstad, Collin, Cope, Hobbs and Speed, (2003) Summaries of Affymetrix GeneChip probe level data.
Nucleic Acids Research 31(4):e15.)
Affymetrix. Affymetrix Microarray Suite User Guide. Affymetrix, Santa Clara, CA, version 5 edition, 2001.
Gautier, Cope, Bolstad, and Irizarry, (2003). affy - an r package for the analysis of affymetrix genechip data at the probe level. Bioinformatics