1. cDNA Microarrays
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2. What is a cDNA Microarray?
Also known as DNA Chip
Allows simultaneous measurement of the level of transcription for every gene in a genome (gene expression)
Transcription?
Process of copying of DNA into messenger RNA (mRNA)
Environment dependant!
Microarray detects mRNA, or rather the more stable cDNA
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3. The cDNA Microarray Technique
High-throughput measuring gene expressions at the same time
Identify genes that behaves different in different cell populations - tumor cells vs healthy cells - brain cells vs liver cells - same tissue different organisms
Time series experiments - gene expressions over time after treatment

4. Overview cDNA clones (probes) printing Hybridize RNA Tumor sample cDNA
PCR product amplification
purification
printing
0.1nl / spot
excitation
red laser
green laser
emission
scanning
Hybridize
RNA
Tumor sample
cDNA
Reference sample
overlay images and normalise
microarray
analysis

5. Creating the slides

7. RNA Extraction & Hybridization
Hybridize
RNA
Tumor sample
cDNA
Reference sample

8. Example of a cDNA Microarray

9. Scanning & Image Analysis

10. Data Output

11. Differentially expressed genes Sample class prediction etc.
Biological question
Differentially expressed genes
Sample class prediction etc.
Testing
Biological verification
and interpretation
Microarray experiment
Estimation
Experimental design
Image analysis
Normalization
Clustering
Discrimination
R, G
16-bit TIFF files
(Rfg, Rbg), (Gfg, Gbg)

12. Reading an array Laser scans array and produces images
One laser for each color, e.g. one for green, one for red
Image analysis, main tasks:
Noise suppression
Spot localization and detection, including the extraction of the background intensity, the spot position, and the spot boundary and size
Data quantification and quality assessment
Image Analysis is a book on its own:
Kamberova, G. & Shah, S. “DNA Array Image Analysis Nuts & Bolts“. DNA Press LLC, 2002
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13. Data Transformation “Observed” data {(R,G)}n=1..5184:
R = red channel signal G = green channel signal
(background corrected or not)
Transformed data {(M,A)}n= :
M = log2(R/G) (ratio),
A = log2(R·G)1/2 = 1/2·log2(R·G) (intensity signal)
 R=(22A+M)1/2, G=(22A-M)1/2

14. Normalization
Biased towards the green channel & Intensity dependent artifacts

15. Replicated measurements
Scaled print-tip normalization
Median Absolute Deviation (MAD) Scaling
Averaging

16. Identification of differentially expressed genes
Extreme in M values?
...or extreme in some other statistics?
Extreme in T values?

17. List of genes that the biologist can understand and verify with other experiments
Gene: Mavg Aavg T SE
...

18. Time Course Gene Expression Profiles

19. Statistical Problems
Image analysis - what is foreground? - what is background?
Quality - which spots can we trust? - which slides can we trust?
Artifacts from preparing the RNA, the printing, the scanning etc.
Data cleanup
Normalization within an experiment: - when few genes change. - when many genes change. - dye-swap to minimize dye effects.
Normalization between experiments: - location and scale effects.
What is noise and what is variability?
Which genes are actually up- and down regulated?
P-values.
Planning of experiments: - what is best design? - what is an optimal sample sizes?
Classification: - of samples. - of genes.
Clustering: - of samples. - of genes.
Time course experiments.
Gene networks. - identification of pathways
...

20. Overview of Example
Brown & Botstein, 1999
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