cDNA Microarrays

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Also known as DNA ChipAllows 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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• High-throughput measuring- 5000-20000 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

microarray

scanning

analysis

cDNA clones(probes)

PCR product amplificationpurification

printing

0.1nl / spotHybridize

RNA

Tumor sample

cDNA

RNA

Reference sample

cDNA

excitation

red lasergreen

laser

emission

overlay images and normalise

Hybridize

RNA

Tumor sample

cDNA

RNA

Reference sample

cDNA

Biological questionDifferentially expressed genesSample 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)

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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Transformed data {(M,A)}n=1..5184:

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

“Observed” data {(R,G)}n=1..5184:

R = red channel signalG = green channel signal

(background corrected or not)

Biased towards the green channel & Intensity dependent artifacts

Scaled print-tip normalization

Median Absolute Deviation (MAD) Scaling

Averaging

Extreme in T values?

Extreme in M values?...or extreme in some other statistics?

Gene: Mavg Aavg TSE

2341 -0.86 10.9 -18.00.125

6412 -0.75 11.1 -14.70.102 6123 -0.70 9.8-12.2 0.121

102 0.65 10.3 -14.50.136 2020 0.64 9.3 -11.90.118

3132 0.62 9.9 -14.40.090

4439 -0.62 9.7 -14.60.088

2031 -0.61 10.7 -13.70.087

657 -0.60 9.2 -13.60.094

502 0.58 10.0 -12.70.101

1239 -0.58 9.8 -11.40.103

5392 -0.57 9.9 -20.70.057

3921 0.52 11.3 13.50.083

...

10. Which genes are actually up- and down regulated?

11. P-values.

12. Planning of experiments:- what is best design?- what is an optimal sample sizes?

13. Classification:- of samples.- of genes.

14. Clustering:- of samples.- of genes.

15. Time course experiments.

16. Gene networks.- identification of pathways

17. ...

1. Image analysis- what is foreground?- what is background?

2. Quality- which spots can we trust?- which slides can we trust?

3. Artifacts from preparing the RNA, the printing, the scanning etc.

4. Data cleanup

5. Normalization within an experiment:- when few genes change.- when many genes change.- dye-swap to minimize dye effects.

6. Normalization between experiments:- location and scale effects.

7. What is noise and what is variability?

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Brown & Botstein, 1999