1. Mathematical Statistics, Centre for Mathematical Sciences
cDNA Microarrays -
an introduction
Henrik Bengtsson
Bioinformatics Group
Mathematical Statistics, Centre for Mathematical Sciences
Lund University

2. Outline The Genomic Code The Central Dogma of Biology
The cDNA Microarray Technique
Data Analysis of cDNA Microarray Data
Statistical Problems
Take-home message

3. The Genomic Code 120.000 genes ? 80.000 genes ? 35.000 genes ? or ?
22+1 chromosome
pairs
genes ?
genes ?
genes ?
or ? bp

4. The Central Dogma of Biology
DNA
RNA
Protein
transcription
translation
CCTGAGCCAACTATTGATGAA
PEPTIDE
CCUGAGCCAACUAUUGAUGAA

5. 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
...

6. Example of a cDNA Microarray

7. 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

8. Creating the slides

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

11. Scanning & Image Analysis

12. Data Output

13. 16-bit TIFF files (Rfg, Rbg), (Gfg, Gbg) R, G 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)

14. 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

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

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

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

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

19. Time Course Gene Expression Profiles

20. 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
...

21. Total microarray articles indexed in Medline
100
200
300
400
500
600
(projected)
Year
Number of papers

22. Acknowledgments/Collaborators
Statistics Dept, UC Berkeley:
Sandrine Dudoit
Terry Speed
Yee Hwa Yang
Oncology Dept, Lund University:
Pär-Ola Bendahl
Åke Borg
Johan Vallon-Christersson
Enerst Gallo Research Inst., California:
Monica Moore
Karen Berger
Endocrinology, Lund University, Malmö:
Leif Groop
Peter Almgren
Lawrence Berkeley National Laboratory:
Saira Mian
Matt Callow
Mathematical Statistics, Chalmers University:
Olle Nerman
Staffan Nilsson
Dragi Anevski
CSIRO Image Analysis Group, Melbourne:
Michael Buckley

23. Take-home message Bioinformatics is the future!
More educated people are needed!
Statistics is fun when it is applied!
Master’s thesis project? Talk to us!

24. Finding genes in DNA sequence
“This is one of the most challenging and interesting problems in computational biology at the moment. With so many genomes being sequenced so rapidly, it remains important to begin by identifying genes computationally.” – Terry Speed.

25. The Central Dogma of Biology
Challenges:
Sequencing
Fragment assembly
Gene finding
Linkage analysis etc
Homology searches
Annotation
DNA
RNA
Protein
transcription
translation
Isolation
Sequencing
RNA structure prediction
Gene expression:
microarrays etc
Protein structure prediction
Protein folding
Homology searches
Functional pathways
Annotation