1. Lecture 22 Introduction to Microarray
CS 5263 Bioinformatics
Lecture 22
Introduction to Microarray

2. Outline What is microarray Basic categories of microarray
How can microarray be used
Computational and statistical methods involved in microarray
Probe design
Image processing
Pre-processing
Differentially expressed gene identification
Clustering / classification
Network / pathway modeling

3. Gene expression
Reverse transcription (in lab)
Product is called cDNA
Genes have different activities at different time / location
DNA Microarrays
Measure gene transcription (amount of mRNA) in a high-throughput fashion
A surrogate of gene activity

4. (an old technique for measuring mRNA expression)
Northern Blot
(an old technique for measuring mRNA expression)
1. mRNA extracted and purified.
4. mRNA are transferred from the gel to a membrane.
2. mRNA loaded for electrophoresis.
Lane 1: size standards.
Lane 2: RNA to be tested.
5. A labeled probe specific for the RNA fragment is incubated with the blot. So the RNA of interest can be detected. -
3. The gel is charged and RNA “swim” through gel according to weight.
Hybridization
Need relatively large amount of mRNA +

5. RT-PCR (reverse transcription-polymerase chain reaction)
RNA is reverse transcribed to DNA.
PCR procedures can be used amplify DNA at exponential rate.
Gel quantification for the amplified product.
---- an semi-quantitative method. Smaller amount of sample needed.
See animation of RT-PCR:
real-time RT-PCR
The PCR amplification can be monitored by fluorescence in “real time”.
The fluorescence values recorded in each cycle represent the amount of amplified product.
---- a quantitative method. The current most advanced and accurate analysis for mRNA abundance. Usually used to validate microarray result.
Often used to validate microarray

6. Limitation of the old techniques
Labor intensive
Can only detect up to dozens of genes.
(gene-by-gene analysis)

7. What is a Microarray Gene 102
Conceptually similar to (reverse) Northern blot
(Many) probes, rather than mRNAs, are fixed on some surface, in an ordered way
Gene 305

8. What is a microarray (2)
A 2D array of DNA sequences from thousands of genes
Each spot has many copies of same gene (probe)
Allow mRNAs from a sample to hybridize
Measure number of hybridizations per spot

9. Goals of a Microarray Experiment
Find the genes that change expression between experimental and control samples
Classify samples based on a gene expression profile
Find patterns: Groups of biologically related genes that change expression together across samples/treatments

10. Microarray categories
cDNAs microarray
Each probe is the cDNA of a gene (hundreds to thousands bp)
Stanford, Brown Lab
Oligonucleotide microarray
Each probe is a synthesized short DNA (uniquely corresponding to a substring of a gene)
Affymetrix: ~ 25mers
Aglient: ~ 60 mers
Others

11. Spotted cDNA microarray

12. Array Manufacturing
Each tube contains cDNAs corresponding to a unique gene. Pre-amplified, and spotted onto a glass slide

13. Experiment
cy3
cy5

14. Data acquisition
Computer programs are used to process the image into digital signals.
Segmentation: determine the boundary between signal and background
Results: gene expression ratios between two samples

15. cDNA Microarray Methodology Animation

16. Affymetrix GeneChip®

17. Array Design 25-mer unique oligo mismatch in the middle nuclieotide
multiple probes (11~16) for each gene
from Affymetrix Inc.

18. Array Manufacturing In situ synthesis of oligonucletides
Technology adapted from semiconductor industry.
(photolithography and combinatorial chemistry)
                                                            
In situ synthesis of oligonucletides
from Affymetrix Inc.

19. GeneChip® Probe Arrays
Hybridized Probe Cell * *
GeneChip Probe Array * * *
Single stranded,
labeled RNA target *
Oligonucleotide probe
24µm
Millions of copies of a specific
oligonucleotide probe
1.28cm
>200,000 different
complementary probes
Image of Hybridized Probe Array

20. Overview of the Affymetrix GeneChip technology
Each probe set combines to give an absolute expression level.
Image segmentation is relatively easy.
But how to use MM signal is debatable
from Affymetrix Inc.

21. Comparison of cDNA array and GeneChip cDNA GeneChip
Probe preparation
Probes are cDNA fragments, usually amplified by PCR and spotted by robot.
Probes are short oligos synthesized using a photolithographic approach.
colors
Two-color
(measures relative intensity)
One-color
(measures absolute intensity)
Gene representation
One probe per gene
11-16 probe pairs per gene
Probe length
Long, varying lengths
(hundreds to 1K bp)
25-mers
Density
Maximum of ~15000 probes.
38500 genes * 11 probes = probes

22. Why the difference? Affymetrix GeneChip One color design
cDNA microarray
Two color design
Why the difference?

23. Affymetrix GeneChip cDNA microarray Photolithography Robotic spotting
(The amount of oligos on a probe is well controlled)
cDNA microarray
Robotic spotting
(The amount of cDNA spotted on a probe may vary greatly)

24. Advantage and disadvantage of cDNA array and GeneChip
cDNA microarray
Affymetrix GeneChip
The data can be noisy and with variable quality
Specific and sensitive. Result very reproducible.
Cross(non-specific) hybridization can often happen.
Hybridization more specific.
May need a RNA amplification procedure.
Can use small amount of RNA.
More difficulty in image analysis.
Image analysis and intensity extraction is easier.
Need to search the database for gene annotation.
More widely used. Better quality of gene annotation.
Cheap. (both initial cost and per slide cost)
Expensive (~$400 per array+labeling and hybridization)
Can be custom made for special species.
Only several popular species are available
Do not need to know the exact DNA sequence.
Need the DNA sequence for probe selection.

25. Computational aspects
Probe design
Image processing
Pre-processing
Differentially expressed gene identification
Clustering / classification
Network / pathway modeling

26. First step: pre-processing
Transformation
Transforms intensities or ratios to a different scale
Why?
For convenience
Convert data into a certain distribution (e.g. normal) assumed by many other statistical procedures
Normalization
Correct for systematic errors
Make data from different samples comparable
Garbage in => Garbage out

27. Where errors could come from?
Random errors
Repeat the same experiment twice, get diff results
Using multiple replicates reduces the problem
Systematic errors
Arrays manufactured at different time
On the same array, probes printed with different printer tips may have different biases
Dye effect: difference between Cy5 and Cy3 labeling
Experimental factors
Array A being applied more mRNAs than array B
Sample preparation procedure
Experiments carried out at different time, by different users, etc.

28. cDNA microarray data preprocessing

29. Typical experiments Probes (genes) Wide-type cells vs mutated cells
Diseased cells with normal cells
Cells under normal growth condition vs cells treated with chemicals
Typically repeated for several times
Ratios
Probes (genes)

30. Transforming cDNA microarray data
Data: Cy5/Cy3 ratios as well as raw intensities
Most common is log2 transformation
2 fold increase => log2(2) = 1
2 fold decrease => log2(1/2) = -1

31. Dye effect cDNA microarray experiments using two identical samples.
Cy5 consistently lower than Cy3. Solution: dye swapping.

32. Dye swapping ½ log2 (cy5/cy3 on chip 1) + ½ log2 (cy3/cy5 on chip 2)
Chip 1: label test by cy5 and control by cy3
Chip 2: label test by cy3 and control by cy5
Ideally cy5/cy3 = cy3/cy5
Not so due to dye effect
Compute average ratio:
½ log2 (cy5/cy3 on chip 1)
+ ½ log2 (cy3/cy5 on chip 2)

33. Total intensity normalization
Even after dye-swapping, may still see systematic biases
Assume the total amount of mRNAs should not change between two samples
Not necessarily true
Rescale so that the two colors have same total intensity
Rescale according to a subset of genes
House-keeping genes
Middle 90% (for example) of genes
Spike-in genes

34. M-A plot Also know as ratio-intensity plot
M: log2(cy5 / cy3) = log2(cy5) – log2(cy3)
A: ½ log2(cy5 * cy3) = (log2(cy5) + log2(cy3)) / 2
Ideal:
M centered at zero
variance does not depend on A.
However:
Systematic dependence between M and A
High variance of M for smaller A M A

35. Lowess normalization Lowess: Locally Weighted Regression
Fit local polynomial functions
M adjusted according to fitted line M M’ A A

36. Replicate filtering Experiments repeated
Genes with very high variability is questionable
Ratio 1
Ratio 2
Log2(ratio2)
Log2(ratio1)

37. oligo microarray data preprocessing
(Affymetrix chip)

38. Typical experiments Multiple microarrays For example
n samples (from different time, location, condition, treatment, etc.)
k replicates for each samples
For example
Samples collected from 100 healthy people and 100 cancer patients
Cells treated with some drugs, take samples every 10 minutes
Repeat on 3 – 5 microarrays for each sample
Improve reliability of the results
Often averaged after some preprocessing

39. Main characteristics
For each gene, there are multiple PM and MM probes (11-16 pairs)
how to obtain overall intensities from these probe-level intensities?
Array outputs are absolute values rather than ratios
Cross-array normalization is important for them to be comparable

40. How to use MM information?
Earlier approach:
First remove outliner probes
Actual intensity = Ipm – Imm
IPM = IMM + Ispecific ?
Recent trend
Tend to ignore Imm or use in a different way
Various software packages
MAS5 (by affymetrix)
dChIP
RMA
GCRMA

41. Normalization Similar to cDNA microarrays
Total intensity normalization
Each array has the same mean intensity
Can be based on all genes or a selected subset of genes
House-keeping genes
Middle 90% (for example) of genes
Spike-in genes
Lowess with a common reference
Many useful tools implemented in Bioconductor

42. Conclusions
Microarray provides a way to measure thousands of genes simultaneously and make the global monitoring of cellular activities possible.
The method produces noisy data and normalization is crucial.
Real Time RT-PCR for validation of small number of genes.

43. Limitation
Measures mRNA instead of proteins. Actual protein abundance and post-translation modification can not be detected.
Suitable for global monitoring and should be used to generate further hypothesis or should combine with other carefully designed experiments.

44. Microarray preproc questions
What kind of array it is?
Two-color?
One-color?
Oligo array?
cDNA array?
How is the experiment designed?
Time series?
Test vs control?
What kind of preprocessing has been done?
What value: raw intensity value or ratios?
Transformation? Log scale? Linear scale?
Normalization: within-array? Cross-array?
What are the next steps you want to proceed?
Identifying differentially expressed genes?
Clustering?

45. Some real data
Joseph L. DeRisi, Vishwanath R. Iyer, Patrick O. Brown, “Exploring the Metabolic and Genetic Control of Gene Expression on a Genomic Scale”, Science, 278: 680 – 686, 1997