1. Introduction to Microarry and Related High Throughput Analysis BMI 705
Kun Huang
Department of Biomedical Informatics
Ohio State University

2. What is microarray?
Affymetrix-like arrays – single channel (background-green, foreground-red)
cDNA arrays – two channel (red, green, yellow)
CGH array, DNA methylation array, SNP array, etc.
CHIP-on-Chip
Tissue microarray
Future - Sequencing
The difference between the microarrays that are currently available must be understood before proceeding in this tutorial. Microarrays can be generalised into two categories:
cDNA (oligonucleotide) spotted arrays
High-density synthetic oligonucleotide (Affymetrix-like) arrays
Broadly speaking this catergorisation is based mainly on the way the arrays are manufactured and the way they are used.
cDNA arrays are mostly used in 2-dye (also known as 2-channel) experiments. This means that 2 samples of RNA are taken from 2 seperate sources and cDNA is created from each by using a technique called RT-PCR (Reverse Transcriptase Polymerase Chain Reaction). Each cDNA sample is labeled with its own specific dye. Both cDNA samples are then washed over a single array, after which dye intensities on each spot is calculated using a fluorescence camera. cDNA arrays are typically less dense than Affymetrix-like arrays, containing approximately 20,000 sequences, each of which is about 60 to 80 nucleotides long.
In contrast to the 2-dye approach, Affymetrix-like arrays can only be treated with a single sample (i.e. RNA from a single source). No RT-PCR is necessary. These arrays can potentially have a density of up to 100,000 sequences, each of which being no longer than 40 nucleotides.

3. How is microarray manufactured?

4. How does two-channel microarray work?
Printed microarrays
Long probe oligonucleotides (80-100) long are “printed” on the glass chip

5. How does two-channel microarray work?
Printing process introduces errors and larger variance
Comparative hybridization experiment

6. How does microarray work?

7. How is microarray manufactured? Affymetrix GeneChip
silicon chip
oligonucleiotide probes lithographically synthesized on the array
cRNA is used instead of cDNA

8. How does Affymetrix microarray work?

9. How does microarray work?

10. How does microarray work?

11. How does microarray work?

12. How does microarray work?

13. How does microarray work?
Fabrication expense and frequency of error increases with the length of probe, therefore 25 oligonucleotide probes are employed.
Problem: cross hybridization
Solution: introduce mismatched probe with one position (central) different with the matched probe. The difference gives a more accurate reading.

14. How do we use microarray?
Profiling
Clustering

15. Spatial Images of the Microarrays
Data for the same brain voxel but for the untreated control mouse
Background levels are much higher than those for the Parkinson’s disearse model mouse
There appears to be something non random affecting the background of the green channel of this slide

16. How do we take readings from microarray (measurement)?
cDNA array – ratio, log ratio
Affymetrix array

17. How do we process microarray data
(McShane, NCI)

18. How do we process microarray data
Normalization
Intensity imbalance between RNA samples
Affect all genes
Not due to biology of samples, but due to technical reasons
Reasons include difference in the settings of the photodetector voltage, imbalance in total amount of RNA in each sample, difference in uptaking of the dyes, etc.
The objective is to adjust the gene expression values of all genes so that the ones that are not really differentially expressed have similar values across the array(s).

19. Normalization Two major issues to consider Housekeeping genes
Which genes to use for normalization
Which normalization algorithm to use
Housekeeping genes
Genes involved in essential activities of cell maintenance and survival, but not in cell function and proliferation. These genes will be similarly expressed in all samples but they may be difficult to identify – need to be confirmed. Affymetrix GeneChip provides a set of house keeping genes (but still no guarantee).
Spiked controls
Genes that are not usually found in the samples (both control and test sample). E.g., yeast gene in human tissue samples. Note: Affy GeneChip protocol includes the spiking of control oligonucleotides into each sample. They are NOT for normalization. Instead, they are for other purposes such as gridding of slide by the image analysis software.
Using all genes
Simplest approach – use all adequately expressed genes for normalization The assumption is that the majority of genes on the array are housekeeping genes and the proportion of over expressed genes is similar to that of the under expressed genes. If the genes one the chip are specially selected, then this method will not work.

20. Ratio-intensity (RI) or MA plot
Normalization
Which normalization algorithm to use
For two-color cDNA arrays - Intra-slide normalization
Ratio-intensity (RI) or MA plot
Scatter plot
Slope = 1

21. Linear (global) normalization
Simplest but most consistent
Move the median to zero (slope 1 in scatter plot, this only changes the intersection)
No clear nonliearity or slope in MA plot

22. Intensity-based (Lowess) normalization
Overall magnitude of the spot intensity has an impact on the relative intensity between the channels.
“Straighten” the Lowess fit line in MA plot to horizontal line and move it to zero

23. Intensity-based (Lowess) normalization Nonlinear
Gene-by-gene, could introduce bias
Use only when there is a compelling reason
(McShane, NCI)

24. Other normalization method
Combination of location and intensity-based normalization
Location
Quantile …

25. Normalization
Which normalization algorithm to use
Inter-slide normalization
Not just for Affymetrix arrays

26. Normalization
Box plot
Upper quartile
Median
Low quartile

27. Normalization
Linear (global) – the chips have equal median (or mean) intensity
Intensity-based (Lowess) – the chips have equal medians (means) at all intensity values
Quantile – the chips have identical intensity distribution
Quantile is the “best” in term of normalizing the data to desired distribution, however it also changes the gene expression level individually
Avoid overfitting
Avoid bias

28. Gene Discovery and T-tests
Student’s t-test

29. Gene Discovery and Multiple T-tests
Controlling False Positives
Statistical tests to control the false positives
Controlling for no false positives (very stringent, e.g., Bonferroni test)
Controlling the number of false positives
Controlling the proportion of false positives
Note that in the screening stage, false positive is better than false negative as the later means missing of possibly important discovery.

30. Microarray Databases Gene Expression Ominbus (GEO) database – NCBI
EMBL-EBI microarray database
ArrayExpress
Stanford Microarray Database (SMD)
Other specialized, regional and aggregated databases …

31. Microarray Softwares DChip Open source R, Bioconductor
BRBArray tools (NCI biometric research branch)
Affymetrix
GeneSpring GX
GenePattern …

32. How do we use microarray (clustering)?

33. How do we process microarray data (clustering)?
Unsupervised Learning – Hierarchical Clustering

34. ChIP-on-chip, “also known as genome-wide location analysis, is a technique for isolation and identification of the DNA sequences occupied by specific DNA binding proteins in cells.” (
Identify protein binding sites on DNA
Study transcriptional factors – identify the genes that controlled by the specific TFs
Identify TFs
Identify regulatory regions such as promoters, enhancers, repressors, silencing elements, insulators, and boundary elements
Determine sequences controlling DNA replication (e.g., histone binding sites)

35. ChIP-on-Chip
ChIP – Chromatin immunoprecipitation
Chip – Microarray

36. ChIP-on-Chip – Example
Simon I., Barnett J., Hannett N., Harbison C.T., Rinaldi N.J., Volkert T.L., Wyrick J.J., Zeitlinger J., Gifford D.K., Jaakkola T.S., et al. "Serial regulation of transcriptional regulators in the yeast cell cycle", Cell, Volume: 106, (2001), pp
Figure 2. Genome-wide Location of the Nine Cell Cycle Transcription Factors(A) 213 of the 800 cell cycle genes whose promoter regions were bound by a myc-tagged version of at least one of the nine cell cycle transcription factors (p < 0.001) are represented as horizontal lines. The weight-averaged binding ratios are displayed using a blue and white color scheme (genes with p value < are displayed in blue). The expression ratios of an α factor synchronization time course from Spellman et al. (1998) are displayed using a red (induced) and green (repressed) color scheme.(B) The circle represents a smoothed distribution of the transcription timing (phase) of the 800 cell cycle genes (Spellman et al., 1998). The intensity of the red color, normalized by the maximum intensity value for each factor, represents the fraction of genes expressed at that point that are bound by a specific activator. The similarity in the distribution of color for specific factors (with Swi4, Swi6, and Mbp1, for example) shows that these factors bind to genes that are expressed during the same time frame

37. ChIP-on-Chip – Example
Simon I., et al. "Serial regulation of transcriptional regulators in the yeast cell cycle", Cell, Volume: 106, (2001), pp

38. ChIP-on-Chip
Problem : Probe design
Most TF binding sites are not in exon
Binding sequences are short
Cover entire genome?
Signal may be small
Tiling array – divide the sequence into chunks, called tiling path. The distance between the center of neighboring chunks is called resolution. A path can be overlapped or spaced.
Affymetrix tiling array for yeasts – 5bp resolution, 3.2 million probes
Affymetrix tiling array for human – 35bp spacing, 90 million probes

39. Sequencing Solexa http://www.illumina.com/pages.ilmn?ID=203 SOLiD
Mikkelsen et al