1. Introduction to microarray technology and analysis
Carol Bult
Associate Professor
The Jackson Laboratory 1

2. Measuring Gene Expression
Idea: measure the amount of mRNA to see which genes are being expressed in (used by) the cell. Measuring protein might be more direct, but is currently harder.

3. Central Assumption of Gene Expression Microarrays
The level of a given mRNA is positively correlated with the expression of the associated protein.
Higher mRNA levels mean higher protein expression, lower mRNA means lower protein expression
Other factors:
Protein degradation, mRNA degradation, polyadenylation, codon preference, translation rates, alternative splicing, translation lag…

4. Principal Uses of Microarrays
Genome-scale gene expression analysis
Differential gene expression between two (or more) sample types
Responses to environmental factors
Disease processes (e.g. cancer)
Effects of drugs
Identification of genes associated with clinical outcomes (e.g. survival)

5. Microarray example: Biomarker identification - lung cancer
Samples
Genes
Gene expression patterns segregate the four major morphological lung tumor subtypes
Patterns of gene expression promise to refine traditional morphologic classification of lung cancer.
Can we distinguish subsets of genes that would allow molecular-level classification of tumor subtypes?
Garber, Troyanskaya et al. Diversity of gene expression in adenocarcinoma of the lung. PNAS 2001, 98(24): 5

6. Data partitioning clinically important: Patient survival for lung cancer subgroups60
Cum. Survival
Time (months) .2 .4 .6 .8 1 10 20 30 40 50
Cum. Survival (Group 3)
Cum. Survival (Group 2)
Cum. Survival (Group 1)
p = 0.002
for Gr. 1 vs. Gr. 3
Can we identify individual genes that can predict patient survival for adenocarcinoma lung cancer?
Garber, Troyanskaya et al. Diversity of gene expression in adenocarcinoma of the lung. PNAS 2001, 98(24): 6

7. Differentially expressed genes Sample class prediction etc.
Biological question
Differentially expressed genes
Sample class prediction etc.
Experimental design
Microarray experiment
Image analysis
Normalization
Estimation
Testing
Clustering
Discrimination
Biological verification
and interpretation

8. Technology basics
Microarrays are composed of short, specific DNA sequences attached to a glass or silicon slide at high density
A microarray works by exploiting the ability of an mRNA molecule to bind specifically to, or hybridize, the DNA template from which it originated
RNA or DNA from the sample of interest is fluorescently-labeled so that relative or absolute abundances can be quantitatively measured
(Chris Workman)

9. Two color vs single color
Bakel and Holstege

10. Other applications of microarray technology (besides measuring gene expression)
DNA copy number analysis
SNP analysis
chIP-chip (interaction data)
Competitive growth assays …

11. Major technologies
cDNA probes (> 200 nt), usually produced by PCR, attached to either nylon or glass supports
Oligonucleotides (25-80 nt) attached to glass support
Oligonucleotides (25-30 nt) synthesized in situ on silica wafers (Affymetrix)
Probes attached to tagged beads

12. cDNA Microarray Design
Probe selection
Non-redundant set of probes
Includes genes of interest to project
Corresponds to physically available clones
Chip layout
Grouping of probes by function
Correspondence between wells in microtiter plates and spots on the chip

13. Building the chip
Ngai Lab arrayer , UC Berkeley
Print-tip head

15. Example dual channel cDNA array results
(Chris Workman)

16. Affymetrix GeneChips
Probes are oligos synthesized in situ using a photolithographic approach
There are at least 5 oligos per cDNA, plus an equal number of negative controls
The apparatus requires a fluidics station for hybridization and a special scanner
Only a single fluorochrome is used per hybridization

18. Affy There may be 5,000-100,000 probe sets per chip
A probe set = PM, MM pairs

20. Interpreting Affymetrix Output Perfect Match/Mismatch Strategy
Each probe designed to be perfectly complementary to a target sequence, a partner probe is generated that is identical except for a single base mismatch in its center.
These probe pairs, called the Perfect Match probe (PM) and the Mismatch probe (MM), allow the quantitation and subtraction of signals caused by non-specific cross-hybridization.
The difference in hybridization signals between the partners serve as indicators of specific target abundance
Moustafa Ghanem 20

21. Differentially expressed genes Sample class prediction etc.
Biological question
Differentially expressed genes
Sample class prediction etc.
Experimental design
Microarray experiment
Image analysis
Normalization
Estimation
Testing
Clustering
Discrimination
Biological verification
and interpretation

22. Experimental Design
Bakel and Holstege

23. - Donald Rumsfeld, former Secretary of Defense
Microarray Analysis: Controlling for the Known Knowns and Unknown Unknowns
- Donald Rumsfeld, former Secretary of Defense

29. Selected references
Best advice?
Consult a statistician before you start!

30. Statistical Power
The probability that a test will reject a null hypothesis if it is false
Type I and Type II errors
Type 1 – fail to accept the null hypothesis
We say there is a difference in gene expression between gene A and gene B when there really isn’t
Type 2- fail to reject the null hypothesis
We say there is no difference in gene expression between gene A and gene B when there actually is!

31. Power in Perspective Sample size Effect size Alpha level Power
Number of units
Effect size
Signal to noise
Alpha level
Significance level
Power
Likelihood of detecting a treatment effect if it is there
What are the 4 main components that determine what conclusions are drawn from a study?

32. Check out this pithy description of Statistical Power and Hypothesis Testing

33. MicroArray Image Analysis
Based on slides from Robin Liechti

34. Microarray analysis Array construction, hybridisation, scanning
Quantitation of fluorescence signals
Data visualisation
Meta-analysis (clustering)
More visualisation

35. Technical pseudo-colour image sample (labelled) probe (on chip)
[image from Jeremy Buhler]

36. Experimental design Track what’s on the chip
which spot corresponds to which gene
Duplicate experimental spots
reproducibility
Controls
DNAs spotted on glass
positive probe (induced or repressed)
negative probe (bacterial genes on human chip)
oligos on glass or synthesised on chip (Affymetrix)
point mutants (hybridisation plus/minus)

37. Images from scanner Resolution
standard 10m [currently, max 5m]
100m spot on chip = 10 pixels in diameter
Image format
TIFF (tagged image file format) 16 bit (65’536 levels of grey)
1cm x 1cm image at 16 bit = 2Mb (uncompressed)
other formats exist e.g.. SCN (used at Stanford University)
Separate image for each fluorescent sample
channel 1, channel 2, etc.

38. Images in analysis software
The two 16-bit images (cy3, cy5) are compressed into 8-bit images
Goal : display fluorescence intensities for both wavelengths using a 24-bit RGB overlay image
RGB image :
Blue values (B) are set to 0
Red values (R) are used for cy5 intensities
Green values (G) are used for cy3 intensities
Qualitative representation of results

39. Images : examples Pseudo-color overlay cy3 cy5 Spot color
Signal strength
Gene expression
yellow
Control = perturbed
unchanged
red
Control < perturbed
induced
green
Control > perturbed
repressed

40. Processing of images Addressing or gridding Segmentation
Assigning coordinates to each of the spots
Segmentation
Classification of pixels either as foreground or as background
Intensity extraction (for each spot)
Foreground fluorescence intensity pairs (R, G)
Background intensities
Quality measures

42. File or archive your e-mail on your own computer

43. Addressing (I)
ScanAlyze
Parameters to address the spots positions
Separation between rows and columns of grids
Individual translation of grids
Separation between rows and columns of spots within each grid
Small individual translation of spots
Overall position of the array in the image
The basic structure of the images is known (determined by the arrayer)

44. Addressing (II)
The measurement process depends on the addressing procedure
Addressing efficiency can be enhanced by allowing user intervention (slow!)
Most software systems now provide for both manual and automatic gridding procedures

45. Segmentation (I)
Classification of pixels as foreground or background -> fluorescence intensities are calculated for each spot as measure of transcript abundance
Production of a spot mask : set of foreground pixels for each spot

46. Segmentation (II) Segmentation methods : Fixed circle segmentation
Adaptive circle segmentation
Adaptive shape segmentation
Histogram segmentation
Fixed circle
ScanAlyze, GenePix, QuantArray
Adaptive circle
GenePix, Dapple
Adaptive shape
Spot, region growing and watershed
Histogram method
ImaGene, QuantArraym DeArray and adaptive thresholding

47. Fixed circle segmentation
Fits a circle with a constant diameter to all spots in the image
Easy to implement
The spots need to be of the same shape and size
Bad example !

48. Adaptive circle segmentation
Dapple finds spots by detecting edges of spots (second derivative)
The circle diameter is estimated separately for each spot
Problematic if spot exhibits oval shapes

49. Adaptive shape segmentation
Specification of starting points or seeds
Regions grow outwards from the seed points preferentially according to the difference between a pixel’s value and the running mean of values in an adjoining region.

50. Histogram segmentation
Uses a target mask chosen to be larger than any other spot
Foreground and background intensity are determined from the histogram of pixel values for pixels within the masked area
Example : QuantArray
Background : mean between 5th and 20th percentile
Foreground : mean between 80th and 95th percentile
Unstable when a large target mask is set to compensate for variation in spot size
Bkgd
Foreground

51. Information extraction

52. Spot intensity
The total amount of hybridization for a spot is proportional to the total fluorescence at the spot
Spot intensity = sum of pixel intensities within the spot mask
Since later calculations are based on ratios between cy5 and cy3, we compute the average* pixel value over the spot mask
*alternative : use ratios of medians instead of means

53. Background intensity
Motivation : spot’s measured intensity includes a contribution of non-specific hybridization and other chemicals on the glass
Fluorescence from regions not occupied by DNA should by different from regions occupied by DNA -> could be interesting to use local negative controls (spotted DNA that should not hybridize)
Different background methods : Local background, morphological opening, constant background, no adjustment

54. Local background Focusing on small regions surrounding the spot mask.
Median of pixel values in this region
Most software package implement such an approach
ScanAlyze
ImaGene
Spot, GenePix
By not considering the pixels immediately surrounding the spots, the background estimate is less sensitive to the performance of the segmentation procedure

55. Morphological opening (spot)
Applied to the original images R and G
Use a square structuring element with side length at least twice as large as the spot separation distance
Remove all the spots and generate an image that is an estimate of the background for the entire slide
For individual spots, the background is estimated by sampling this background image at the nominal center of the spot
Lower background estimate and less variable

56. Constant background
Global method which subtracts a constant background for all spots
Some findings suggests that the binding of fluorescent dyes to ‘negative control spots’ is lower than the binding to the glass slide
-> More meaningful to estimate background based on a set of negative control spots
If no negative control spots : approximation of the average background = third percentile of all the spot foreground values

57. No adjustment
Do not consider the background

58. Quality measures (-> Flag)
How good are foreground and background measurements ?
Variability measures in pixel values within each spot mask
Spot size
Circularity measure
Relative signal to background intensity
b-value : fraction of background intensities less than the median foreground intensity
p-score : extend to which the position of a spot deviates from a rigid rectangular grid
Based on these measurements, one can flag a spot

59. Summary
Spot, GenePix
ScanAlyze
M = log2 R/G
A = log2 √(R•G)
The choice of background correction method has a larger impact on the log-intensity ratios than the segmentation method used
The morphological opening method provides a better estimate of background than other methods
Low within- and between-slide variability of the log2 R/G
Background adjustment has a larger impact on low intensity spots

60. Selected references
Yang, Y. H., Buckley, M. J., Dudoit, S. and Speed, T. P. (2001), ‘Comparisons of methods for image analysis on cDNA microarray data’. Technical report #584, Department of Statistics, University of California, Berkeley.
Yang, Y. H., Buckley, M. J. and Speed, T. P. (2001), ‘Analysis of cDNA microarray images’. Briefings in bioinformatics, 2 (4), Excellent review in concise format!

61. Download the limma package and work through the Swirl zebrafish example.

62. Differentially expressed genes Sample class prediction etc.
Biological question
Differentially expressed genes
Sample class prediction etc.
Experimental design
Microarray experiment
Image analysis
Normalization
Estimation
Testing
Clustering
Discrimination
Biological verification
and interpretation

63. 63

64. Normalization - two problems
How do we detect biases? Which genes should we use for estimating biases among chips/channels?
How do we remove the biases?

65. Why normalize?
Microarray data have significant systematic variation both within arrays and between arrays that is not true biological variation
Accurate comparison of genes’ relative expression within and across conditions requires normalization of effects
Sources of variation:
Spatial location on the array
Dye biases which vary with spot intensity
Plate origin
Printing/spotting quality
Experimenter

66. Why is normalization important?
Experiment:
Comparison of gene expression response in mouse heart and kidney in response to drug
Most biological effects are swamped by systematic effects!
Source:

67. Other Sources of Systematic Bias
Individual Factors
Print (20% - 30%)
Experimenter (20% - 30%)
Organism (3% - 10%)
Date (5%)
Software (2%)
Number of tips (3%)
Interactions
Print - Experimenter (40%)
Print - Date (40%)
Experimenter - Date (40%)
(based on ~4,600 experiments in Stanford Microarray Database analyzed by ANOVA)
(slide from Catherine Ball)

68. Clearly visible plate effects
KO #8
Probes: ~6,000 cDNAs, including 200 related to lipid metabolism.
Arranged in a 4x4 array of 19x21 sub-arrays.

69. Spatial Biases Solution: spatial background estimation/subtraction
(Gavin Sherlock)
Solution: spatial background estimation/subtraction

70. Spatial plots: background from two slides

71. Highlighting extreme log ratios
Top (black) and bottom (green) 5% of log ratios

72. Pin group (sub-array) effects
Lowess lines through points from pin groups
Boxplots of log ratios by pin group

73. Boxplots and highlighting pin group effects
Log-ratios
Print-tip groups
Clear example of spatial bias

74. Time of printing effects
spot number
Green channel intensities (log2G). Printing over 4.5 days.
The previous slide depicts a slide from this print run.

75. Normalization in a nutshell
Goal is to measure the ratios of gene expression levels, (ratio)i = Ri/Gi
Where Ri/Gi are, respectively, the measured intensities for the ith spot
In a self hybridzation, we would expect all ratios to be equal to one:
Ri/Gi = 1 for all i. But they probably won’t be…
Why?
noise (systematics bias)
signal (true differences)
Normalization brings appropriate ratios closer to 1

76. 76

77. The Starting Point: The Ratio (2-color arrays)
(Gavin Sherlock)

78. Log ratios treat up- and down-regulated genes equally
(Gavin Sherlock)
(two-color arrays)
log2(1) = 0 log2(2) = 1 log2(1/2) = -1

79. A note about Affymetrix (1-color) pre-processing
Typical Affymetrix probe intensity distribution
Log transform
After log-transform

80. Normalization methods

81. Which Genes to use for bias detection?
All genes on the chip
Assumption: Most of the genes are equally expressed in the compared samples, the proportion of the differential genes is low (<20%).
Limits:
Not appropriate when comparing highly heterogeneous samples (different tissues)
Not appropriate for analysis of ‘dedicated chips’ (apoptosis chips, inflammation chips etc)

82. Which Genes to use for bias detection?
Housekeeping genes
Assumption: based on prior knowledge a set of genes can be regarded as equally expressed in the compared samples
Affy novel chips: ‘normalization set’ of 100 genes
NHGRI’s cDNA microarrays: 70 "house-keeping" genes set
Limits:
The validity of the assumption is questionable
Housekeeping genes are usually expressed at high levels, not informative for the low intensities range

83. Which Genes to use for bias detection?
Spiked-in controls from other organism, over a range of concentrations
Limits:
low number of controls- less robust
Can’t detect biases due to differences in RNA extraction protocols
“Invariant set”
Trying to identify genes that are expressed at similar levels in the compared samples without relying on any prior knowledge:
Rank the genes in each chip according to their expression level
Find genes with small change in ranks

84. 1. Global normalization (Scaling)
A single normalization factor (k) is computed for balancing chips\channels:
Xinorm = k*Xi or
log2 R/G  log2 R/G – c (2-color)
Multiplying intensities by this factor equalizes the mean (median) intensity among compared chips
Assumption: Total RNA (mass) used is same for both samples.
So, averaged across thousands of genes, total hybridization should be the same for both samples.

85. Global Normalization (1-color, e.g. Affymetrix)
Before
After
Xinorm = k*Xi

86. Global Normalization (2-color)
Un-normalized
Normalized
Frequency
(Gavin Sherlock)
Log-ratios
log2 R/G  log2 R/G – c
where c = log2 (∑Ri/ ∑Gi)

87. 2. Intensity-dependent normalization (Yang, Speed)
(Lowess – local linear fit)
Compensate for intensity-dependent biases

88. Detect Intensity-dependent Biases: M vs A plots (also called R-I plot)
X axis: A – average intensity
A = 0.5*log(Cy3*Cy5)
Y axis: M – log ratio
M = log(Cy3/Cy5)

89. Intensity-dependent bias
High intensities
M>0: Cy3>Cy5
M = log(Cy3/Cy5)
Low intensities
M<0: Cy3<Cy5
* Global normalization cannot remove intensity-dependent biases A

90. We expect the M vs A plot to look like:
M = log(Cy3/Cy5) A

91. LOWESS (Locally Weighted Scatterplot Smoothing)
Local linear regression model
Tri-cube weight function
Least Squares
Estimated values of log2(Cy5/Cy3) as function of log10(Cy3*Cy5)

92. A note about Affymetrix (1-color) pre-processing
within-chip
cross-chip
sequence specific
background correction
within-probe set
aggregation of intensity values
(Johannes Freudenberg)
Two “standard” methods
MAS 5.0 (now GCOS/GDAS) by Affymetrix (compares PM and MM probes)
RMA by Speed group (UC Berkeley) (ignores MM probes)

93. Normalization – Thoughts
There are many different ways to normalize data
Global median, LOWESS, LOESS, etc
By print tip, spatial, etc
Choose one wisely
BUT: don’t expect it to fix bad data!
Won’t make up for lack of replicates
Won’t make up for horrible slides

94. For next time.. Read Quackenbush paper on normalization
Look up the paper on Robust Multichip Averaging (RMA) out of Terry Speed’s lab
What is meant by least squares?
Visit the Gene Expression Omnibus (GEO) resource at NCBI and explore what is there
If you aren’t familiar with the statistical computing environment, R, look it up on the web
Look up MeV (multi-experiment viewer) on the web.

95. File or archive your e-mail on your own computer

96. Differentially expressed genes Sample class prediction etc.
Biological question
Differentially expressed genes
Sample class prediction etc.
Experimental design
Microarray experiment
Image analysis
Normalization
Estimation
Testing
Clustering
Discrimination
Biological verification
and interpretation

97. Analysis

98. Microarray experiment
Microarray Data Flow
Microarray experiment
Unsupervised
Analysis –
clustering
Image Analysis
Database
Data Selection & Missing value estimation
Supervised
Analysis
Normalization & Centering
Networks & Data Integration
Data Matrix
Decomposition techniques 98

99. Differentially expressed genes Sample class prediction etc.
Biological question
Differentially expressed genes
Sample class prediction etc.
Experimental design
Microarray experiment
Image analysis
Normalization
Estimation
Testing
Clustering
Discrimination
Biological verification
and interpretation

100. Interpretation

101. Microarray data on the Web
Several initiatives to create “unified” databases
EBI: ArrayExpress
NCBI: Gene Expression Omnibus

102. Normalization - tools
Normalization is typically provided in microarray vendor’s software/core facilities but you should always understand the data you’re working with
How has your data been processed?
Are there any lingering effects?
Bioconductor (both Affymetrix and cDNA):
Packages in R language
dChip (Affymetrix):
Quantile, Invariant set
MAANOVA
Microarray ANOVA analysis