1. 4. Functional Genomics and Microarray Analysis (1)

2. Background Functional Genomics
Systematic analysis of gene activity in healthy and diseased tissues.
Obtaining an overall picture of genome functions, including the expression profiles at the mRNA level and the protein level.
Functional Genome Analysis:
used to understand the functions of genes and proteins in an organism. This is typically known as genome annotation.
used in integrative biology and systems biology studies aiming to understand health and disease states (e.g. cancer, obesity, …etc)
Used as an important step in the search for new target molecules in the drug discovery process. (which genes, proteins to target and how)

3. What is…? Gene Expression: Microarrays:
determines
DNA sequence
(split into genes)
Amino Acid
Sequence
Protein 3D
Structure
Function
Cell
Activity
codes for
folds into
dictates
has
What is…?
Gene Expression:
The process by which the information encoded in a gene is converted into an observable phenotype (most commonly production of a protein).
The degree to which a gene is active in a certain tissue of the body, measured by the amount of mRNA in the tissue.
Microarrays:
Tools used to measure the presence and abundance of gene expression (measure as mRNA) in tissue.
microarray technologies provide a powerful tool by which the expression patterns of thousands of genes can be monitored simultaneously and measured quantitatively

4. Applications of Microarray Technology
Applications covered only as example contexts, emphasis is on analysis methods
Identify Genes expressed in different cell types (e.g. Liver vs Kidney)
Learn how expression levels change in different developmental stages (embryo vs. adult)
Learn how expression levels change in disease development (cancerous vs non-cancerous)
Learn how groups of genes inter-relate (gene-gene interactions)
Identify cellular processes that genes participate in (structure, repair, metabolism, replication, … etc)

5. Microarrays Basic Idea
Affymetrix Inc. is the leading provider of Microarray technology (GeneChip® )
Microarrays Basic Idea
A Microarray is a device that detects the presence and abundance of labelled nucleic acids in a biological sample.
In the majority of experiments, the labelled nucleic acids are derived from the mRNA of a sample or tissue.
The Microarray consists of a solid surface onto which known DNA molecules have been chemically bonded at special locations.
Each array location is typically known as a probe and contains many replicates of the same molecule.
The molecules in each array location are carefully chosen so as to hybridise only with mRNA molecules corresponding to a single gene.

6. Several companies sell equipment to make DNA chips, including spotters to deposit the DNA on the surface and scanners to detect the fluorescent or radioactive signals.
Basic Idea
A Microarray works by exploiting the ability of a given mRNA molecule to bind specifically to, or hybridize to, the DNA template from which it originated.
By using an array containing many DNA samples, scientists can determine, in a single experiment, the expression levels of hundreds or thousands of genes within a cell by measuring the amount of mRNA bound to each site on the array.
With the aid of a computer, the amount of mRNA bound to the spots on the Microarray is precisely measured, generating a profile of gene expression in the cell.

7. Microarray Process
The molecules in the target biological sample are labelled using a fluorescent dye before sample is applied to array
If a gene is expressed in the sample, the corresponding mRNA hybridises with the molecules on a given probe (array location).
If a gene is not expressed, no hybridisation occurs on the corresponding probe.
Reading the array output
After the sample is applied, a laser light source is applied to the array.
The fluorescent label enables the detection of which probes have hybridised (presence) via the light emitted from the probe.
If gene is highly expressed, more mRNA exists and thus more mRNA hybridises to the probe molecules (abundance) via the intensity of the light emitted.

8. The array Chemistry Basics:
Surface Chemistry is used to attach the probe molecules to the glass substrate.
Chemical reactions are used to attach the florescent dyes to the target molecules
Probe and Target hybridise to form a double helix

9. Affymetrix GeneChip Example of Single Label Chips
Hundreds of thousands of oligonucleotide probes packed at extremely high densities. The probes designed to maximize sensitivity, specificity, and reproducibility, allowing consistent discrimination between specific and background signals, and between closely related target sequences.
RNA labeled and scanned in a single “color” one sample per chip

10. From Microarray images to Gene Expression Matrices
Samples
Genes
Gene expression levels
Final data
Gene Expression Matrix
Raw data
Array scans
Images
Spots
Spot/Image quantiations
Intermediate data

11. Steps of a Microarray Experiment
Biological question
Sample Attributes
Experimental design
Platform Choice
Microarray experiment
16-bit TIFF Files
Image analysis
Quantify the Dots
Normalization
Clustering
Statistical Analysis
Data Mining
Pattern Discovery
Classification
Biological verification
and interpretation

12. Qualitative Interpretation of Reads
GREEN represents High Control hybridization
  RED represents High Sample hybridization
  YELLOW represents a combination of Control and Sample where both hybridized equally.   BLACK represents areas where neither the Control nor Sample hybridized.
Main issue is to quantify the results:
How green is green?
What is the ratio of the signal to background noise?
How to compare multiple experiments using different chips?
How to quantify cross hybridization (if any)?

13. Normalization
Normalisation is a general term for a collection of methods that are directed at reasoning about and resolving the systematic errors and bias introduced by microarray experimental platforms
Normalisation methods stand in contrast with the data analysis methods described in other lectures (e.g. differential gene expression analysis, classification and clustering).
Our overall aim is to be able to quantify measured/calculated variability, differentials and similarity:
Are they biologically significant or just side effects of the experimental platforms and conditions?

14. Why Normalization Sources of Microarray Data Variability
The measured gene expression in any experiment includes true gene expression,together with contributions from many sources of variability
Why Normalization Sources of Microarray Data Variability
There are several levels of variability in measured gene expression of a feature.
At the highest level, there is biological variability in the population from which the sample derives.
At an experimental level, there is
variability between preparations and labelling of the sample,
variability between hybridisations of the same sample to different arrays, and
variability between the signal on replicate features on the same array.
Variability between Individuals
True gene expression of individual
Variability between sample preparations
Variability between arrays and hybridisations
Variability between replicate features
Measured gene expression

15. Normalisation Examples Probe Intensity Value
Typical Problem: Usually more variability at low intensity
Normalisation Examples Probe Intensity Value
The raw intensities of signal from each spot on the array are not directly comparable. Depending on the types of experiments done, a number of different approaches to normalization may be needed. Not all types of normalization are appropriate in all experiments. Some experiments may use more than one type of normalization.
Reasonable Assumption: intensities of fluorescent molecules reflect the abundance of the mRNA molecules – generally true but could be problematic
Example:
intensity of gene A spot is 100 units in normal-tissue array
intensity of gene A spot is 50 units in cancer-tissue array
Conclusion: gene A’s expression level in normal issue is significantly higher than in cancer tissue

16. Normalisation Examples Probe Intensity Value
Images showing examples of how background intensity can be calculated
Problem? What if the overall background intensity of the normal-tissue array is 95 units while the background intensity of cancer-tissue array is 10 units?
Solutions:
Subtract background intensity value
Take ratio of spot intensity to background intensity (preferable)
In both cases have to decide where to measure background intensity (e.g. local to spot or globally per chip)
In general, There could be many factors contributing to the background intensity of a microarray chip
To compare microarray data across different chips, data (intensity levels) need to be normalized to the “same” level

17. Differential Gene Expression Analysis
Consider a microarray experiment
that measures gene expression in two groups of rat tissue (>5000 genes in each experiment).
The rat tissues come from two groups:
WT: Wild-Type rat tissue,
KO: Knock Out Treatment rat tissue
Gene expression for each group measured under similar conditions
Question: Which genes are affected by the treatment? How significant is the effect? How big is the effect?

18. Calculating Expression Ratios
In Differential Gene Expression Analysis, we are interested in identifying genes with different expression across two states, e.g.:
Tumour cell lines vs. Normal cell lines
Treated tissue vs. diseased tissue
Different tissues, same organism
Same tissue, different organisms
Same tissue, same organism
Time course experiments
We can quantify the difference (effect) by taking a ratio
i.e. for gene k, this is the ratio between expression in state a compared to expression in state b
This provides a relative value of change (e.g. expression has doubled)
If expression level has not changed ratio is 1

19. Fold change (Fold ratio)
A gene is up-regulated in state 2 compared to state 1 if it has a higher value in state 2
A gene is down-regulated in state 2 compared to state 1 if it has a lower value in state 2
Fold change (Fold ratio)
Ratios are troublesome since
Up-regulated & Down-regulated genes treated differently
Genes up-regulated by a factor of 2 have a ratio of 2
Genes down-regulated by same factor (2) have a ratio of 0.5
As a result
down regulated genes are compressed between 1 and 0
up-regulated genes expand between 1 and infinity
Using a logarithmic transform to the base 2 rectifies problem, this is typically known as the fold change

20. Examples of fold change
A, B and D are down regulated
C is up-regulated
E has no change
Examples of fold change
Gene ID
Expression in state 1
Expression in state 2
Ratio
Fold Change A
100 50 2 1 B 10 5 C
0.5 -1 D
200
7.65 E
You can calculate Fold change between pairs of expression values:
e.g. Between State 1 vs State 2 for gene A
Or Between mean values of all measurements for a gene in the WT/KO experiments
mean(WT1..WT4) vs mean (KO1..KO4)

21. Statistics Significance of Fold Change
For our problem we can calculate an average fold ratio for each gene (each row)
This will give us an average effect value for each gene
2, 1.7, 10, 100, etc
Question which of these values are significant?
Can use a threshold, but what threshold value should we set?
Use statistical techniques based on number of members in each group, type of measurements, etc -> significance testing.

22. Statistics Unpaired statistical experiments
Condition
Group 1 members
Group 2 members
Statistics Unpaired statistical experiments
Overall setting: 2 groups of 4 individuals each
Group1: Imperial students
Group2: UCL students
Experiment 1:
We measure the height of all students
We want to establish if members of one group are consistently (or on average) taller than members of the other, and if the measured difference is significant
Experiment 2:
We measure the weight of all students
We want to establish if members of one group are consistently (or on average) heavier than the other, and if the measured difference is significant
Experiment 3:
………

23. Statistics Unpaired statistical experiments
Condition
Group 1 members
Group 2 members
Statistics Unpaired statistical experiments
In unpaired experiments, you typically have two groups of people that are not related to one another, and measure some property for each member of each group
e.g. you want to test whether a new drug is effective or not, you divide similar patients in two groups:
One groups takes the drug
Another groups takes a placebo
You measure (quantify) effect of both groups some time later
You want to establish whether there is a significant difference between both groups at that later point
The WT/KO example is an unpaired experiment if the rats in the experiments are different !

24. Statistics Unpaired statistical experiments
The WT/KO example is an unpaired experiment if the rats in the experiments are different!
Experiment for WT Rats for Gene 96608_at
Rat #
WT gene expression
WT1
100
WT2
WT3
200
WT4
300
Experiment for KO Rats for Gene 96608_at
Rat #
KO gene expression
KO1
150
KO2
300
KO3
100
KO4

25. Statistics Unpaired statistical experiments
How do we address the problem?
Compare two sets of results (alternatively calculate mean for each group and compare means)
Graphically:
Scatter Plots
Box plots, etc
Compare Statistically
Use unpaired t-test
Are these two series significantly different?
Are these two series significantly different?

26. Statistics Paired statistical experiments
Group members
Condition 1
Condition 2
Statistics Paired statistical experiments
In paired experiments, you typically have one group of people, you typically measure some property for each member before and after a particular event (so measurement come in pairs of before and after)
e.g. you want to test the effectiveness of a new cream for tanning
You measure the tan in each individual before the cream is applied
You measure the tan in each individual after the cream is applied
You want to establish whether the there is a significant difference between measurements before and after applying the cream for the group as a whole

27. Statistics Paired statistical experiments
The WT/KO example is a paired experiment if the rats in the experiments are the same!
Experiments for Gene 96608_at
Rat #
WT gene expression
KO gene expression
Rat1
100
200
Rat2
300
Rat3
400
Rat4
500

28. Statistics Paired statistical experiments
How do we address the problem?
Calculate difference for each pair
Compare differences to zero
Alternatively (compare average difference to zero)
Graphically:
Scatter Plot of difference
Box plots, etc
Statistically
Use unpaired t-test
Are differences close to Zero?

29. Statistics Significance testing
In both cases (paired and unpaired) you want to establish whether the difference is significant
Significance testing is a statistical term and refers to estimating (numerically) the probability of a measurement occurring by chance.
To do this, you need to review some basic statistics
Normal distributions: mean, standard deviations, etc
Hypothesis Testing
t-distributions
t-tests and p-values

30. Mean and standard deviation
68% of dist.
1 s.d. x
Mean and standard deviation
Mean and standard deviation tell you the basic features of a distribution
mean = average value of all members of the group
u = (x1+x2+x3 ….+xN)/N
standard deviation = a measure of how much the values of individual members vary in relation to the mean
The normal distribution is symmetrical about the mean 68% of the normal distribution lies within 1 s.d. of the mean

31. Note on s.d. calculation
Through the following slides and in the tutorials, I use the following formula for calculating standard deviation
Some people use the unbiased form below (for good reasons)
Please use the simple form if you want the answers to add up at the end

32. The Normal Distribution
Many continuous variables follow a normal distribution, and it plays a special role in the statistical tests we are interested in;
68% of dist.
1 s.d. x
The x-axis represents the values of a particular variable
The y-axis represents the proportion of members of the population that have each value of the variable
The area under the curve represents probability – i.e. area under the curve between two values on the x-axis represents the probability of an individual having a value in that range

33. Hypothesis Testing: Are two data sets different
We use z-test (normal distribution) if the standard deviations of two populations from which the data sets came are known (and are the same)
We pose a null hypothesis that the means are equal
We try to refute the hypothesis using the curves to calculate the probability that the null hypothesis is true (both means are equal)
if probability is low (low p) reject the null hypothesis and accept the alternative hypothesis (both means are different)
If probability is high (high p) accept null hypothesis (both means are equal) Ho
Population 1
Population 2 Ha
Population 1
Population 2
If standard deviation known use z test, else use t-test

34. Comparing Two Samples Graphical interpretation
To compare two groups you can compare the mean of one group graphically.
The graphical comparison allows you to visually see the distribution of the two groups.
If the p-value is low, chances are there will be little overlap between the two distributions. If the p-value is not low, there will be a fair amount of overlap between the two groups.
We can set a critical value for the x-axis based on the threshold of p-value

35. t-test terminology
t-test: Used to compare the mean of a sample to a known number
Assumptions: Subjects are randomly drawn from a population and the distribution of the mean being tested is normal.
Test: The hypotheses for a single sample t-test are:
Ho: u = u0
Ha: u < > u0
p-value: probability of error in rejecting the hypothesis of no difference between the two groups.
(where u0 denotes the hypothesized value to which you are comparing a population mean)

36. t-Tests Intuitively

37. t-test terminology Unpaired vs. paired t-test
Same as before !! Depends on your experiment
Unpaired t-Test: The hypotheses for the comparison of two independent groups are:
Ho: u1 = u2 (means of the two groups are equal)
Ha: u1 <> u2 (means of the two group are not equal)
Paired t-test: The hypothesis of paired measurements in same individuals
Ho: D = 0 (the difference between the two observations is 0)
Ha: D <> 0 (the difference is not 0)

38. Calculating t-test (t statistic)
Remember these formulae !!
Calculating t-test (t statistic)
First calculate t statistic value and then calculate p value
For the paired t-test, t is calculated using the following formula:
And n is the number of pairs being tested.
For an unpaired (independent group) t-test, the following formula is used:
Where σ (x) is the standard deviation of x and n (x) is the number of elements in x.
Where d is calculated by

39. Calculating p-value for t-test
When carrying out a test, a P-value can be calculated based on the t-value and the ‘Degrees of freedom’.
There are three methods for calculating P:
One Tailed >:
One Tailed <:
Two Tailed:
Where p(t,v) is looked up from the t-distribution table
The number of degrees (v) of freedom is calculated as:
UnPaired: n (x) +n (y) -2
Paired: n- 1 (where n is the number of pairs.)

40. p-values
Results of the t-test: If the p-value associated with the t-test is small (usually set at p < 0.05), there is evidence to reject the null hypothesis in favour of the alternative.
In other words, there is evidence that the mean is significantly different than the hypothesized value. If the p-value associated with the t-test is not small (p > 0.05), there is not enough evidence to reject the null hypothesis, and you conclude that there is evidence that the mean is not different from the hypothesized value.

41. t-value and p-value
Given a t-value, and degrees of freedom, you can look-up a p-value
Alternatively, if you know what p-value you need (e.g. 0.05) and degrees of freedom you can set the threshold for critical t

42. Finding a critical t A = .05 -tc A = .05 tc
The table provides the t values (tc) for which P(tx > tc) = A
Finding a critical t A
= .05
-tc A
= .05 tc
=-1.812
=1.812
t.100
t.05
t.025
t.01
t.005

43. Meaning of t-value High t-value
Take Gene A, assuming paired test:
For Either type of test
Average Difference is = 100, SD. = 0
t value is near infinity,
p is extremely low

44. Consider Gene M for a paired experiment
Where d is calculated by
Consider Gene M for a paired experiment
Average Difference is = 0
t value is zero, what does this mean?

45. Consider Gene T for a paired experiment
Where d is calculated by
Consider Gene T for a paired experiment

46. Hypothesis Testing Uses hypothesis testing methodology.
For each Gene (>5,000)
Pose Null Hypothesis (Ho) that gene is not affected
Pose Alternative Hypothesis (Ha) that gene is affected
Use statistical techniques to calculate the probability of rejecting the hypothesis (p-value)
If p-value < some critical value reject Ho and Accept Ha
The issues:
Large number of genes (or experiments)
Need quick way to filter out significant genes that have high fold change
Need also to sort genes by fold change and significance

47. Volcano Plots A visual approach
Volcano plots are a graphical means for visualising results of large numbers of t-tests allowing us to plot both the Effect and significance of each test in an easy to interpret way
For each gene calculate the significance of the change
(t-test, p-value)
For each gene compare the value of the effect between population WT vs. KO
(fold change)
Identify Genes with high effect and high significance
Volcano Plot

48. Volcano plots In a volcano plot:
X-axis represents effect measured as fold change:
y-axis represents the number of zeroes in the p-value
Effect = log(WT) – log(KO) 2
= log(WT / KO) 2
If WT = WO, Effect Fold Change = 0 , If WT = 2 WO, Effect Fold Change = 1
...

49. Numerical Interpretation (Significance)
(2 decimal places)
p< 0.1
(1 decimal place)
Using log10 for Y axis: