1. :: Microarray analysis ::
Data pre-processing
Normalization
Molecular diagnosis
Statistical classification
Florian Markowetz

2. From experiment to data

3. Raw data are not mRNA concentrations
tissue contamination
RNA degradation
amplification efficiency
reverse transcription efficiency
Hybridization efficiency and specificity
clone identification and mapping
PCR yield, contamination
spotting efficiency
DNA support binding
other array manufacturing related issues
image segmentation
signal quantification
“background” correction

4. Quality control: Noise and reliable signal
Probe level
Array level
Gene level
Arrays n
Probe level: quality of the expression measurement of one spot on one particular array
Array level: quality of the expression measurement on one particular glass slide
Gene level: quality of the expression measurement of one probe across all arrays

5. Probe-level quality control
Individual spots printed on the slide
Sources:
faulty printing, uneven distribution, contamination with debris, magnitude of signal relative to noise, poorly measured spots;
Visual inspection:
hairs, dust, scratches, air bubbles, dark regions, regions with haze
Spot quality:
Brightness: foreground/background ratio
Uniformity: variation in pixel intensities and ratios of intensities within a spot
Morphology: area, perimeter, circularity.
Spot Size: number of foreground pixels
Action:
set measurements to NA (missing values)
local normalization procedures which account for regional idiosyncrasies.
use weights for measurements to indicate reliability in later analysis.

6. Spot identification NA
Individual spots are recognized, size and shape might be adjusted per spot (automatically fine adjustments by hand).
Additional manual flagging of bad (X) or non-present (NA) spots NA X
poor spot quality
good spot quality
Different Spot identification methods: Fixed circles, circles with variable size, arbitrary spot shape (morphological opening)

7. Spot identification The signal of the spots is quantified.
Histogram of pixel intensities of a single spot
„Donuts“
Mean / Median / Mode / 75% quantile

8. Local background
GenePix
QuantArray
ScanAlyse

9. Array level quality control
Problems:
array fabrication defect
problem with RNA extraction
failed labeling reaction
poor hybridization conditions
faulty scanner
Quality measures:
Percentage of spots with no signal (~30% excluded spots)
Range of intensities
(Av. Foreground)/(Av. Background) > 3 in both channels
Distribution of spot signal area
Amount of adjustment needed: signals have to substantially changed to make slides comparable.

10. Gene-level quality control
Poor hybridization in the reference channel may introduce bias on the fold-change
Some probes will not hybridize well to the target RNA
Printing problems: such that all spots of a given inventory well have poor quality.
Gene g
A well may be of bad quality – contamination
Genes with a consistently low signal in the reference channel are suspicious

11. Gene expression data sample1 sample2 sample3 sample4 sample5 …
mRNA Samples
sample1 sample2 sample3 sample4 sample5 …
Gene
gene-expression level or ratio for gene i in mRNA sample j 3
Log2(red intensity / green intensity)
M =
Function (PM, MM) of MAS, dchip or RMA
average: log2(red intensity), log2(green intensity)
A =
Function (PM, MM) of MAS, dchip or RMA

12. Scatterplot Data Data (log scale)
Message: look at your data on log-scale!

13. MA Plot
A = 1/2 log2(RG)
M = log2(R/G)

14. Median centering
One of the simplest strategies is to bring all „centers“ of the array data to the same level.
Assumption: the majority of genes are un-changed between conditions.
Median is more robust to outliers than the mean.
Divide all expression measurements of each array by the Median.
Log Signal, centered at 0

15. Problem of median-centering
Median-Centering is a global Method. It does not adjust for local effects, intensity dependent effects, print-tip effects, etc.
Log Green
Log Red
Scatterplot of log-Signals after Median-centering
A = (Log Green + Log Red) / 2
M = Log Red - Log Green
M-A Plot of the same data

16. Lowess normalization Use the estimate to bend the banana straight
A = (Log Green + Log Red) / 2
M = Log Red - Log Green
Local estimate
Use the estimate to bend
the banana straight

17. Summary I Raw data are not mRNA concentrations
We need to check data quality on different levels
Probe level
Array level (all probes on one array)
Gene level (one gene on many arrays)
Always log your data
Normalize your data to avoid systematic (non-biological) effects
Lowess normalization straightens banana

18. From data to knowledge
Ok, now we made sure that our data is of high quality and systematic, non-biological effects are removed.
The result is a gene expression matrix
Gene
mRNA Samples
sample1 sample2 sample3 sample4 sample5 …
Is that already a result? No! It’s just data, not knowledge.
We need to use this data to answer a scientific question.

19. Supervised analysis = learning from examples, classification
We have already seen groups of healthy and sick people. Now let’s diagnose the next person walking into the hospital.
We know that these genes have function X (and these others don’t). Let’s find more genes with function X.
We know many gene-pairs that are functionally related (and many more that are not). Let’s extend the number of known related gene pairs.
Known structure in the data needs to be generalized to new data.

20. Un-supervised analysis
= clustering
Are there groups of genes that behave similarly in all conditions?
Disease X is very heterogeneous. Can we identify more specific sub-classes for more targeted treatment?
No structure is known. We first need to find it. Exploratory analysis.

21. Supervised analysis
Calvin, I still don’t know the difference between cats and dogs …
Oh, now I get it!!
Don’t worry!
I’ll show you once more:
Class 1: cats
Class 2: dogs

22. Un-supervised analysis
Calvin, I still don’t know the difference between cats and dogs …
I don’t know it either.
Let’s try to figure it out together …

23. Supervised analysis: setup
Training set
Data: microarrays
Labels: for each one we know if it falls into our class of interest or not (binary classification)
New data (test data)
Data for which we don’t have labels.
Eg. Genes without known function
Goal: Generalization ability
Build a classifier from the training data that is good at predicting the right class for the new data.

24. One microarray, one dot
Think of a space with #genes dimensions (yes, it’s hard for more than 3).
Each microarray corresponds to a point in this space.
If gene expression is similar under some conditions, the points will be close to each other.
If gene expression overall is very different, the points will be far away.
Expression of gene 2
Expression of gene 1

25. Which line separates best?D

26. No sharp knive, but a …
FAT PLANE

27. Support Vector Machines
Maximal margin
separating hyperplane
Datapoints closest
to separating
hyperplane
= support vectors

28. How well did we do? Same classifier (= line)
Training error: how well do we do on the data we trained the classifier on?
But how well will we do in the future, on new data?
Test error: How well does the classifier generalize?
Same classifier (= line)
New data from same classes
The classifier will usually perform worse than before:
Test error > training error

29. Train classifier and test it
Cross-validation
Training error
Train classifier and test it
Test error
Train
Test
K-fold Cross-validation
Step 1.
Train
Train
Test
Here for
K=3
Step 2.
Train
Test
Train
Step 3.
Test
Train
Train

30. Summary II Supervised and un-supervised learning
… are needed everywhere in biology and medicine
Microarrays = points in high-dimensional spaces
Classifiers = lines (hyperplanes) in these spaces
Support Vector Machines use maximal margin hyperplanes as classifiers
Classifier performance: Test error > training error
Cross-validation is the right way to evaluate classifier performance

31. Experimental Cycle
Biological question (hypothesis-driven or explorative)
To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination:
He may be able to say what the experiment died of.
Ronald Fisher
Experimental design
Failed
Microarray experiment
Quality
Measurement
Image analysis
Pre-processing
Normalization
Pass
Analysis
Estimation
Testing
Clustering
Discrimination
Biological verification
and interpretation

32. Books
Terry Speed, „Statistical Analysis of Gene Expression Microarray Data”. Chapman & Hall/CRC
David W. Mount, „Bioinformatics“, Cold Spring Harbor
Giovanni Parmigani et al, „The Analysis of Gene Expression Data“, Springer
Gentleman, Carey, Huber, “Bioinformatics and Computational Biology Solutions Using R and Bioconductor”, Springer
Pierre Baldi & G. Wesley Hatfield, „DNA Microarrays and Gene Expression”, Cambridge

33. And how do I analyze my own data?
Open source
Free
Easy installation
Helpful community
High quality standards
Regularly maintained and updated
Tons of documentation
Every package comes with example vignettes to walk you through standard tasks.

34. Acknowlegdements http://compdiag.molgen.mpg.de/ngfn/
I ‘borrowed’ slides from:
Tim Beissbarth, Achim Tresch, Wolfgang Huber, Ulrich Mansmann, Terry Speed, Jean Yang, Benedikt Brors, Anja von Heydebreck, Rainer König
More info on microarray analysis, lectures, tutorials: