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HWW Gene Expression Experiments: H ow? W hy? W hat’s the problem?. High Throughput Experiments. Functional Genomics. Bioinformatics. DNA Hybridization. The principle: have two denatured DNA strands bond together, then check double strand amount (florescent dye, radioactive label)

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Hww gene expression experiments h ow w hy w hat s the problem
HWW Gene Expression Experiments: How?Why?What’s the problem?

High throughput experiments
High Throughput Experiments



Dna hybridization
DNA Hybridization

  • The principle: have two denatured DNA strands bond together, then check double strand amount (florescent dye, radioactive label)

  • “Traditional”: Southern/Northern/Western Blot

  • The great advance: micro array DNA chips – automation, material eng., computer aided (including algorithmic solutions)


cDNA microarrays have evolved from Southern blots, with clone libraries gridded out on nylon membrane filters being an important and still widely used intermediate. Things took off with the introduction of non-porous solid supports, such as glass - these permitted miniaturization - and fluorescence based detection. Currently, about 20,000 cDNAs can be spotted onto a microscope slide. The other, Affymetrix technology can produce arrays of 100,000 oligonucleotides on a silicon chip.

The process

Building the Chip:





Preparing RNA:

Hybing the Chip:








Building the Chip:



Full yeast genome

= 6,500 reactions

IPA precipitation +EtOH washes + 384-well format


The arrayer: high precision spotting device capable of printing 10,000 products in 14 hrs, with a plate change every 25 mins


Polylysine coating for adhering

PCR products to glass slides


Chemically converting the positive polylysine surface to prevent non-specific hybridization

Preparing RNA:


Designing experiments to profile conditions/perturbations/

mutations and carefully controlled growth conditions


RNA yield and purity are determined by system. PolyA isolation is preferable but total RNA is useable. Two RNA samples are hybridized/chip.


Single strand synthesis or amplification of RNA can be performed.

cDNA production includes incorporation of Aminoallyl-dUTP.

Hybing the Chip:


Cy3 and Cy5 RNA samples are simultaneously hybridized to chip. Hybs are performed for 5-12 hours and then chips are washed.


Ratio measurements are determined via quantification of 532 nm and 635 nm emission values. Data are uploaded to the appropriate database where statistical and other analyses can then be performed.


Two RNA samples are labelled with Cy3 or Cy5 monofunctional dyes via a chemical coupling to AA-dUTP. Samples are purified using a PCR cleanup kit.

Printing microarrays
Printing Microarrays

  • Print Head

  • Plate Handling

  • XYZ positioning

    • Repeatability & Accuracy

    • Resolution

  • Environmental Control

    • Humidity

    • Dust

  • Instrument Control

  • Sample Tracking Software

Printing approaches
Printing Approaches

Non - Contact

  • Piezoelectric dispenser

  • Syringe-solenoid ink-jet dispenser

    Contact (using rigid pin tools, similar to filter array)

  • Tweezer

  • Split pin

  • Micro spotting pin

Practical problems
Practical Problems

  • Surface chemistry: uneven surface may lead to high background.

  • Dipping the pin into large volume -> pre-printing to drain off excess sample.

  • Spot variation can be due to mechanical difference between pins. Pins could be clogged during the printing process.

  • Spot size and density depends on surface and solution properties.

  • Pins need good washing between samples to prevent sample carryover.

Post processing arrays
Post Processing Arrays

Protocol for Post Processing Microarrays

Hydration/Heat Fixing

1. Pick out about 20-30 slides to be processed.

2. Determine the correct orientation of slide, and if necessary, etch label on lower left corner of array side

3. On back of slide, etch two lines above and below center of array to designate array area after processing

4. Pour 100 ml 1X SSC into hydration tray and warm on slide warmer at medium setting

5. Set slide array side down and observe spots until proper hydration is achieved.

6. Upon reaching proper hydration, immediately snap dry slide

7. Place slides in rack.

Practical problems 1
Practical Problems 1

  • Comet Tails

  • Likely caused by insufficiently rapid immersion of the slides in the succinic anhydride blocking solution.

Practical problems 3
Practical Problems 3

High Background

  • 2 likely causes:

    • Insufficient blocking.

    • Precipitation of the labeled probe.

      Weak Signals

Practical problems 4
Practical Problems 4

Spot overlap:

Likely cause: too

much rehydration

during post -


Steps in images processing
Steps in Images Processing

1. Addressing: locate centers

2. Segmentation: classification of pixels either as signal or background. using

seeded region growing).

3. Information extraction: for

each spot of the array,

calculates signal intensity

pairs, background and quality


Steps in image processing
Steps in Image Processing

3. Information Extraction

  • Spot Intensities

    • mean (pixel intensities).

    • median (pixel intensities).

    • Pixel variation (IQR of log (pixel intensities).

  • Background values

    • Local

    • Morphological opening

    • Constant (global)

    • None

  • Quality Information




This is the process of assigning coordinates to each of the spots.

Automating this part of the procedure permits high throughput analysis.

4 by 4 grids

19 by 21 spots per grid




Problems in automatic addressing
Problems in automatic addressing

Misregistration of the red and green channels

Rotation of the array in the image

Skew in the array


Segmentation methods
Segmentation methods

  • Fixed circles

  • Adaptive Circle

  • Adaptive Shape

    • Edge detection.

    • Seeded Region Growing. (R. Adams and L. Bishof (1994) :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.

  • Histogram Methods

    • Adaptive threshold.

Limitation of circular segmentation
Limitation of circular segmentation

  • Small spot

  • Not circular

Results from SRG

Information extraction
Information Extraction

  • Spot Intensities

    • mean (pixel intensities).

    • median (pixel intensities).

  • Background values

    • Local

    • Morphological opening

    • Constant (global)

    • None

  • Quality Information

Take the average

Summary of analysis possibilities
Summary of analysis possibilities

Determine genes which are differentially expressed (this task can take many forms depending on replication, etc)

Connect differentially expressed genes to sequence databases and perhaps carry out further analyses, e.g. searching for common upstream motifs

Overlay differentially expressed genes on pathway diagrams

Relate expression levels to other information on cells, e.g. known tumour types

Define subclasses (clusters) in sets of samples (e.g. tumours)

Identify temporal or spatial trends in gene expression

Seek roles for genes on the basis of patterns of co-expression

……..much more

Many challenges: transcriptional regulation involves redundancy, feedback, amplification, .. non-linearity

Biological Question

Data Analysis & Modeling


Microarray Life Cycle


Microarray Reaction

Taken from Schena & Davis

Oligonucleotide arrays tech
Oligonucleotide Arrays Tech.

  • ~20 probes per “gene”, 25bases each*

  • Probe size: 24x24 micron (contain ~106 copies of the probe)

  • Probe is either a Perfect Match (PP) or a Miss Match (MM)

  • MM:

    • usually at the center of the probe

    • Aim: to give estimate on the random hybrd.


  • Data is noisy, missing values.

  • Each array is scanned separately, in different settings

    → To extract biological meaningful results we need:

  • Good expression estimations

  • Scale/Normalize across arrays

What we need
What we need

  • Image segmentation

  • Background/Gradient correction

  • Artifact detection

  • Allow array to array comparison (scale/normalize)

  • Assess gene presence (quantitative “Measure”)

  • Find differentially expressed genes

Why isn t normalization easy
Why isn’t “Normalization” Easy?

  • No ability to read mRNA level directly

  • Various noise factors → hard to model exactly.

  • Variable biological settings, experiment dependent.

  • Need to differentiate between changes caused by biological signal from noise artifacts.

Variability sources
Variability Sources

  • Real Biology –

    • Biological noise

    • Biological Signal

  • Sample preparation related

  • Technical dependent

Dchip mbei
dChip MBEI

  • Based on several papers by Li & Wong (PNAS, 2001 vol 98 no.1 and others)

  • Implemented on their freely available dChip software

  • Model based: The estimation is based on a model of how the probe intensity values respond to changes of the expression levels of the gene

Dchip model
dChip Model

i is the array indexj is the probe index

is the baseline response of the probe due to non specific hybridization

is the rate of increase of the MM response

is the additional rate of increase of the PM response

Dchip reduced model
dChip “Reduced” Model

Basic idea: Least square parameter estimation, iteratively fitting and

Dchip reduced model1
dChip “Reduced” Model

For one array, assume that the set has been learned from a large number of arrays, and therefore known and fixed

Given this set, the linear least square estimate for theta is

An approx. Std. can be computed for this estimator:

Dchip reduced model2
dChip “Reduced” Model

  • Similarly, we regard the set as known, and compute std. for each phi

  • We use these estimated Std. to find outlier and exclude them from the computation:

Normalization scaling

  • We saw how to get MBEI from dchip, i.e measure “quantitation “

  • We still need to scale the different arrays:

    • Arrays usually differ in overall image brightness (differ in time, place, exper. Cond….)

  • This is usually done PRIOR to the “measure quantitation” manipulations (as dChip’s MBEI we just described).

Global normalization scaling
Global – Normalization/Scaling

  • Suppose we have two arrays X,Y with values x1…xM and y1 .. yM

  • “Global” normalization (MAS 5): find the constant “a” such that

    Which means:

    When we have multiple arrays then we choose Y to be the avg. of all arrays or compute a such that sum_i (x_i) = constant

Better way: a(x) i.e adopt the fit parameter as a function of expression level ( as by dChip)

Dchip normalization scaling
dChip – Normalization/Scaling

  • Big question: Which gene to use for this scaling??

  • There are various ways to choose the set:

    • “House keeping” genes (Affy. chips)

    • Spiked controls added in various stages of the experiment, in a range of concentrations

    • Both of the above are very good in theory but (still) not in practice (esp. in Affy chips)

    • The result: several approaches suggested on how to use the set of genes tested in the experiments

  • We’ll review dChip’s solution: The “Invariant set”

Dchip invariant set
dChip “Invariant Set”

  • Main idea:

    • Initialize: set of probes P = all probes

    • Order the probes in both arrays by their expression values

    • Give each probe in each array an index according to it’s relative expression order

    • Find a set of probes P’ who’s relative order is similar in both arrays

    • Set P = P’ and iterate from stage (2) until convergence

    • Use the resulting P to compute a piecewise linear running median line as the normalization curve

Normalization tools current state
Normalization Tools – Current State

  • Commonly Used:

    • RMA by Speed Lab

    • dChip by Li & Wong

    • GeneChip = MAS5 (Affy. built in tool)

  • “The Future”:

    • New Chip design (both Affy. And cDNA) with better probes, better built in controls etc.

    • New algorithms – facilitating probes GC content (gcRMA), location etc.

    • New MAS tool (this year ?) is also supposed to incorporate RMA,dChip etc.

How to measure performance
How to Measure Performance?

  • Theoretical Validation – use some theoretical assumptions and evaluate Statistical characteristics of the method at hand.

  • Experimental Validation –

    • Use public data sets to measure different aspects of performance

    • Evaluate relevant characteristics on your data set. Design your data set accordingly (if possible)

A benchmark for affy expression measures
A Benchmark for Affy. Expression Measures*

  • Main Idea: Define a “universal” test set & test statistics

  • Based on 3 publicly available spike in data sets

  • Tests for:

    • Variability across replicate arrays

    • Response of GE measures to change in abundance of RNA

    • Sensitivity of fold change measures to amount of actual RNA sample

    • Accuracy of fold change as a measure of relative expression

    • Usefulness of raw fold change score to detect differential expressed genes

* Cope et al. Bioinformatics, 03 (Speed’s Lab)

Ma plot
MA Plot

M1 = X1 – X2A = (X1 + X2)/ 2 Where Xi is the log2 of expression measure

Variance across replicates plot
Variance across replicates plot

Test Statistics: 1. Median std. 2. Avg. R2 (squared corr. coef.) between two replicates

Observed expression vs nominal expression plots
Observed Expression vs. Nominal Expression Plots

Test Statistics: Fit a linear curve and compute1. linear fit slope (should be 1) 2. R2 to the linear fit

Roc curves
ROC Curves

  • One of the chief uses of GE arrays is to identify differentially expressed genes

  • ROC ( Receiver Operator Characteristic):A graphical representation of both Sens. and Spec. as a function of threshold value

  • X axis: TPR (Sens.)

  • Y axis: FPR (1-Spec.)

  • In this case: Use fold change as the score, knowing which probes are spiked or not..

Fc roc plots
FC ROC Plots

Here actual TP, FP numbers are used for the axes

Test Statistic: AUC (area under the graph)

Fc roc plots1
FC ROC Plots

Same as before, but only for FC = 2 cases (harder)

The benchmark bottom line
The Benchmark – Bottom Line

  • 15 parameters used to test performace

  • 3 “synthetic” spike in data sets

  • Automatic submission and evaluation tool + comparative results at:www.biostat.jhsph.edu

Other tests
Other Tests

  • Evaluate separately normalization and expression measures techniques ( as by Huffman et al., Genome Biology, Vol. 3, 2002)

  • How do we evaluate performance on our own, very specific, data??? ( hint: see next class..)