1. Using Web-Based Tools for Microarray Analysis
Michael Elgart

2. Outline
Introduction to microarrays – why use them and what to expect from their results
What are they?
Why use them?
What types are there?
Low level analysis
Background correction
Normalization
Quality control
Significance analysis
Annotations
Functional Analysis:
Gene Ontology
Promoter Analisys

3. Outline
Introduction to microarrays – why use them and what to expect from their results
What are they?
Why use them?
What types are there?
Low level analysis
Background correction
Normalization
Quality control
Significance analysis
Annotations
Functional Analysis:
Gene Ontology
Promoter Analisys

4. What is a microarray?
A tool for analyzing gene expression that consists of a small membrane or glass slide containing samples of thousands of genes arranged in a regular pattern.

5. The Boom of Microarray Technology: Number of Publications with Affymetrix Chips
200
400
600
800
1000
1200
Year
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Number of publications

6. What’s the Point? Large scale (genome-wide) screening
Eliminate bias of pre-selecting candidate genes
Test multiple hypotheses simultaneously
Generate new hypotheses by identifying novel genes associated with experiment
Identify novel relationships/patterns among genes

7. GEO: Public Database Example

8. Outline
Introduction to microarrays – why use them and what to expect from their results
What are they?
Why use them?
What types are there?
Low level analysis
Background correction
Normalization
Quality control
Significance analysis
Annotations
Functional Analysis:
Gene Ontology
Promoter Analisys

9. What are DNA microarrays?
Microarrays are a method of scanning the genome based on an well known property of nucleic acids (hybridization)
Complementary strands of DNA/RNA will find each other in solution

10. Types of DNA Microarray Experiments
Some types of experiments that can
be done:
Measure changes in gene expression
RNA hybridizes to DNA
Identify genomic gains and losses
Genomic DNA hybridizes to DNA
Identify mutations in DNA
PCR product hybridizes to DNA

11. Expression Microarray Basics
Two parts:
Probes: the single stranded DNA molecules on the solid surface
Targets: the single stranded labeled population from your experimental source

12. Microarray Overview
Probe

13. Probe deposition on array
Contact printing
Ink jet spraying
On chip synthesis

14. Pin Spotting of DNA Arrays
Can be automated or manual
Relatively cheap but may result in QC issues with spots
~10$ per 100 probe array

15. Under the microscope

16. Ink jet spraying

17. Ink jet sprayed spots on a chip

18. Affymetrix
Will be dealing mainly with this type today, so here is a little more data

19. On chip synthesis
Lithography

22. Set of probes that identifies a transcript = ProbeSet

23. Affymetrix: Gene Expression Arrays Transcripts/Genes
Arabidopsis Genome 24,000
C. elegans Genome 22,500
Drosophila Genome 18, 500
E. coli Genome , 366
Human Genome U133 Plus 47,000
Mouse Genome 39, 000
Yeast Genome 5, 841 (S. cerevisiae) & 5, 031 (S. pombe)
Rat Genome 30, 000
Zebrafish 14, 900
Plasmodium/Anopheles 4,300 (P. falciparum) & 14,900 (A. gambiae)
Barley (25,500), Soybean (37, ,300 pathogen), Grape (15,700)
Canine (21,700), Bovine (23,000),B.subtilis (5,000), S. aureus (3,300 ORFS), Xenopus (14, 400)

24. Spots on an Affymetrix chip printed
using photolithography

25. DNA Deposition on Array
2um
Taken from Duggan et al, Nature Genetics 21:10

27. RNA Quality and Quantity
28S rRNA
18S rRNA
Degraded sample

28. Hybridization = expression level
The amount of hybridization of RNA to a fragment of DNA representing any gene can be measured if the RNA is labeled with some dye
The intensity of hybridization is a surrogate that measures the level of expression of the gene represented by that DNA fragment

29. Hybridization and Washing of DNA Microarrays
Remains one of the most poorly controlled steps in the process
Long oligonucleotide probes were designed to standardize the Tms across the slide
However, there will be variable efficiency, variable specificity

30. Slide Scanning Selectable lasers Emission filters with range
from nm
5 micron resolution
Goal is to generate images of
the arrays that are used as
input for quantitation algorithms

31. Outline
Introduction to microarrays – why use them and what to expect from their results
What are they?
Why use them?
What types are there?
Low level analysis
Background correction
Normalization
Quality control
Significance analysis
Annotations
Functional Analysis:
Gene Ontology
Promoter Analisys

35. Usually the 75th percentile

36. Do not use MM data!
MAS (3,4,5…) is NOT GOOD
Use RMA !!!

37. Fortunately (?) you don’t do this
The result
[INTENSITY]
NumberCells=
X Y MEAN STDV NPIXELS
[CEL]
Version=3
[HEADER]
Cols=2166
Rows=2166
TotalX=2166
TotalY=2166
OffsetX=0
OffsetY=0
GridCornerUL=
GridCornerUR=
GridCornerLR=
GridCornerLL= .

38. So can we just use the data now?
Not quite…

39. Sources of Microarray Data Variability
Biological variability in the population No good solution here…
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
Expression values in 2 replicas will be different!
Can we handle it? 39

40. Normalization
Deals with the fact that the results from identical experiments on two identical microarrays will never be exactly the same. In addition to unavoidable random errors there are also systematic differences caused by:
Different incorporation efficiencies of dyes. For example, green colored markers are stronger then red ones (measured as stronger illumination) creating a bias between experiments done with green and red markers.
Different amounts of mRNA in the tested sample, causing different expression levels.
Difference in experimenter or protocol.
Different scanning parameters
Differences between chips created in different production batches.

41. Quantile Normalization
Intensity distributions are adjusted to be equivalent
Scaling to a target intensity sets the mean signal intensity to the defined value
500
Probe Intensity
Probe Intensity
Number of Probes
Number of Probes

42. Background Correction
Different GC content of probes
Location on Chip Effect
etc.
All this need to be compensated for. The algorythm to do it is
RMA

43. Correct Experimental Design
Tree representation of replicate experiments:
The first level is at the level of biological replicates
This is followed by two independent mRNA extractions
In each microarray experiment, each gene (each probe or probe set) is really a separate experiment in its own right
Biological Replicates
Experiment
Replicate 1
Replicate 2
Technical Replicates
Extract 1
Extract 2
“We need normalization to be able to look at the biological differences between samples and not technical ones” Elgart M. 43

44. Reproducibility
How big is the difference between sample that was twice hybridized on same type of array?
If we look at technical replicas, what do we expect to see?

45. Summary Statistics Correlation (>2x Diffl Only) % Agree on
All using only Top 10,000
brightest probes
Correlation
(>2x Diffl Only)
Red = In Replicates
% Agree on
2x Diff’l

46. Set of probes that identifies a transcript = ProbeSet
If all 10 probes give high signal in Treatment and low in Control then all’s well.
But what if only 6 of 10 are “positive”? How do we decide whether this gene is expressed?

47. Set of probes that identifies a transcript = ProbeSet
If all 10 probes give high signal in Treatment and low in Control then all’s well.
But what if only 6 of 10 are “positive”? How do we decide whether this gene is expressed?

48. Is this a “hands-on” thing ?
Yes.
Example :

49. 49

50. Outline Background correction Normalization Quality control
Introduction to microarrays – why use them and what to expect from their results
What are they?
Why use them?
What types are there?
Low level analysis
Background correction
Normalization
Quality control
Significance analysis
Annotations
Functional Analysis:
Gene Ontology
Promoter Analisys