Using WebBased Tools for Microarray Analysis

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Using Web-Based Tools for Microarray Analysis
Michael Elgart
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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

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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

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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.

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The Boom of Microarray Technology Number of
Publications with Affymetrix Chips
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Whats 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

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GEO Public Database Example
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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

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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

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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

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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

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Microarray Overview
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Probe deposition on array

• Contact printing
• Ink jet spraying
• On chip synthesis

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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
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Under the microscope
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Ink jet spraying
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Ink jet sprayed spots on a chip
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Affymetrix

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

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On chip synthesis
Lithography
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Set of probes that identifies a transcript
ProbeSet
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Affymetrix

• Gene Expression Arrays Transcripts/Genes
• Arabidopsis Genome 24,000
• C. elegans Genome 22,500
• Drosophila Genome 18, 500
• E. coli Genome 20, 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 (37,500 23,300
  pathogen), Grape (15,700)
• Canine (21,700), Bovine (23,000),B.subtilis
  (5,000), S. aureus (3,300 ORFS), Xenopus (14, 400)

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Spots on an Affymetrix chip printed using
photolithography
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DNA Deposition on Array
2um
Taken from Duggan et al, Nature Genetics 2110
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RNA Quality and Quantity
28S rRNA
18S rRNA
Degraded sample
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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

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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

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Slide Scanning
Selectable lasers Emission filters with range
from 500-700 nm 5 micron resolution
Goal is to generate images of the arrays that are
used as input for quantitation algorithms
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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

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Usually the 75th percentile
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Do not use MM data! MAS (3,4,5) is NOT GOOD Use
RMA !!!
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Fortunately (?) you dont do this
The result
INTENSITY NumberCells4691556 X Y MEAN STDV NPIX
ELS 0 0 30022.0 4025.9 9 1
0 507.0 48.5 9 2 0 30116.0 4500.7 9 3
0 602.0 97.3 9 4 0 339.0 36.3 9 5
0 491.0 59.1 9 6 0 29208.0 3090.8 9 7
0 877.0 126.0 9 8 0 28683.0 4069.2 9 9
0 645.0 63.6 9 10 0 28536.0 3462.7 9 11
0 473.0 100.5 9 12 0 29509.0 4287.0 9
13 0 667.0 83.2 9
CEL Version3 HEADER Cols2166 Rows2166 Tota
lX2166 TotalY2166 OffsetX0 OffsetY0 GridCorner
UL623 408 GridCornerUR16090 586 GridCornerLR159
32 15984 GridCornerLL464 15807 . . . .
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So can we just use the data now?

• Not quite

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Sources of Microarray Data Variability

• Biological variability in the populationNo 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.

Expression values in 2 replicas will be
different! Can we handle it?
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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.

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Quantile Normalization

• Intensity distributions are adjusted to be
  equivalent
• Scaling to a target intensity sets the mean
  signal intensity to the defined value

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Probe Intensity
Probe Intensity
Number of Probes
Number of Probes
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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

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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

Experiment
Biological Replicates
Replicate 1
Replicate 2
Extract 2
Extract 1
Technical Replicates
We need normalization to be able to look at the
biological differences between samples and not
technical ones Elgart M.
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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?

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Summary Statistics
All using only Top 10,000 brightest probes
Correlation (gt2x Diffl Only)
Red In Replicates
Agree on 2x Diffl
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Set of probes that identifies a transcript
ProbeSet
If all 10 probes give high signal in Treatment
and low in Control then alls well. But what if
only 6 of 10 are positive? How do we decide
whether this gene is expressed?
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Set of probes that identifies a transcript
ProbeSet
If all 10 probes give high signal in Treatment
and low in Control then alls well. But what if
only 6 of 10 are positive? How do we decide
whether this gene is expressed?
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• Is this a hands-on thing ?
• Yes.
• Example

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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

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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

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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 Analysis

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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 Analysis

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Verifications...
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The END!
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Sources

• Gene Expression Omnibus
• http//www.ncbi.nlm.nih.gov/geo/
• R
• www.r-project.org
• Bioconductor
• www.bioconductor.org
• Race
• http//race.unil.ch
• Microarray Blob Remover(MBR)
• http//liulab.dfci.harvard.edu/Software/MBR/MBR.ht
  m
• Significance Analysis of Microarrays(SAM)
• http//www-stat.stanford.edu/tibs/SAM/
• Affymetrix NetAffx
• http//www.affymetrix.com/analysis/index.affx
• Onto Tools
• http//vortex.cs.wayne.edu/projects.htm
• Ensembl
• http//www.ensembl.org/index.html
• CisGenome