Arrays

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Arrays How do they work ? What are they ?
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Arrays are inverted Northerns
Tissue sample
quantitate
Extract target RNA
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Probe preparation
Acquire or Generate probes All the genes you
want
Spot
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Hybridise Scan
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Arrays How do you make them ?
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Arrayers
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Pins
Pin type blunt, ring, quill, coated.. Breaking
bending, sticking Consistency of spots
coffee-cup, splash, drip Contamination
carry-over, dust, hairs, crystals. Etc etc.
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Slides

• Cracking
• Splitting
• Exfoliating
• Fluorescing
• Coatings - Hydrophobic, hydrophilic, correctly
  aged poly-lysine (a bit of an art)
• Home-made vs bought (cost of internal vs
  external quality control.
• Scan before coating, scan after coating, scan
  after arraying, scan after hyb-ing all part of QC
• Etcetc...

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RNA Quality control
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What biological questions can you answer with
arrays ?
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Global analysis of gene expression
Im a big fan of ignorance based techniques
because humans have a lot of ignorance, and we
want to play to our strong suit. -Eric Lander
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What it looks like
Before processing, we have a LOT of spots
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Example Hybridisation
After processing, we have a LOT of objective data
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Sorting out gene families
microarray
5 hormone response gene family members In
different experiments
Biopsy type
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However ..
Duplication in genomes is a real problem
Human
Plant
Yeast
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Apart from wholesale duplication
Gene families (plant) ( of members as a
proportion of the genome)

• Conservation between genes
• 37 of genes are highly conserved
• (TBLASTX Elt10-30)
• 10 more are partially conserved
• (TBLASTX Elt10-5)

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Segmental Duplication of the Human Genome
Sequence Identity
Exp lt 1e-10
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What goes on the slide ?
One choice would be Amplifications of cDNAs
chosen by partial sequence (ESTs)
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ESTs have inherent problems
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• Better solutions
• GSTs (gene specific tags)
• Oligo arrays
• Affymetrix genechips

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Bioinformatics I How do you designthe
experiments to best sort out the data ?
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Replication of spots (hybridisation controls)

• 3 is a statistical minimum
• mean
• median
• or mode ? ?

Eliminate rogues throw away inconsistent data
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Why replicate spots ?

• spot variability
• hybridisation variability
• detection / analysis variability
• Lee et al (2000) PNAS 979834-9839
• Comparison of 3 replicates of 288 genes, 6mm
  apart on one slide, using only one channel (Cy3).
• 9 false positives for 1 of the replicates
  (2,500)
• 0.7 false positives for 3 spots per gene (200)

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Replication between slides
(Using the same target RNA sample)

• slide variability (spotting batches)
• Reduced by large batch generation and batch QC
• Fluors
• Swapped labelling experiments.
• Also Differential degradation (re-scanning)

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Replication between samples

• RNA probes (pooling vs replicates)
• equivalence of material
• environment
• sampling
• extraction

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Percentage CV as Estimate of Variability

• CV is a measure of variance amongst replicates
  of a single condition
• Defined as the standard deviation divided by the
  mean multiplied by 100
• Example 5 signal values representing 5
  replicates
• - 230.4, 241.7, 252.9, 338.8, 178.9
• - Mean 248.56 ? 57.9 CV 23.29
• CV helps you assess pilot studies

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Bioinformatics II How do you display the data
from arrays to make sense of it ?
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Problems with arraying
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70
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The Yeast array
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Scanning software

• e.g. GenePix Pro to analyse images from GenePix
  scanner
• semi-automated spot finding
• produces mean, median, SD of the pixels in each
  spot
• reports background intensities around each spot
• reports a normalisation factor other QC
  measures

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Primary Analysis(Analysing the raw spot
intensities)

• Ratios of intensities
• Comparing the intensity to the control channel
• Logged intensities
• Makes variation of intensities and ratios of
  intensities more independent of absolute
  magnitude
• Normalised or standardised data
• Removes systematic variations in experiments.

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Primary Analysis - Affy Data
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Why Normalise ?

• To correct for systematic measurement error and
  bias in data
• - Differences in probe labeling
• - Target concentration
• - Hybridization efficiency
• - Scanner noise
• Allows for data comparison

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Data Normalization Methods

• Scaling Factor (linear) normalization
• - Global or selected gene set
• - Works well when data quality metrics are
  consistent
• - Simplifies database construction
• - Weakness assumes error is uniform across all
  genes
• assumes total mRNA is the
  same for all cells
• Non-linear
• - Can provide higher precision, especially at
  the extremes
• - Requires selected gene (invariant) set
• - May give false confidence in poor data

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Secondary Analysis(Analysing intensity ratios
across whole slide, gene expression histories)

• scatter plot of array data e.g.
• log Cy5 vs. log Cy3
• Separate Affy chips

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Normalization Curves
Not normalized
Normalized
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2 Slide - scatter
Using this you can plot two slides against each
other (this is with (optional) log scales).
Naturally you can click on each gene.
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Standards for Storing Data
Minimum Information About a Microarray Experiment
(MIAME) http//www.mged.org

• Experimental design time course, dose
  response, normal vs. treated
• Array description description of array which
  physical copy used
• Description of sample growth conditions, dev.
  stage, labelling
• Hybridisation conditions wash procedure,
  time, concentration
• Measurements scanning hardware software
• Normalisation controls housekeeping genes,
  spiking controls

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Database Software for Storing Data
ArrayExpress

• ArrayExpress
• A data model designed by EBI for array data
• Modelled to support MIAME standards
• Continuing adoption support of new standards
• MicroArray Gene Expression Object Model
  (MAGE-OM)
• MicroArray Gene Expression Markup Language
  (MAGE-ML)

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Secondary Analysis(Analysing intensity ratios
across whole slide, gene expression histories)
Gene expression history how has my favourite
gene been expressed in all experiments in the
database
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Scatter plot 2 two genes, many slides
Apetela (x) Vs Agamous (y)
petal
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Tertiary Analysis(Clustering)

• Clustering of genes based on similar expression
  profiles
• Several techniques have been applied to array
  data
• Hierarchical clustering
• Non-hierarchical clustering methods
• Principle Component Analysis Self Organising
  Maps K-means clustering

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Yeast cell cycle array data set Organised by
Gene type
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Yeast cell cycle array data set Organised by
cycle expression
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An Expression Roadmap to Wood formation
Bark Phloem Cambium
Xylem
Division Expansion 2nd
cell-wall
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Differential expression patterns
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Cluster analysis
A3
A10
A3
A10
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Lignin biosynthesis
C4H EC1.14.13.11
COMT EC2.1.1.68
F5H EC1.14.13.-
COMT EC2.1.1.68
PAL EC4.3.1.5
C3H
4CL
4CL
4CL
4CL
EC4.1.1.28
EC3.2.1.21
4CL EC6.2.1.12
Coumarine
CCoAOMT EC2.1.1.104
CCoAOMT EC2.1.1.104
CCoA-3H
CCR
CCR
CCR
EC1.14.11.9
CCR EC1.2.1.44
F5H EC1.14.13.-
Flavonoids
CAD
CAD
Fold Change
COMT EC2.1.1.68
F5H EC1.14.13.-
CAD EC1.1.1.195
15 4 3 2 1.5 11 1.5 2 3
4 15
Transport
Anionic Peroxidases EC1.11.1.7
Polymerisation
C
D
E
A
B
Laccase EC1.10.3.2
Dirigent-like
L I G N I N
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...or general metabolites
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Amniotic membrane for Ocular surface
reconstruction
Oligo array
2D protein
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Caution !
These are just CLUES !!
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What software should I use ?

• Free
• Expression profiler
• D-Chip
• Commercial
• J-express
• Genespring

but dont forget Excel