affymetrix genechips and analysis methods
DESCRIPTION
Affymetrix GeneChips and Analysis Methods. Neil Lawrence. Schedule. and some of this. Photolithography. Photolithography (Affymetrix) Based on the same technique used to make the microprocessors. Oligonucleotides are generated in situ on a silicon surface. - PowerPoint PPT PresentationTRANSCRIPT
Affymetrix GeneChipsand
Analysis Methods
Neil Lawrence
Schedule
18th April Introduction and Background
25th April cDNA Mircoarrays
2nd May No Lecture
9th May Affymetrix GeneChips
16th May Guest Lecturer – Dr Pen Rashbass
23rd May Analysis methods
and some of this
Photolithography
• Photolithography (Affymetrix) – Based on the same technique used to make
the microprocessors.– Oligonucleotides are generated in situ on a
silicon surface. – Oligonucleotides up to 30bp in length. – Array density of 106 probes per cm-2.
Affymetrix Stock Price
Affymetrix
• Only one biological sample per chip.
• Oligonucleotides represent a portion of a gene’s sequence.
• Twenty sub-sequences present for each gene.
Perfect vs Mismatch
• For each oligonucleotide there is– A perfect match– A mismatch
• The perfect match is a sub-sequence of the true sequence.
• The mismatch is a sub-sequence with a ‘central’ base-pair replaced.
Affymetrix Analysis
• Mismatch is designed to measure ‘background’.
• Signal from each sub-sequence isIPerfect match – IMismatch
• Twenty of these sub-sequences are present.
• Average of all these signals is taken.
Problems
• Sometimes Imismatch > Iperfect match
– Solution: set it to 20??!!!
• Other issues– Present/Absent call
• Based on the number of Signals > 0.
• Proprietary Technology– You don’t know what the subsequences are.
• Apparently this is changing!
Scaling Factors – Maximum likelihood estimation
• The data produced is still affected by undesirable variations that we need to remove.
• We can assume that the variations are primarily multiplicative: (No intensity dependent or print-tip effect)
Obs.-exp.Level = true-exp.Level * error *random-noise
(chip variations) (biological noise)
Model Assumption
• Organise the twelve values from three exogenous control species in a matrix:
X=[NControls * NChips]
• Error model: Here mi is associated with each control and rj is associated
with each chip or experiment.
Taking logs we have:
Scaling Factors
• Calculating scaling factors using maximum likelihood estimation of the model parameters
Likelihood:
• Estimates are calculated solving
Scaling factors are thus :
You Should Know
• The Central Dogma (Gene Expression).
• cDNA chip overview.
• Noise in cDNA chips.
• Affymetrix GeneChip overview.
Analysis of Microarray Data
• Vanilla-flavour analysis:– Obtain temporal profiles (e.g. from last
week’s mouse experiment).– ‘Cluster’ profiles– Assume genes in the same cluster are
functionally related.
Temporal Profiles
• Lack of statistical independence.
• Take temporal differences to recover.
• Justified by assuming and underlying Markov process.
Analysis of Microarray Data
Day 1 Day 2 Day 3 Day 4 Day 5 Day 60
40
80
120
2-1 3-2 4-3 5-4 6-5
-80
-40
0
40
80
Original Temporal Profile
Take Temporal Differences
Gene e
xpre
ssio
n level
Change in e
xp.
level
Consider Clustering via MSE
These two similar profiles won’t cluster
Day 1 Day 2 Day 3 Day 4 Day 5 Day 60
40
80
120
Gene e
xpre
ssio
n level
Day 1 Day 2 Day 3 Day 4 Day 5 Day 620
60
100
140
Gene e
xpre
ssio
n level
The Temporal Differences Will
2-1 3-2 4-3 5-4 6-5
-80
-40
0
40
80
Change in e
xp.
level
2-1 3-2 4-3 5-4 6-5
-80
-40
0
40
80
Change in e
xp.
level
Many Other Different Techniques
• Hierachical Clustering• Self-Organising Maps
• ML-Group– Generative Topographic Mappings (GTM)
GTM
• Data lies in high dimensional space (>2).
• Model it with a lower embedded dimensionality (2).
• MATLAB Demo of embedded dimensions.
GTM on Gene Data
• MATLAB Demo.
Conclusions
• Take Temporal differences of Profiles.
• Attempt to Cluster.
• Test Hypothesis that clustered Genes are functionally related.
• Good luck in the Exam!