Transcript Lecture Slides in PowerPoint

Affymetrix GeneChips
and
Analysis Methods
Neil Lawrence
Schedule
18th April
25th April
2nd May
9th May
16th May
23rd May
Introduction and Background
cDNA Mircoarrays
No Lecture
and some of this
Affymetrix GeneChips
Guest Lecturer – Dr Pen Rashbass
Analysis methods
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 subsequences 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 is
IPerfect 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
Scaling factors are thus :
are calculated solving
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
120
80
40
0
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Take Temporal Differences
Change in exp. level
Gene expression level
Original Temporal Profile
80
40
0 2-1
-40
-80
3-2
4-3
5-4
6-5
Consider Clustering via MSE
Gene expression level
Gene expression level
These two similar profiles won’t cluster
120
80
40
0
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
140
100
60
20
Day 1
Day 2
Day 3
Day 4
Day 5
Day 6
Change in exp. level
Change in exp. level
The Temporal Differences Will
80
40
0 2-1
3-2
4-3
5-4
6-5
3-2
4-3
5-4
6-5
-40
-80
80
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0 2-1
-40
-80
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!