Download genes - Computational Diagnostics Group

Sample classification
using Microarray Data
We have two sample
entities
A
• malignant vs. benign tumor
• patient responding to drug
vs. patient resistant to drug
• etc...
B
From a tissue
database we get
biopsies for both
entities
A
B
We do expression profiling ...
DNA Chip
Tissue
Expression
profile
... and the situation
looks like this:
A
What about
differences in
the profiles?
Do they exist?
What do they
mean?
B
Which characteristics
does this data have?
This is a statistical question ...
... and the answer is coming from biology.
The data describes a huge and
complex interactive network
?
... that we do not know.
Expression profiles describe states
of this network
?
Diseases can (should) be understood
(defined) as characteristic states of
this network
?
This data is:
• Very high dimensional
• Consists of highly dependent variables
(genes)
Back to the statistical
classification problem:
A
What is the problem
with high dimensional
dependent data?
B
A two gene scenario, where everything works
out fine:
A new patient
A
A
B
And here things go wrong:
Problem 1:
No separating line
Problem 2:
To many separating lines
And in 30000 dimensional
spaces ...
...
1
2
3
30000
• Problem 1 never exists!
• Problem 2 exists almost always!
Spent a minute thinking about this in
three dimensions
Ok, there are three genes, two
patients with known diagnosis, one
patient of unknown diagnosis, and
separating planes instead of lines
OK! If all points fall onto one line it does not always
work. However, for measured values this is very
unlikely and never happens in praxis.
In summary:
There is always a linear
signature separating the
entities ... a biological reason
for this is not needed.
Hence, if you find a
separating signature, it does
not mean (yet) that you have
a nice publication ...
... in most cases it means
nothing.
In general a separating
linear signature is of no
significance at all.
Take it easy!
There are meaningful
differences in gene
expression ... we
have build our
science on them ...
and they should be
reflected in the
profiles.
There are good signatures and bad
ones!
Strategies for finding good signatures ...
... statistical learning:
1. Gene Selection
2. Factor-Regression
3. Support Vector Machines (machine learning)
4. Informative Priors (Bayesian Statistics)
5. .....
What are the ideas?
Gene selection
Why does it help?
When considering all possible linear planes for separating the
patient groups, we always find one that perfectly fits, without a
biological reason for this.
When considering only planes that depend on maximally 20 genes
it is not guaranteed that we find a well fitting signature. If in spite
of this it does exist, chances are better that it reflects
transcriptional disorder.
If we require in addition that the genes are all good classifiers
them selves, i.e. we find them by screening using the t-Score,
finding a separating signature is even more exceptional.
Gene selection
What is it?
Choose a small number of genes … say 20 … and
then fit a model using only these genes.
How to pick genes:
-Screening ( i.e. t-score)
single gene association
-Wrapping
multiple gene association
1 . choose the best classifying single gene g1
2. choose the optimal complementing gene g2
g1 and g2 are optimal for classification
3. etc ….
How many
genes
- All linear
planes
Tradeoffs
High probability for
finding a fitting
signature
Low probability that
a signature is
meaningful
- All linear
planes
depending on at
most 20 genes
- All linear
planes
depending on a
given set of 20
genes
How you
find them
-Wrapping:
multiple
gene
association
Low probability for
finding a fitting
signature
High probability that
a signature is
meaningful
-Screening:
Single gene
association
More Generally
High probability for
finding a fitting
signature
Low probability that
a signature is
meaningful
Flexible model
Smooth model
Low probability for
finding a fitting
signature
High probability that
a signature is
meaningful
Factor-Regression
P [ Yi
1| 
]   (0  
βi xi
)
For example PCA, SVD ...
Use the first n (3-4) Factors only
... does not work well with expression data, at least not
with a small number of samples
Support Vector Machines
Fat planes: With an infinitely thin plane the data can
always be separated correctly, but not necessarily
with a fat one.
Again if a large margin separation exists, chances are
good that we found something relevant.
Large Margin Classifiers
Informativer Priors
P [ Yi
1| 
Likelihood
]   (0  
Prior
βi xi
Posterior
)
First:Singular Value
Decomposition
Loadings
Singular values
E  A 
DF
X
Data
Expression levels of super
genes, orthogonal matrix
Keep all super-genes:
The Prior Needs to Be
designed in n Dimensions
•
•
•
•
•
n number of samples
Shape?
Center?
Orientation?
Not to narrow ... not to wide
Shape
multidimensional
normal
for simplicity
Center
   i  P [ Yi  1 |  ]
Assumptions on the model correspond
to assumptions on the diagnosis
Orientation
orthogonal super-genes !
Not to Narrow ... Not to
Wide
Auto adjusting model
Scales are hyper
parameters with their
own priors
What are the
additional assumptions
that came in by the prior?
• The model can not be dominated by only a
few super-genes ( genes! )
• The diagnosis is done based on global
changes in the expression profiles
influenced by many genes
• The assumptions are neutral with respect
to the individual diagnosis
A common idea behind all models ...
All models confine the set of possible signatures a priori;
however, they do it in different ways.
Gene selection aims for few genes in the signature
SVM go for large margins between data points and the
separating hyper-plane.
PC-Regression confine the signature to 3-4 independent
factors
The Bayesian model prefers models with small weights
(a la ridge-regression or weight decay)
... and a common problem
of all models:
The bias variance trade off
Model Complexity:
- max number of genes
- minimal margin
- width of prior
-etc
How come?
Population mean:
Genes have a certain mean
expression and correlation in the
population
Sample mean:
We observe average expression and
empirical correlation
Fitted model:
Regularization
How much
regularization
do we need?
- The Bayesian answer: What you do
not know is a random variable ...
regularization becomes part of the
model
- Or model selection by evaluations ...
Model Selection with separate
data
100
50
50
Training
Selection
Test
Split of some samples for Model Selection
Train the model on the training data with different
choices for the regularization parameter
Apply it to the selection data and optimize this
parameter (Model Selection)
Test how good you are doing on the test data
(Model Assessment)
10 Fold Cross-Validation
...
Train Train Select Train Train
Train Train Train Select Train
...
Chop up the training data (don‘t touch the test data) into 10
sets
Train on 9 of them and predict the other
Iterate, leave every set out once
Select a model according to the prediction error (deviance)
Leave one out Cross-Validation
...
1
Train Train Select Train Train
Train Train Train Select Train
1
...
Essentially the same
But you only leave one sample out at a time and predict it using
the others
Good for small training sets
Model Assessment
How well did I do?
Can I use my signature for clinical
diagnosis?
How well will it perform?
How does it compare to traditional
methods?
The most important thing:
Don‘t fool yourself! (... and others)
This guy (and others)
thought for some time he
could predict the nodal
status of a breast tumor
from a profile taken from
the primary tumor!
... there are significant differences.
But not good enough for prediction
(West et al PNAS 2001)
DOs AND DONTs :
1. Decide on your diagnosis model (PAM,SVM,etc...) and don‘t
change your mind later on
2. Split your profiles randomly into a training set and a test set
3. Put the data in the test set away.
4. Train your model only using the data in the training set
(select genes, define centroids, calculate normal vectors for
large margin separators,perform model selection ...)
don‘t even think of touching the test data at this time
5. Apply the model to the test data ...
don‘t even think of changing the model at this time
6. Do steps 1-5 only once and accept the result ...
don‘t even think of optimizing this procedure
The selection bias
- You can not select 20 genes using all your
data and then with this 20 genes split test and
training data and evaluate your method.
-There is a difference between a model that
restricts signatures to depend on only 20 genes
and a data set that only contains 20 genes
-Your model assessment will look much better
than it should