Sample classification using Microarray Data

AB We have two sample entities malignant vs. benign tumor patient responding to drug vs. patient resistant to drug etc...

A B From a tissue database we get biopsies for both entities

Tissue DNA Chip Expression profile We do expression profiling...

A B What about differences in the profiles? Do they exist? What do they mean?... and the situation looks like this:

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)

A B What is the problem with high dimensional dependent data? Back to the statistical classification problem:

A B A new patient A A two gene scenario, where everything works out fine:

Problem 1: No separating line Problem 2: To many separating lines And here things go wrong:

And in dimensional spaces

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 Problem 1 never exists! Problem 2 exists almost always! 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.

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. In general a separating linear signature is of no significance at all. 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) 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 - All linear planes depending on at most 20 genes - All linear planes depending on a given set of 20 genes High probability for finding a fitting signature Low probability for finding a fitting signature High probability that a signature is meaningful Low probability that a signature is meaningful How you find them -Wrapping: multiple gene association -Screening: Single gene association Tradeoffs

Flexible model Smooth model High probability for finding a fitting signature Low probability for finding a fitting signature High probability that a signature is meaningful Low probability that a signature is meaningful More Generally

Factor-Regression 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

LikelihoodPriorPosterior Informativer Priors

First:Singular Value Decomposition Data Loadings Singular values 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 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

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) A common idea behind all models...

... 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 TrainingTest Selection 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 Select Train Select... 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 Train Select Train Select... Essentially the same But you only leave one sample out at a time and predict it using the others Good for small training sets 1 1

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