Classification (Supervised Clustering)

1. Classification(Supervised Clustering) Naomi Altman Nov '06

2. Objective Starting from a sample from known groups: 1) Select a set of genes that identify the groups 2) Compute a function of the expression values that can be used to classify a new sample. e.g. Normal and cancer prostate tissues from 24 patients 1) a) Find the set of genes that may be involved in the disease process (differential expression analysis). b) Find a set of genes that mark the disease (possibly not all the genes involved.) 2) Take a sample from a new patient - does this person have prostate cancer?

3. The Main PictureFor Linear Discriminant Analysis separating hyperplane linear discrimination direction To classify a new point, see what side of the hyperplane it lies on

4. The Main PictureFor Support Vector Machines separating hyperplane To classify a new point, see what side of the hyperplane it lies on

5. The Main PictureFor Linear Discriminant Analysis To classify a new point, see what side of the hypercurve it lies on

6. The Main PictureFor Recursive Partitioning To classify a new point, see the classification of its partition in space

7. Linear and Quadratic Discriminant Analysis, Logistic Regression Each sample belongs to A or B. Linear and quadratic discriminant analysis are essentially regressions on a 0/1 indicator variable. Suppose we have samples of sizes m from A and n from B. Sample ts (t=group, s=sample within group) has gene expression values Y1ts ... YGts For each group we can compute the mean expression values for each gene,Ŷt, the variance of each gene, sit2 and the covariance between genes sijt. We can also compute the pooled variance and covariance of each gene, which is essentially the average over the 2 groups.

8. Linear Discriminant Analysis where S is the pooled variance matrix is the linear discriminant function. In the simplest case, we classify each sample depending on whether it is above (A) or below (B) the midpoint of the line, which is If the 2 conditions are not equally likely, we may wish to weight so that we classify new samples proportionally to the expected percentages.

9. Linear Discriminant Analysis This is extended to p groups by considering the discriminant score, which is another SVD decomposition and is similar to multivariate ANOVA. 1. Consider the covariance matrix of the sample means weighted by the sample sizes. between variance between covariance Assemble these into the Between group variance matrix V. 2. Consider the pooled covariance matrix S, (which in this context is often called W for Within group variance matrix).

10. Linear Discriminant Analysis Now consider the SVD of S-1/2BS-1/2. (It is symmetric, so the left and right eigenvectors are the same.) The first eigenvector is the direction of greatest separation of the means, in terms of the axes of the ellipses defining the groups. The 2nd eigenvector is the direction of 2nd greatest separation that is orthogonal to the first. etc. The rank of B is p-1, so there are only p-1 non-zero eigenvalues. Each sample is assigned to the group with nearest mean in the eigenvector coordinates. This is equivalent to looking at the combinations of the pairwise discriminant functions and mapping every sample to the group with the nearest mean.

11. Linear Discriminant Analysis As in the 2-group case, you can weight the discriminant scores by the prior probability of group membership SVD LDA LDA Regions

12. Quadratic Discriminant Analysis Is very similar to linear discriminant analysis, except that every group is allowed to have its own variance matrix, allowing the ellipses to have a different orientation.

13. Logistic Regression Let pt be the probability of membership in group t. Use maximum likelihood to fit log(pt/(1- pt)) = b0 + SbiYits Classify a sample into group t if the predicted log(pt/(1- pt)) is the maximum over all groups. Again, we can weight by prior probability.

14. Recursive Partitioning PL< 2.45 PW< 1.75 se(50 0 0) ve (0 49 5) vi (0 1 45)

15. Assessing Accuracy Count the number of misclassifications of the training sample (optimistic). Cross-validation: Do not use a fraction of the data (test data). "Train" using the remainder of the sample, with the same rule used for the complete data. Count the number of misclassifications of the test data. Repeat. About 1/3 test data appears to be best.

16. But... a) If the number of genes exceeds the number of samples, we always "overfit" - e.g. with logistic regression we can almost always achieve perfect classification b) Rank S=min(row rank, col rank) so S is not invertible (LDA) and neither are the within treatment variance matrices (QDA) b) Most of the methods use all of the genes. i.e. With microarray data, we will need to select a smaller set of genes to work with. For medical diagnostics we often want a very small set of markers.

17. Reducing the Number of Genes 1.With n samples, use the n-k most significantly differentially expressing genes. 2. Cluster the genes and take the most significantly differentially expressing gene in each cluster. 3. Add variables to your discrimination function stepwise. 4. PAM - shrink the group center to the overall center, and then apply a robust QDA with moderated variance estimates (like SAM). The method ends up with the within group centroid=total centroid for most genes. So the differences among groups rely only on the other genes, which are the only genes used in the QDA. Problem: (All methods) Often replicability is lost when studies are repeated. e.g. we can tell the difference between ALL and AML in all studies, but different discriminant functions are required, maybe different genes.