1. Class Prediction and Discovery Using Gene Expression Data Donna K. Slonim, Pablo Tamayo, Jill P. Mesirov, Todd R. Golub, Eric S. Lander 발표자 : 이인희

2. Introduction Gene expression: process of transcribing DNA sequence into RNA for protein production Gene expression level: approximate number of copies of RNA in a cell  Correlate with amount of the corresponding protein made.  May provide the additional information for improving cancer classification and diagnosis Class discovery: dividing samples into groups with similar behavior or properties Class prediction: given a set of known classes, determine the correct class for a new patient

3. Definitions Data Class vector: datan samples m genes Gene expression vector

4. Method for Choosing Correlated Genes Metric for gene selection  Predictive gene’s typical expression in one class must be quite different from its typical expression in the other.  Variation of expression in one class must be as little as possible.  Correlation metric:

5. Neighborhood analysis  Whether there are any genes likely to be predictors of given class distinction  Determine if the neighborhood around c holds more gene expression vectors than we’d expect to see by chance(around random permutation of c).

7. Choosing a prediction set S  Could simply choose the top k genes by the absolute value of P(g, c).  Choose the top k 1 genes(highly expressed in class 1) and the bottom k 2 genes(highly expressed in class 2). Optimal size of the prediction set  Tradeoff between additional information and robustness and amount of additional noise.  Variant |S| with constraint that k 1 and k 2 are roughly equal. This prediction method is not highly sensitive to the exact number of genes used.

8. Prediction by Weighted Voting Each gene casts a weighted vote  V=weight(g) * distance(x, b)  g: each gene in S, x: new sample in test set.  b: ‘decision boundary’  weight(g)=P(g,c) Tradeoff of reliability vs. utility  PS(‘prediction strength’)  In this paper, PS threshold was 0.3. That is,  Error rate, ‘no call’ rate

9. Application: Classifying Patient Samples Training set: 38 leukemia samples(11 AML, 28 ALL) Test set: 34 samples(14 AML, 20 ALL) ALL/AML distiction

10. About 700 genes above the 1% level in each direction Arbitrarily chose to use 50-gene predictor  36 correct prediction out of 38 training samples.  29 correct prediction out of 34 test samples.

11. Application: Verifying Proposed Classes One needs to show that the class distinctions discovered are real and biologically interesting. Validate the clusters by testing predictability.  If clusters reflect true structure, the distinction should be predictable in additional samples  Examine prediction strengths in cross-validation  Test if distribution of prediction strengths for given class distinction is significantly higher than we’d expect for a random class distinction.

12. Discussion and Conclusion Use similar methods to predict any trait of characteristic at the transcriptional level. Future work  When no one biological pathway is responsible for all the cases in either class.  When there is multiple classes.