1. Molecular Classification of Cancer
Christopher Davis
Mark Fleharty
9/18/2018
Class discovery and class prediction by gene expression

2. Class discovery and class prediction by gene expression
Introduction
Clinical applications of computational molecular biology
Class prediction
Class discovery
9/18/2018
Class discovery and class prediction by gene expression

3. Class discovery and class prediction by gene expression
Topics of Discussion
Acute Leukemia
AML
ALL
DNA Microarrays
Data mining methods
9/18/2018
Class discovery and class prediction by gene expression

4. Class discovery and class prediction by gene expression
Acute Leukemia
Different types
Acute myeloid leukemia (AML)
Acute lymphoblastic leukemia (ALL)
Importance of correct diagnosis
Maximize efficacy
Minimize toxicity
Morphological vs. Molecular characteristics
9/18/2018
Class discovery and class prediction by gene expression

5. Class discovery and class prediction by gene expression
DNA Microarrays
Hybridization of mRNA’s onto chips with complementary strands of DNA
What they tell us
How much is a gene expressed
When are genes expressed
Where are genes expressed
Under what conditions are they expressed
9/18/2018
Class discovery and class prediction by gene expression

6. Gene Expression Example
mRNA’s are indicator
Yeast – Wine
Anaerobic
Alcohol
Yeast – Bread
Aerobic
CO2
9/18/2018
Class discovery and class prediction by gene expression

7. Class discovery and class prediction by gene expression
Data Mining
Correlation Weighting Methods
Self Organizing Maps
K-means
PCA (Principle Component Analysis)
9/18/2018
Class discovery and class prediction by gene expression

8. Correlated Weighting Methods
The magnitude of each vote is dependant on the expression level in the new sample and the correlation with the class distinction
9/18/2018
Class discovery and class prediction by gene expression

9. Pearson’s “r” Correlation
Continuous interval between –1 and 1
+1 if 2 genes are correlated perfectly
-1 if 2 genes are correlated negatively
0 if there is no correlation
9/18/2018
Class discovery and class prediction by gene expression

10. Class discovery and class prediction by gene expression
Example r = .8
9/18/2018
Class discovery and class prediction by gene expression

11. Idealized AML/ALL Gene
9/18/2018
Class discovery and class prediction by gene expression

12. High Correlation With Idealized Gene
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Class discovery and class prediction by gene expression

13. Class discovery and class prediction by gene expression
Allow genes to “vote”
Sort strongest correlated genes (This list is often informative)
Genes cast weighted votes based on their correlation with the idealized gene and how much they are expressed in the patient
Votes are summed and based on a predetermined threshold the patient is classified as having AML/ALL/Inconclusive
Prediction Strength
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Class discovery and class prediction by gene expression

14. Class discovery and class prediction by gene expression
Self Organizing Maps
Method for unsupervised learning – reduces high dimensional data to low dimensional data
Based on a grid of artificial neurons
Each grid location has a weight vector
9/18/2018
Class discovery and class prediction by gene expression

15. Self Organizing Maps Continued
The node with a weight vector closest to input vector is chosen and it’s weights adjusted closer to the input vector
This node’s neighbors are also adjusted to be closer to the input vector according to some decay function
Process all vectors and repeat until stable
9/18/2018
Class discovery and class prediction by gene expression

16. Use SOM to discover classes
SOM is used to find the class members to train the predictors
Predictors are tested on a new set of known classification
If the cross validation is positive and the prediction strength good the cluster discovery and prediction are considered good
Iterate if you want to find finer classes
9/18/2018
Class discovery and class prediction by gene expression

17. Class discovery and class prediction by gene expression
K-Means
Dataset is partitioned into K clusters randomly
For each data point calculate the distance from the point to the cluster – if it is closer to it’s current cluster leave it there, otherwise move it to the closest cluster
Repeat until stable
9/18/2018
Class discovery and class prediction by gene expression

18. Principle Components Analysis
A transform that chooses a new coordinate system for the data set s.t. the greatest variance comes to lie on the first axis(principle component), the 2nd greatest variance on the 2nd axis, etc.
Can be used to reduce dimensionality by eliminating later principle components
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Class discovery and class prediction by gene expression

19. Class discovery and class prediction by gene expression
What This Means
Diagnostic Tools
Use in diagnosis of other diseases
Look for toxins in environment
Decoding regulatory networks
Use of time sensitive data
Use of stress data
Drug discovery
New classifications of disease
9/18/2018
Class discovery and class prediction by gene expression

20. Class discovery and class prediction by gene expression
Future Work
Algorithm research
How do we gear experiments to maximize the amount of information we get?
9/18/2018
Class discovery and class prediction by gene expression