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Molecular Classification of Cancer. Christopher Davis Mark Fleharty. Introduction. Clinical applications of computational molecular biology Class prediction Class discovery. Topics of Discussion. Acute Leukemia AML ALL DNA Microarrays Data mining methods. Acute Leukemia.

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molecular classification of cancer

Molecular Classification of Cancer

Christopher Davis

Mark Fleharty

Class discovery and class prediction by gene expression

introduction
Introduction
  • Clinical applications of computational molecular biology
  • Class prediction
  • Class discovery

Class discovery and class prediction by gene expression

topics of discussion
Topics of Discussion
  • Acute Leukemia
    • AML
    • ALL
  • DNA Microarrays
  • Data mining methods

Class discovery and class prediction by gene expression

acute leukemia
Acute Leukemia
  • Different types
    • Acute myeloid leukemia (AML)
    • Acute lymphoblastic leukemia (ALL)
  • Importance of correct diagnosis
    • Maximize efficacy
    • Minimize toxicity
  • Morphological vs. Molecular characteristics

Class discovery and class prediction by gene expression

dna microarrays
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

Class discovery and class prediction by gene expression

gene expression example
Gene Expression Example
  • mRNA’s are indicator
  • Yeast – Wine
    • Anaerobic
    • Alcohol
  • Yeast – Bread
    • Aerobic
    • CO2

Class discovery and class prediction by gene expression

data mining
Data Mining
  • Correlation Weighting Methods
  • Self Organizing Maps
  • K-means
  • PCA (Principle Component Analysis)

Class discovery and class prediction by gene expression

correlated weighting methods
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

Class discovery and class prediction by gene expression

pearson s r correlation
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

Class discovery and class prediction by gene expression

example r 8
Example r = .8

Class discovery and class prediction by gene expression

idealized aml all gene
Idealized AML/ALL Gene

Class discovery and class prediction by gene expression

high correlation with idealized gene
High Correlation With Idealized Gene

Class discovery and class prediction by gene expression

allow genes to vote
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

Class discovery and class prediction by gene expression

self organizing maps
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

Class discovery and class prediction by gene expression

self organizing maps continued
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

Class discovery and class prediction by gene expression

use som to discover classes
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

Class discovery and class prediction by gene expression

k means
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

Class discovery and class prediction by gene expression

principle components analysis
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

Class discovery and class prediction by gene expression

what this means
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

Class discovery and class prediction by gene expression

future work
Future Work
  • Algorithm research
  • How do we gear experiments to maximize the amount of information we get?

Class discovery and class prediction by gene expression