Data Mining in Genomics: the dawn of personalized medicine

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Data Mining in Genomics the dawn of personalized
medicine

• Gregory Piatetsky-Shapiro
• KDnuggets
• www.KDnuggets.com/gps.html
• Connecticut College, October 15, 2003

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Overview

• Data Mining and Knowledge Discovery
• Genomics and Microarrays
• Microarray Data Mining

3
Trends leading to Data Flood

• More data is generated
• Bank, telecom, other business transactions ...
• Scientific Data astronomy, biology, etc
• Web, text, and e-commerce
• More data is captured
• Storage technology faster and cheaper
• DBMS capable of handling bigger DB

4
Knowledge Discovery Process
Integration
Interpretation Evaluation
Knowledge
Data Mining
Knowledge
RawData
Transformation
Selection Cleaning
Understanding
Transformed Data
Target Data
DATA Ware house
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Major Data Mining Tasks

• Classification predicting an item class
• Clustering finding clusters in data
• Associations e.g. A B C occur frequently
• Visualization to facilitate human discovery
• Summarization describing a group
• Estimation predicting a continuous value
• Deviation Detection finding changes
• Link Analysis finding relationships

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Major Application Areas for Data Mining Solutions

• Advertising
• Bioinformatics
• Customer Relationship Management (CRM)
• Database Marketing
• Fraud Detection
• eCommerce
• Health Care
• Investment/Securities
• Manufacturing, Process Control
• Sports and Entertainment
• Telecommunications
• Web

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Genome, DNA Gene Expression

• An organisms genome is the program for making
  the organism, encoded in DNA
• Human DNA has about 30-35,000 genes
• A gene is a segment of DNA that specifies how to
  make a protein
• Cells are different because of differential gene
  expression
• About 40 of human genes are expressed at one
  time
• Microarray devices measure gene expression

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Molecular Biology Overview
Nucleus
Cell
Chromosome
Gene expression
Gene (DNA)
Gene (mRNA), single strand
Protein
Graphics courtesy of the National Human Genome
Research Institute
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Affymetrix Microarrays
1.28cm
107 oligonucleotides, half Perfectly Match mRNA
(PM), half have one Mismatch (MM) Gene
expression computed from PM and MM
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Affymetrix Microarray Raw Image
Gene Value D26528_at
193 D26561_cds1_at -70 D26561_cds2_at
144 D26561_cds3_at 33 D26579_at
318 D26598_at 1764 D26599_at
1537 D26600_at 1204 D28114_at
707
raw data
Scanner
enlarged section of raw image
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Microarray Potential Applications

• New and better molecular diagnostics
• New molecular targets for therapy
• few new drugs, large pipeline,
• Outcome depends on genetic signature
• best treatment?
• Fundamental Biological Discovery
• finding and refining biological pathways
• Personalized medicine ?!

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Microarray Data Mining Challenges

• Avoiding false positives, due to
• too few records (samples), usually lt 100
• too many columns (genes), usually gt 1,000
• Model needs to be robust in presence of noise
• For reliability need large gene sets for
  diagnostics or drug targets, need small gene sets
• Estimate class probability
• Model needs to be explainable to biologists

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False Positives in Astronomy
cartoon used with permission
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CATs Clementine Application Templates

• CATs - examples of complete data mining processes
• Microarray CAT

Preparation
Multi- Class
Clustering
2-Class
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Key Ideas

• Capture the complete process
• X-validation loop w. feature selection inside
• Randomization to select significant genes
• Internal iterative feature selection loop
• For each class, separate selection of optimal
  gene sets
• Neural nets robust in presence of noise
• Bagging of neural nets

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Microarray Classification
Train data
Feature and Parameter Selection
Data
Model Building
Evaluation
Test data
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Classification External X-val
Gene Data
Train data
Feature and Parameter Selection
T r a i n
Data
Model Building
Evaluation
Test data
FinalTest
Final Model
Final Results
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Measuring false positives with randomization
Rand Class
Gene
Class
178 105 4174 7133
1 1 2 2
2 1 1 2
Randomize 500 times
Gene
Class
Bottom 1 T-value -2.08 Select potentially
interesting genes at 1
178 105 4174 7133
2 1 1 2
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Gene Reduction improves Classification

• most learning algorithms look for non-linear
  combinations of features -- can easily find many
  spurious combinations given small of records
  and large of genes
• Classification accuracy improves if we first
  reduce of genes by a linear method, e.g.
  T-values of mean difference
• Heuristic select equal genes from each class
• Then apply a favorite machine learning algorithm

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Iterative Wrapper approach to selecting the best
gene set

• Test models using 1,2,3, , 10, 20, 30, 40, ...,
  100 top genes with x-validation.
• Heuristic 1 evaluate errors from each class
  select number of genes from each class that
  minimizes error for that class
• For randomized algorithms, average 10
  Cross-validation runs!
• Select gene set with lowest average error

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Clementine stream for subset selection by
x-validation
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Microarrays ALL/AML Example

• Leukemia Acute Lymphoblastic (ALL) vs Acute
  Myeloid (AML), Golub et al, Science, v.286, 1999
• 72 examples (38 train, 34 test), about 7,000
  genes
• well-studied (CAMDA-2000), good test example

ALL
AML
Visually similar, but genetically very different
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Gene subset selection one X-validation
Single Cross-Validation run
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Gene subset selection multiple cross-validation
runs
For ALL/AML data, 10 genes per class had the
lowest error (lt1)
Point in the center is the average error from 10
cross-validation runs Bars indicate 1 st.
dev above and below
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ALL/AML Results on the test data

• Genes selected and model trained on Train set
  ONLY!
• Best Net with 10 top genes per class (20 overall)
  was applied to the test data (34 samples)
• 33 correct predictions (97 accuracy),
• 1 error on sample 66
• Actual Class AML, Net prediction ALL
• other methods consistently misclassify sample 66
  -- misclassified by a pathologist?

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Pediatric Brain Tumour Data

• 92 samples, 5 classes (MED, EPD, JPA, EPD, MGL,
  RHB) from U. of Chicago Childrens Hospital
• Outer cross-validation with gene selection inside
  the loop
• Ranking by absolute T-test value (selects top
  positive and negative genes)
• Select best genes by adjusted error for each
  class
• Bagging of 100 neural nets

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Selecting Best Gene Set

• Minimizing Combined Error for all classes is not
  optimal

Average, high and low error rate for all classes
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Error rates for each class
Error rate
Genes per Class
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Evaluating One Network
Averaged over 100 Networks
Class Error rate
MED 2.1
MGL 17
RHB 24
EPD 9
JPA 19
ALL 8.3
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Bagging 100 Networks
Class Individual Error Rate Bag Error rate Bag Avg Conf
MED 2.1 2 (0) 98
MGL 17 10 83
RHB 24 11 76
EPD 9 0 91
JPA 19 0 81
ALL 8.3 3 (2) 92

• Note suspected error on one sample (labeled as
  MED but consistently classified as RHB)

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AF1q New Marker for Medulloblastoma?

• AF1Q ALL1-fused gene from chromosome 1q
• transmembrane protein
• Related to leukemia (3 PUBMED entries) but not to
  Medulloblastoma

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Future directions for Microarray Analysis

• Algorithms optimized for small samples
• Integration with other data
• biological networks
• medical text
• protein data
• Cost-sensitive classification algorithms
• error cost depends on outcome (dont want to miss
  treatable cancer), treatment side effects, etc.

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Acknowledgements

• Eric Bremer, Childrens Hospital (Chicago)
  Northwestern U.
• Greg Cooper, U. Pittsburgh
• Tom Khabaza, SPSS
• Sridhar Ramaswamy, MIT/Whitehead Institute
• Pablo Tamayo, MIT/Whitehead Institute

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Thank you

• Further resources on Data Mining
  www.KDnuggets.com
• Microarrays
• www.KDnuggets.com/websites/microarray.html
• Contact
• Gregory Piatetsky-Shapiro www.kdnuggets.com/gps.h
  tml