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Harvard Medical School
Transcriptional Diagnosis by
Bayesian Network
Hsun-Hsien Chang and Marco F. Ramoni
Children’s Hospital Informatics Program
Harvard-MIT Division of Health Sciences and Technology
Harvard Medical School
March 17, 2009
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Background
• Microarray technology enables profiling expression of
thousands of genes in parallel on a single chip.
• Comparative analysis of gene expression across tissue
states extracts signature genes for disease diagnosis.
• Challenge:
– Number of variables (i.e., genes) is much greater than the
number observations (i.e., biological samples), inducing the
problem of overfitting.
• Existing methods:
– Gene selection: compute statistics (eg., t-statistics, SNR,
PCA) of individual genes and select high rank genes.
– Classification model: create a classification function of
selected genes.
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Proposed Approach
• Issues:
– Assumption on gene independencies is inadequate.
– Other genes may be collinearly expressed with the signature.
– Selection and classification are two non-integrated steps.
Need a cut-off threshold to select high rank genes.
• Proposed strategies:
– Adopt system biology approach to infer the functional
dependence among genes.
– Use the dependence network for tissue discrimination.
– Integrate gene selection and classification model in
Bayesian network framework.
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Data Representation by Bayesian Network
Tissue state 1
Tissue state 2
Pheno
Gene 1
Gene 2
.
.
.
.
• Bayesian networks are directed acyclic graphs where:
– Node corresponds to random variables.
G
– Directed arcs encode conditional probabilities
G of the target
nodes on the source nodes.
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Gene N
GN
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Gene Selection by Bayes Factor
Pheno
G1
G1
G2
gene selection by
Bayes factor
G2
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.
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Pheno
Gp
Gq
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GN
GN
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Collinearity Elimination via Network Learning
Pheno
G1
G1
G2
G2
Gp
collinearity
elimination
Pheno
Gp
Gq
Gq
GN
GN
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Sample Classification
G1
G2
Pheno
Gp
Gq
• The phenotype variable is
independent of the blue genes, given
the green genes.
• Technically, the green genes are
under the Markov blanket of the
phenotype variable, and they are the
signature genes used for phenotype
determination.
• Tissue classification:
GN
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Algorithm Summary
..
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..
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Gene Selection
by Bayes Factor
Collinearity
Elimination
..
.
Sample
Classification
..
.
..
.
Optimize
Performance
Optimize
Hyperparameters
(sensitivity analysis)
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Discriminate Lung Carcinoma Subtypes
• Adenocarcinoma (AC) and squamous cell carcinoma
(SCC) are major subtypes of lung cancer:
– AC and SCC are distinct in survival, chances of metastasis,
and responses to chemotherapy and targeted therapy.
– Physicians lack confidence in correct recognition when there
are multiple primary carcinomas.
• Training:
– 58 ACs and 53 SCCs.
– 77 genes selected in the network.
– 25 signature genes.
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Bayesian Network for Lung Carcinoma
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Large-Scale Testing on Independent Samples
• 422 samples (232 ACs and 190 SCCs) aggregated from 7
cohorts (including Caucasians, African-Americans, Chinese).
• Accuracy = 95.2% AUROC.
ROC curves
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0.9
0.8
0.7
sensitivity
0.6
0.5
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0.3
0.2
0.1
Proposed Bayes Net (95.2%)
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0
0.1
0.2
0.3
0.4
0.5
0.6
1-specificity
0.7
0.8
0.9
1
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Comparisons with Other Popular Methods
• Higher classification accuracy.
• Small-sized signature to avoid overfitting.
Bayesian Network
PCA/LDA
PAM
(Tibshirani et al., PNAS 2002)
Weighted Voting
(Golub et al., Science 1999)
Testing
AUROC
95.2%
---
# signature
genes
25
91.2%
0.0047
13
91.0%
0.0014
77
93.4%
0.6240
800
p-value
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KRT6 Family Characterizes the Lung
Carcinoma Discrimination
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KRT6 Family Characterizes the Lung
Carcinoma Discrimination
• Keratin-6 family genes (KRT6A, KRT6B, KRT6C) are
important for distinguishing lung cancer subtypes.
– Accounting for 95% of
the accuracy of the whole
25-gene signature.
– Located on chromosome
12q12-q13.
– A nonlinear, concave
discriminative surface.
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Verification by Chr12q12-q13 Aberrations
• Investigate DNA copy number changes in comparative
genomic hybridization (CGH) array.
– 12 ACs and 13 SCCs from
Vrije University Medical
Center, Netherland.
– A dumbbell discriminative
surface achieves 80%
classification accuracy.
– Treat average CGH values
of genes occupying q12,
q13, and q12-13
respectively as three
features to construct a
Naïve Bayes Classifier.
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Conclusion
• Reverse engineer regulatory network
information for tissue classification.
• Adopt the system biology approach to infer
gene dependencies network.
– Select genes by Bayes factor.
– Eliminate collinearity via network learning.
– Integrate gene selection and classification model
in a single Bayesian network framework.
• Demonstrate the promising translational
value of the system biology approach in
clinical study.
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