Transcriptional Diagnosis by Bayesian Network

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Transcriptional Diagnosis by Bayesian Network

• Hsun-Hsien Chang and Marco F. Ramoni

Childrens 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

• Bayesian networks are directed acyclic graphs
  where
• Node corresponds to random variables.
• Directed arcs encode conditional probabilities of
  the target nodes on the source nodes.

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Gene Selection by Bayes Factor
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Collinearity Elimination via Network Learning
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Sample Classification

• 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

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Algorithm Summary
Gene Selection by Bayes Factor
Collinearity Elimination
Sample Classification
(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.

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Comparisons with Other Popular Methods

• Higher classification accuracy.
• Small-sized signature to avoid overfitting.

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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.