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Bayesian Multi-topic Microarray Analysis with Hyperparameter Reestimation

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Tomonari Masada
Tomonari Masada
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Tomonari MASADA ( 正田备也 ) NAGASAKI University ( 长崎大学 ) [email_address]
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Problem ,[object Object],[object Object],[object Object]
Gene expression http://bix.ucsd.edu/bioalgorithms/slides.php
DNA microarray experiment ,[object Object]
L atent P rocess D ecomposition latent Dirichlet allocation ( LDA ) [Blei et al. 01] latent process decomposition ( LPD ) [Rogers et al. 05] text mining microarray analysis document sample word gene word frequency gene expression level latent topic latent process
LPD as a multi-topic model ,[object Object]
LPD as a generative model ,[object Object],[object Object],[object Object],[object Object],[object Object]
Inference by VB [Rogers et al. 05] ,[object Object],[object Object],[object Object],[object Object]
Variational lower bound
Inference by MVB [Ying et al. 08] ,[object Object],[object Object],[object Object],[object Object]
Marginalization in MVB
Our proposal: MVB+ ,[object Object],[object Object],[object Object],[object Object]
Update formulas in MVB+ Inversion of digamma function is required.
Hyperparameter reestimation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Experiments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data specifications Dataset name (abbreviation) # of samples # of genes Leukemia ( LK ) 72 12582 Five types of breast cancer ( D1 ) 286 17816 Three types of bladder cancer ( D2 ) 40 3036 Healthy tissues ( D3 ) 103 10383
Results ,[object Object],[object Object],[object Object]
LK # of iterations lower bound
D1
D2
D3
LK # of processes lower bound (after convergence)
D1
D2
D3
Sample clustering evaluation (averaged over 100 trials) dataset method precision recall F -score LK MVB+ 0.934 + 0.007 0.931 + 0.010 0.932 + 0.009 MVB 0.930 + 0.000 0.924 + 0.000 0.927 + 0.000 D2 MVB+ 0.837 + 0.038 0.822 + 0.032 0.829 + 0.033 MVB 0.779 + 0.084 0.751 + 0.069 0.763 + 0.071
Qualitative difference ( LK ) ,[object Object],[object Object],MVB+ MVB
Qualitative difference ( D2 ) ,[object Object],[object Object],MVB+ MVB
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]
Future work ,[object Object],[object Object],[object Object],[object Object],[object Object]

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Bayesian Multi-topic Microarray Analysis with Hyperparameter Reestimation

  • 1. Tomonari MASADA ( 正田备也 ) NAGASAKI University ( 长崎大学 ) [email_address]
  • 2.
  • 3.
  • 5.
  • 6.
  • 7.
  • 8. L atent P rocess D ecomposition latent Dirichlet allocation ( LDA ) [Blei et al. 01] latent process decomposition ( LPD ) [Rogers et al. 05] text mining microarray analysis document sample word gene word frequency gene expression level latent topic latent process
  • 9.
  • 10.
  • 11.
  • 13.
  • 15.
  • 16.
  • 17. Update formulas in MVB+ Inversion of digamma function is required.
  • 18.
  • 19.
  • 20. Data specifications Dataset name (abbreviation) # of samples # of genes Leukemia ( LK ) 72 12582 Five types of breast cancer ( D1 ) 286 17816 Three types of bladder cancer ( D2 ) 40 3036 Healthy tissues ( D3 ) 103 10383
  • 21.
  • 22. LK # of iterations lower bound
  • 23. D1
  • 24. D2
  • 25. D3
  • 26. LK # of processes lower bound (after convergence)
  • 27. D1
  • 28. D2
  • 29. D3
  • 30. Sample clustering evaluation (averaged over 100 trials) dataset method precision recall F -score LK MVB+ 0.934 + 0.007 0.931 + 0.010 0.932 + 0.009 MVB 0.930 + 0.000 0.924 + 0.000 0.927 + 0.000 D2 MVB+ 0.837 + 0.038 0.822 + 0.032 0.829 + 0.033 MVB 0.779 + 0.084 0.751 + 0.069 0.763 + 0.071
  • 31.
  • 32.
  • 33.
  • 34.