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Gene Extrapolation Models for Toxicogenomic Data

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Nacho Caballero
Nacho Caballero
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gene EXTRAPOLATION models for TOXICOGENOMIC data daniel gusenleitner nacho caballero
Testing for carcinogenicity is costly
Genes show clustered responses Expression correlates between platforms
We want to extrapolate the expression of regular genes 11000 Genes 2K Arrays 10K 1K Regular Landmark Genes Genes
We fit a linear model to each 2K Arrays 1K regular gene X Landmark Genes Predicted Expression = Xβ Expression Gene 1 = X1β1 +X2β2 +…+X2Kβ2K Expression Gene 2 = X1β1 +X2β2 +…+X2Kβ2K … Expression Gene 10K = X1β1 +X2β2 +…+X2Kβ2K
Elastic Net mean error number of variables glmnet: Lasso and elastic- net regularized generalized linear models http://cran.r-project.org/web/ packages/glmnet/index.html
Neural Networks regular genes hidden layer nnet: Feed-forward Neural Networks and Multinomial Log- Linear Models landmark http://cran.r-project.org/ genes web/packages/nnet/index.html
mean fluorescent intensity signal ratio Intensity variation -to- noise intensity standard deviation SNR = extrapolation mean error
Building 10451 models takes a long time… runtime single total per CPU runtime model runtime linear 120 x 3 h 2 min 360 h regression elastic 120 x 16 h 11 min 1920 h net neural 50 x 0.75 7800 h 45 min network h ?
Signal-to-Noise Comparison E-net LM NN ENSRNOG00000013133 135.58 19.62 13.21 ENSRNOG00000011861 209.82 28.82 12.08 ENSRNOG00000033466 190.81 26.58 11.86 ENSRNOG00000036816 197.82 23.09 9.93 ENSRNOG00000003515 273.62 29.35 8.68 ENSRNOG00000002254 53.43 8.83 7.21 ENSRNOG00000031266 76.19 8.19 6.70 ENSRNOG00000005963 145.06 6.99 6.49 ENSRNOG00000008613 38.86 3.97 6.07 ENSRNOG00000023095 13.57 2.70 5.98 ENSRNOG00000020947 17.27 2.41 5.04 ENSRNOG00000007258 103.77 13.71 4.91 ENSRNOG00000019813 16.53 3.01 4.68 ENSRNOG00000014232 61.69 9.17 4.05 ENSRNOG00000002454 50.71 5.58 3.80 ENSRNOG00000018201 5.04 1.64 3.39
The elastic net outperforms standard linear regression Signal-to-noise ratio Elastic Linear Net Regression
Additional feature selection Performance of extrapolation models on carcinogenicity classifiers Correlation between Luminex and Affymetrix chips

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Gene Extrapolation Models for Toxicogenomic Data

  • 1. gene EXTRAPOLATION models for TOXICOGENOMIC data daniel gusenleitner nacho caballero
  • 3. Genes show clustered responses Expression correlates between platforms
  • 4. We want to extrapolate the expression of regular genes 11000 Genes 2K Arrays 10K 1K Regular Landmark Genes Genes
  • 5. We fit a linear model to each 2K Arrays 1K regular gene X Landmark Genes Predicted Expression = Xβ Expression Gene 1 = X1β1 +X2β2 +…+X2Kβ2K Expression Gene 2 = X1β1 +X2β2 +…+X2Kβ2K … Expression Gene 10K = X1β1 +X2β2 +…+X2Kβ2K
  • 6. Elastic Net mean error number of variables glmnet: Lasso and elastic- net regularized generalized linear models http://cran.r-project.org/web/ packages/glmnet/index.html
  • 7. Neural Networks regular genes hidden layer nnet: Feed-forward Neural Networks and Multinomial Log- Linear Models landmark http://cran.r-project.org/ genes web/packages/nnet/index.html
  • 8. mean fluorescent intensity signal ratio Intensity variation -to- noise intensity standard deviation SNR = extrapolation mean error
  • 9. Building 10451 models takes a long time… runtime single total per CPU runtime model runtime linear 120 x 3 h 2 min 360 h regression elastic 120 x 16 h 11 min 1920 h net neural 50 x 0.75 7800 h 45 min network h ?
  • 10. Signal-to-Noise Comparison E-net LM NN ENSRNOG00000013133 135.58 19.62 13.21 ENSRNOG00000011861 209.82 28.82 12.08 ENSRNOG00000033466 190.81 26.58 11.86 ENSRNOG00000036816 197.82 23.09 9.93 ENSRNOG00000003515 273.62 29.35 8.68 ENSRNOG00000002254 53.43 8.83 7.21 ENSRNOG00000031266 76.19 8.19 6.70 ENSRNOG00000005963 145.06 6.99 6.49 ENSRNOG00000008613 38.86 3.97 6.07 ENSRNOG00000023095 13.57 2.70 5.98 ENSRNOG00000020947 17.27 2.41 5.04 ENSRNOG00000007258 103.77 13.71 4.91 ENSRNOG00000019813 16.53 3.01 4.68 ENSRNOG00000014232 61.69 9.17 4.05 ENSRNOG00000002454 50.71 5.58 3.80 ENSRNOG00000018201 5.04 1.64 3.39
  • 11. The elastic net outperforms standard linear regression Signal-to-noise ratio Elastic Linear Net Regression
  • 12. Additional feature selection Performance of extrapolation models on carcinogenicity classifiers Correlation between Luminex and Affymetrix chips