Training and test samples are reported in table 2. We constrained numerosities

Training and test samples are reported in table 2. We constrained numerosities in order for the test sample to be equally balanced between positive and negative pCR, and for the training sample to respect proportions of the original data set. The adopted method for the estimation of the smoothed ROC curve is LLoyd and Yong’s one [22], which is proved to perform better than the empirical estimation. They proposed to estimate this curve from kernel smoothing of the distribution functions of the diagnostic measurement underlying the binary decision rule, i.e. the conditional posterior probabilities of positive pCR, and showed the significant accuracy achieved by this method for realistic sample size compared with the empirical estimation. As mentioned above, the tests we performed were done on a sample of 22 patients, for which we had previously measured their pCR, and are based on the posterior probabilities of the clinical outcome being 1, P(ut 1Dwb(g)t ,ygt ) , obtained running the Gibbs Sampler for 30.000 iterations. We performed the same analysis using marginally the two platforms and obtaining respectively posterior probabilities P(ut 1Dwb(g)t ) and P(ut 1Dygt ) . These posterior probabilities, obtained through the joint and marginal models, are showed in Figure 4. The ROC curves are compared in Figure 5 and such comparison confirms our choice of borrowing information between the two Pleuromutilin Genomic platforms, since the ROC curve corresponding to the integrated model has by far the highest Area Under the Curve, slightly below 0.9. We finally tried and compared our method with a simple logistic regression with LASSO variable selection (LLR) [23] [24], whose corresponding ROC curves are plotted in Figure 1662274 6. We performed the analysis using the package glmnet in R, and set the elastic net mixing parameter a to 1. The penalty is defined as (1{a) DDbDD2 zaDDbDD1 2 2 and a 1 correponds to the Lasso penalty, which in this case gave the best prediction performances. We therefore plotted in Figure 7 the smoothed ROC curves based on posterior probabilities of pCR obtained through the integrated model and on predictive probabilities obtained through LLR using only copy number variation data. The AUC under the curve obtained through our integrated model shows to be much higher that the one under the curve obtained through LLR.Bayesian Models and Integration Genomic PlatformsDiscussionWe have introduced a Bayesian hierarchical model to GHRH (1-29) site integrate two types of genomics data, copy number and RNA expression. The proposed model can be easily extended to multiple platforms, with modification to the modeling of latent probit scores. Since the entire statistical inference is based on a coherent probability model, scientific questions can be addressed with probability statements, allowing for reporting uncertainty measures such as FDR. This is the main advantage of the proposed models over existing ones. In table 3 we reported the list of genes which show jointly over expression and copy number amplification in TN patients, which was of great interest for clinicians and was also the list associated with the lowest FDR levels. Gene MYC appeared in the list and the result is promising since MYC is a key regulator of cell growth, proliferation, metabolism, differentiation, and apoptosis and MYC deregulation contributes to breast cancer development and progression and is associated with poor outcomes. Multiple mechanisms are involved in MYC deregulation in breast c.Training and test samples are reported in table 2. We constrained numerosities in order for the test sample to be equally balanced between positive and negative pCR, and for the training sample to respect proportions of the original data set. The adopted method for the estimation of the smoothed ROC curve is LLoyd and Yong’s one [22], which is proved to perform better than the empirical estimation. They proposed to estimate this curve from kernel smoothing of the distribution functions of the diagnostic measurement underlying the binary decision rule, i.e. the conditional posterior probabilities of positive pCR, and showed the significant accuracy achieved by this method for realistic sample size compared with the empirical estimation. As mentioned above, the tests we performed were done on a sample of 22 patients, for which we had previously measured their pCR, and are based on the posterior probabilities of the clinical outcome being 1, P(ut 1Dwb(g)t ,ygt ) , obtained running the Gibbs Sampler for 30.000 iterations. We performed the same analysis using marginally the two platforms and obtaining respectively posterior probabilities P(ut 1Dwb(g)t ) and P(ut 1Dygt ) . These posterior probabilities, obtained through the joint and marginal models, are showed in Figure 4. The ROC curves are compared in Figure 5 and such comparison confirms our choice of borrowing information between the two genomic platforms, since the ROC curve corresponding to the integrated model has by far the highest Area Under the Curve, slightly below 0.9. We finally tried and compared our method with a simple logistic regression with LASSO variable selection (LLR) [23] [24], whose corresponding ROC curves are plotted in Figure 1662274 6. We performed the analysis using the package glmnet in R, and set the elastic net mixing parameter a to 1. The penalty is defined as (1{a) DDbDD2 zaDDbDD1 2 2 and a 1 correponds to the Lasso penalty, which in this case gave the best prediction performances. We therefore plotted in Figure 7 the smoothed ROC curves based on posterior probabilities of pCR obtained through the integrated model and on predictive probabilities obtained through LLR using only copy number variation data. The AUC under the curve obtained through our integrated model shows to be much higher that the one under the curve obtained through LLR.Bayesian Models and Integration Genomic PlatformsDiscussionWe have introduced a Bayesian hierarchical model to integrate two types of genomics data, copy number and RNA expression. The proposed model can be easily extended to multiple platforms, with modification to the modeling of latent probit scores. Since the entire statistical inference is based on a coherent probability model, scientific questions can be addressed with probability statements, allowing for reporting uncertainty measures such as FDR. This is the main advantage of the proposed models over existing ones. In table 3 we reported the list of genes which show jointly over expression and copy number amplification in TN patients, which was of great interest for clinicians and was also the list associated with the lowest FDR levels. Gene MYC appeared in the list and the result is promising since MYC is a key regulator of cell growth, proliferation, metabolism, differentiation, and apoptosis and MYC deregulation contributes to breast cancer development and progression and is associated with poor outcomes. Multiple mechanisms are involved in MYC deregulation in breast c.