C mean, describes a baseline bwhere xt is the clinical binary covariate mentioned above, while y yw dg and dg trinary indicators accounting respectively for Lixisenatide manufacturer differential gene expression in TN subgroup and interaction 64849-39-4 supplier between the two measurement for gene g , following similar prior w to the one mentioned above for dg . Markov dependence across probes. A Markov dependence is assumed across the probes and it is defined in the following conditional prior on the probe specific effect. Define zw (zw ,:::,zw ): Assuming that the index b is ordered according to 1 BBayesian Models and Integration Genomic PlatformsFigure 3. Posterior probabilities of differential CNA (on the x-axis) and differential expression (y-axis) obtained respectively through the marginal models on CNA data and gene expression data (A). Black dots highlight posterior probabilities of genes which are claimed by the model to show joint differential behaviour (A). Comparison between differences in means of the gene expression data and posteriorBayesian Models and Integration Genomic Platformsprobability of differential expression (B). Comparison between sample correlations and posterior probabilities of positive interaction between platforms (C). doi:10.1371/journal.pone.0068071.glocus proximity on the chromosome, the dependence across adjacent probes is described as follows. Let z1 *N(0,1) and zw Dzb{1 w ,bb{1 *N(bb{1 zw ,t2 ) b b{1 for b [ f2,:::,Bg: In this formulation the parameters b (b1 ,:::,bb{1 ) can be directly interpreted as partial correlation coefficients, defining the strength of dependence between log2 23148522 ratios associated with probes that are adjacent on the chromosome. Priors. The last step is the specification of the priors for the set of parameters that index the sampling model. We assume conditionally conjugate priors. Denoting G(a,b) a gamma distribution with mean ab, we assume n{2 *G(an ,bn ), bb{1, for example. Finally we assume conditionally conjugate priors for the gene and slide specific effectsmg *N(hm ,s2 ), mat *N(0,s2 ), a X subject to at 0 . Finally, the normal range of variability in mRNA expressions{2 *G(as ,bs ), g the tail over-dispersion parameters 1 wbz={*G(awz={ ,bwz={ ),z={ yg*G(ayz={ ,byz={ ),s{2 *G(as ,bs ): a Particular attention is given to the formulation of the prior for cdgw where 8 > N({k1 ,s2 ) 1 > > > < N(0,s2 ) 2 w cdg * > > N(k1 ,s2 ) > 1 > :w if dg {1 w if dg 0 w if dgand the regression parametersag *N(0:1), 8 > N({k2 ,s2 ) 1 > > > < N(0,s2 ) 2 ld yw * g > > N(k2 ,s2 ) > 1 > : 8 > N({k3 ,s2 ) 1 > > > < N(0,s2 ) 2 cd y * g > N(k3 ,s2 ) > > 1 > :yw if dg {1 yw if dg 0 yw if dg,with s1 much larger than s2 and k1 fixed at 1. The prior for b ‘s is given by pffiffiffiffiffiffiffiffiffiffiffiffi bb *N( 1{t2 ,s2 ) for b [ f1,2,:::,B{1g , with t2 v1 so that the marginal variance of zb ‘s is bounded above. Note that this model assumes that adjacent probes are equally correlated, characterized by b ‘s and t2 . Alternatively, one could model the correlation between probes as a function of their genomics distances, and this can be easily achieved by modeling bb{1 as a distance between probes b and Table 2. Numerosities in the training set and test set.y if dg {1 y if dg 0 y if dgwith the same assumptions on s2 , s2 and k2 , k3 fixed at 1. 1 2 A summary of the model is given in the upper part of Figure 1.Modified Probability Model for the prediction of pCRThe idea of this section raises from the question of whether or not we could use.C mean, describes a baseline bwhere xt is the clinical binary covariate mentioned above, while y yw dg and dg trinary indicators accounting respectively for differential gene expression in TN subgroup and interaction between the two measurement for gene g , following similar prior w to the one mentioned above for dg . Markov dependence across probes. A Markov dependence is assumed across the probes and it is defined in the following conditional prior on the probe specific effect. Define zw (zw ,:::,zw ): Assuming that the index b is ordered according to 1 BBayesian Models and Integration Genomic PlatformsFigure 3. Posterior probabilities of differential CNA (on the x-axis) and differential expression (y-axis) obtained respectively through the marginal models on CNA data and gene expression data (A). Black dots highlight posterior probabilities of genes which are claimed by the model to show joint differential behaviour (A). Comparison between differences in means of the gene expression data and posteriorBayesian Models and Integration Genomic Platformsprobability of differential expression (B). Comparison between sample correlations and posterior probabilities of positive interaction between platforms (C). doi:10.1371/journal.pone.0068071.glocus proximity on the chromosome, the dependence across adjacent probes is described as follows. Let z1 *N(0,1) and zw Dzb{1 w ,bb{1 *N(bb{1 zw ,t2 ) b b{1 for b [ f2,:::,Bg: In this formulation the parameters b (b1 ,:::,bb{1 ) can be directly interpreted as partial correlation coefficients, defining the strength of dependence between log2 23148522 ratios associated with probes that are adjacent on the chromosome. Priors. The last step is the specification of the priors for the set of parameters that index the sampling model. We assume conditionally conjugate priors. Denoting G(a,b) a gamma distribution with mean ab, we assume n{2 *G(an ,bn ), bb{1, for example. Finally we assume conditionally conjugate priors for the gene and slide specific effectsmg *N(hm ,s2 ), mat *N(0,s2 ), a X subject to at 0 . Finally, the normal range of variability in mRNA expressions{2 *G(as ,bs ), g the tail over-dispersion parameters 1 wbz={*G(awz={ ,bwz={ ),z={ yg*G(ayz={ ,byz={ ),s{2 *G(as ,bs ): a Particular attention is given to the formulation of the prior for cdgw where 8 > N({k1 ,s2 ) 1 > > > < N(0,s2 ) 2 w cdg * > > N(k1 ,s2 ) > 1 > :w if dg {1 w if dg 0 w if dgand the regression parametersag *N(0:1), 8 > N({k2 ,s2 ) 1 > > > < N(0,s2 ) 2 ld yw * g > > N(k2 ,s2 ) > 1 > : 8 > N({k3 ,s2 ) 1 > > > < N(0,s2 ) 2 cd y * g > N(k3 ,s2 ) > > 1 > :yw if dg {1 yw if dg 0 yw if dg,with s1 much larger than s2 and k1 fixed at 1. The prior for b ‘s is given by pffiffiffiffiffiffiffiffiffiffiffiffi bb *N( 1{t2 ,s2 ) for b [ f1,2,:::,B{1g , with t2 v1 so that the marginal variance of zb ‘s is bounded above. Note that this model assumes that adjacent probes are equally correlated, characterized by b ‘s and t2 . Alternatively, one could model the correlation between probes as a function of their genomics distances, and this can be easily achieved by modeling bb{1 as a distance between probes b and Table 2. Numerosities in the training set and test set.y if dg {1 y if dg 0 y if dgwith the same assumptions on s2 , s2 and k2 , k3 fixed at 1. 1 2 A summary of the model is given in the upper part of Figure 1.Modified Probability Model for the prediction of pCRThe idea of this section raises from the question of whether or not we could use.

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