If the expression level of a certain set of genes faithfully represents pathway activity and if these genes are commonly upre gulated in response to pathway activation, then one would expect these genes to show significant STAT inhibition correla tions at the level of gene expression across a sample set, provided of course that differential activity of this path way accounts for a proportion of the data variance. Thus, one may use a gene expression data set to evalu ate the consistency of the prior information and to filter out the information which represents noise. Simulated Data To test the principle we first generated syn thetic data where we know which samples have a hypothetical pathway activated and others where the where the summation is over the validation sets, S is the threshold function of pij defined by notes its absolute value.
Thus, the quantity Vij takes into account the significance of the correlation between the pathways, penalizes the score if the directionality of correlation is opposite to that predicted ) and weighs in the mag method, we thus obtain a set of hypotheses objective comparison IEM 1754 between two different methods for pathway activity estimation can be achieved by comparing the distribution of V to that of V over the common hypothesis space i. e H ? H. For this we used a two tailed paired Wilcoxon test. Results and Discussion We argue that more robust statistical inferences regard ing pathway activity levels and which use prior pathway is switched off. We considered two different simulation scenarios as described in Methods to represent two different levels of noise in the data.
Next, we applied three different methods to infer path way activity, one which simply averages Metastatic carcinoma the expression profiles of each gene in the pathway, one which infers a correlation relevance network, prunes the network to remove inconsistent prior information and estimates activity by averaging the expression values of the genes in the maximally connected component of the pruned network. The third method also gener ates a pruned network and estimates activity over the maximally connected subnetwork but does so by a weighted average where the weights are directly given by the degrees of the nodes. To objectively compare the different algorithms, we applied a varia tional Bayesian clustering algorithm to the one dimensional estimated activity profiles to identify the different levels of pathway activity.
The variational Baye sian approach was used over the Bayesian Information E7080 ic50 Criterion or the Akaike Information Criterion, since it is more accurate for model selection problems, particularly in relation to estimating the number of clusters. We then assessed how well samples with and without pathway activity were assigned to the respective clusters, with the cluster of lowest mean activity representing the ground state of no pathway activity. Examples of specific simulations and inferred clusters in the two different noisy scenarios are shown in Figures 2A 2C.