Figure 1

Measuring pathway module activation. Flowchart figure showing overall strategy used for inferring pathway module activity in clinical tumor samples from a model (perturbation) signature. A) A gene mRNA signature that represents a perturbed cancer cell phenotype (i.e oncogene overexpression) is combined with mRNA expression data of a large panel of clinical tumor specimens to derive an "expression relevance network" where nodes represent genes from the signature and an edge between two nodes indicates a statistically significant Pearson correlation between the two corresponding genes as measured over the clinical tumor panel. Having constructed the relevance network, the network is first pruned so that network edges that are inconsistent with prior information are removed. Signs on edges between labelled genes indicate the sign of the significant correlation between the two genes, which must be consistent with their directionality as given by the model signature. Modules defined as subnetworks with higher than average edge density are then inferred using a spectral decomposition algorithm (see Methods). B) For a given relatively large module, the module of pathway activation (MPA), pathway activity is then computed using a metric defined over the topology of the module. In the formula, PA _s stands for the estimated pathway module activity in sample s, M is the number of genes in the module, σ _i is a binary weight (1,-1) indicating the directionality of gene expression of gene i (1 = upregulated, -1 = downregulated), z _is is the z-score normalised gene expression value in sample s and A _ij is the adjacency matrix of the module. Effectively, this metric gives more weight to gene interactions that are supported by the data. Color and sign of nodes reflect the directionality of expression in the in-vitro signature (Red = upregulated &σ = 1, Green = downregulated &σ = -1). Pathway activity levels can then be shown as heatmaps (blue = high activity, yellow = low activity).