The goal of URA is to identify molecules upstream of the genes in the dataset that potentially explain the observed expression changes. Since it is a priori unknown which causal edges in the master network are applicable to the experimental context, we use a statistical approach to determine and score those regulators whose network connections to dataset genes as well as associated regulation directions are unlikely to occur in a random model. In particular, we define an overlap P-value measuring enrichment of network-regulated genes in the dataset, as well as an activation Z-score which can be used to find likely regulating molecules based on a statistically significant pattern match of up- and down-regulation, and also to predict the activation state (either activated or inhibited) of a putative regulator.
Here, we consider transcription and expression (T) edges only by looking at the subgraph and defining the subset of genes that are regulated by at least one edge in
A potential regulator r can be any node in V that is either a gene, protein family, complex, microRNA, or chemical. For a particular given regulator we define the set of downstream regulated genes as
For each the sign of v is defined as regulation direction of v under the assumption that r is activated, which is given by the regulation direction of the connecting edge, as
Similarly we define the weight associated with v to be
Here, we consider transcription and expression (T) edges only by looking at the subgraph and defining the subset of genes that are regulated by at least one edge in
A potential regulator r can be any node in V that is either a gene, protein family, complex, microRNA, or chemical. For a particular given regulator we define the set of downstream regulated genes as
For each the sign of v is defined as regulation direction of v under the assumption that r is activated, which is given by the regulation direction of the connecting edge, as
Similarly we define the weight associated with v to be
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