One of the core aims of differential expression analysis is to understand what is regulating any differentially expressed genes. One method to help determine this is Upstream Regulator Analysis (URA). In URA a predefined database of upstream regulator genes alongside the genes that each regulates, and the typical direction of that regulation (e.g. TNFA upregulates PCP2 and downregulated TNFIAP3) is supplied, alongside a list of observed differential expression fold changes. The upstream regulator algorithm then determines for each upstream regulator how closely the observed pattern of differential expression matches the expected pattern. Enrichment of an activated or inhibited regulator is indicated as a z-score.
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As individual genes can have several different functions, and upstream regulators can have similar or overlapping functions there is often considerable redundancy in upstream regulators databases and thus enrichment results. I.e. often there are many upstream regulators that regulate very similar genes, and so are themselves very similar. This can often mean that the ten most enriched upstream regulators are highly similar, and only represent a portion of the enriched biology. It is therefore highly useful to view all upstream regulator enrichment results as a network, where each node is an upstream regulator and each edge links upstream regulators which regulate highly similar genes. This often gives rise to clusters of upstream regulators with similar function. When viewing these plots it is more important to consider the general biological theme of each cluster rather than the exact details of the upstream regulators or genes within them. 
