Indirect Comparison of Interaction Graphs

Motivation: Over the past years, testing for differential coexpression of genes has become more and more important, since it can uncover biological differences where differential expression analysis fails to distinguish between groups. The standard approach is to estimate gene graphs in the two groups of interest by some appropriate algorithm and then to compare these graphs using a measure of choice. However, different graph estimating algorithms often produce very different graphs, and therefore have a great influence on the differential coexpression analysis.

Results: This talk presents three published proposal and introduces an indirect approach for testing the differential conditional independence structures (CIS) in gene networks. The graphs have the same set of nodes and are estimated from data sampled under two different conditions. Out test uses the entire pathplot in a Lasso regression as the information on how a node connects with the remaining nodes in the graph, without estimating the graph explicitly. The test was applied on CLL and AML data in patients with different mutational status in relevant genes. Finally, a permutation test was performed to assess differentially connected genes. Results from simulation studies are also presented.

Discussion: The strategy presented offers an explorative tool to detect nodes in a graph with the potential of a relevant impact on the regulatory process between interacting units in a complex process. The findings introduce a practical algorithm with a theoretical basis. We see our result as the first step on the way to a meta-analysis of graphs. A meta-analysis of graphs is only useful if the graphs available for aggregation are homogeneous. The assessment of homogeneity of graphs needs procedures like the one presented.