This applet demonstrates the reconstruction of gene regulatory networks from genetical genomics data using Bayesian network structure learning techniques. It aims at recovering the underlying gene regulation network given measures of expression data and SNP occurrences for a set of individuals.
It can also be used to simply view a network (for instance the result of a previous run), and/or compare two networks (for instance, the result of a run and a known truth to assess the performance, or the results of two different runs).
A word about the input data
We assume a one gene-one marker dataset. Each gene is associated to a genetic marker corresponding to the same columns in the gene/marker datasets. If the position/effect of a marker inside its associated gene is known, a mutation in the promoter region is encoded by 1 (denoted cis marker in the applet) and a mutation in the coding region by 2 (denoted trans marker in the applet), and no mutation is encoded by 0. If the effect is unknown, use 0/1 values only. If a gene has no marker associated to it, use a dummy marker column filled in with 0 values. If a marker has no gene associated to it, use a dummy gene expression column filled in with 0 values.