Impact of increasing number of orderings used in the RIPE algorithm.

<p>Average performance measures, in percentages, for RIPE in the synthetic network .</p

Performance of RIPE and competing methods on the reconstruction of synthetic networks.

<p>(A) Average measures for reconstruction using NEM, PCALG, FFLDR and RIPE on a network of size . corresponds to the ideal influence graph and to represent increasing levels of noise in perturbation data (see also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0082393#pone.0082393.s007" target="_blank">Figure S7</a>). (B)–(C) Average measures for reconstruction of synthetic regulatory networks using PCALG, FFLDR and RIPE for different levels of false positive and negative noise in perturbation data. Numbers in parentheses indicate the expected number of false edges in each case. The true graph is an acyclic graph (DAG) of size in (B) and a cyclic graph of size in (C).</p

Impact of increasing false positive and negative errors in perturbation data on estimation of acyclic graphs.

<p>Average performances measures, in percentages for PCALG, FFLDR and RIPE in the synthetic DAG of size <i>p</i> = 100 with different error structures. Numbers in parentheses indicate the standard deviation of the estimates over 50 draws of simulated data (only for PCALG and RIPE).</p

Reconstruction results for DREAM4 networks with simulated steady state data.

<p>Performance measures, in percentages, for methods of reconstruction of DREAM4 <i>Net1</i>, <i>Net3</i>, and <i>Net5</i>, using both knockout (KO) and knockdown (KD) data. Steady state expression data is generated from structural equation models based on the true graph.</p

Influence graph characteristics versus -value for DREAM4.

<p>Number of edges (open triangles) in the influence graph and the size of the largest connected component (dots) versus cut-off -value for differential expression. The data is based on five replications of the knockout and wild-type experiments for (A) 100-node network 1, (B) 100-node network 3, and (C) 100-node network 5 in the DREAM4 challenge.</p

Algorithm for DFS.

<p>Detailed steps of the algorithm for the DFS search.</p

Overview and details of the RIPE method.

<p>(A) Overview of the RIPE algorithm. (B) First step to obtain a large set of causal orderings from a network with cycles. The network graph is decomposed into strongly connected components, so called super-nodes, (left) followed by a DFS on the strongly connected components (right). For example, the post-visit time of super-node A is 8, and thus A precedes all other nodes. The topological ordering of super-nodes is ABCD. (C) Illustration of MC-DFS algorithm. Gene perturbation graph (left), DFS visit times for labeling #1 (middle), and DFS visit times for labeling #2 (right). (D) Depiction of a small network to illustrate the influence graph and the predictors used in the penalized likelihood estimation procedure. The true regulatory network (left), the influence graph under no noise (middle), observed influence graph, with false positive and false negative edges (right). Edges in the regulatory networks are shown in thick lines and additional edges in the influence graph are distinguished with narrow lines; red dash lines indicate false positives.</p

Reconstruction results for DREAM4 networks with multifactorial data.

<p>Performance measures, in percentages, for methods of reconstruction of DREAM4 <i>Net1</i>, <i>Net3</i>, and <i>Net5</i>, using both knockout (KO) and knockdown (KD) data. Multifactorial data from DREAM4 challenge is used as steady-state expression data.</p

Performance evaluation for the reconstruction of the yeast regulatory network.

<p>Number of true positives for each method, in comparison to the BIOGRID database, as well as a histogram for the number of true positives in randomly generated networks of the same size are shown. The p-values are obtained based on 10,000 randomly generated networks.</p