Venn diagram for the top 50 genes ranked according to the differential analysis (p-value or log-fold change) and marginal causal approach (Bayes factor or total causal effects.

<p>Ranking was performed from lowest to highest for p-values and highest to lowest for absolute total effect, absolute log fold-change, and Bayes factor.</p

Comparison between the differential analysis and the marginal causal approach on chicken microarray data.

<p>Each point corresponds to a gene for which the differential and marginal causal analyses have been applied.</p

Models given observational or interventional data.

<p>Graphical representation of the <i>M</i>₁ (downstream) and <i>M</i>₀ (upstream or correlated) models under observational and interventional data.</p

Bayes factor for the simulated graph structure.

<p>Results from 100 simulations based on the graph in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171142#pone.0171142.g004" target="_blank">Fig 4</a>. Nodes simulated under the upstream/correlation model (<i>M</i>₀) appear to the left in black, and those simulated under the downstream model (<i>M</i>₁) appear to the right in red.</p

Illustration of upstream and downstream causality.

<p>Nodes <i>X</i>₀ and <i>X</i>₁ are both upstream causally related to knocked-out gene <i>G</i>, while nodes <i>X</i>₂ and <i>X</i>₃ are both downstream causally related to <i>G</i>.</p