Example perturbation-responding subgraphs.

<p>Two example subgraphs are shown: <b>Panel A</b>, GO:0019236 (<i>response to pheromone</i>) and <b>Panel B</b>, GO:0006826 (<i>iron ion transport</i>). For each subgraph, the perturbation instances (green hexagons) are shown in the top tier; responding genes (blue circles) are shown in the middle tiers; and the transcription factor modules (grey triangles) are shown in the bottom tier. To avoid an overly crowded figure, a red dash line indicates that a perturbation instance and a responding gene are NOT connected.</p

Algorithm for organizing perturbation instances and RMs.

<p>Algorithm for organizing perturbation instances and RMs.</p

Subgraph connectivity.

<p>Cumulative distribution of within bipartite subgraph connectivity of the modules identified in three experiments: MBSEC with module-based input graphs (red); SAMBA with module-based input graphs (green); and SAMBA with global input graph (blue).</p

Characterization of the summary GO terms.

<p><b>A</b>. The histograms of the number of genes associated with each GO term before and after ontology-guided knowledge mining: 1) the original GO annotations for all responding genes (blue); and 2) the GO terms returned by the instance-based module search (red). <b>B</b>. The distribution of the levels of the above GO term sets in the ontology hierarchy are shown as normalized histograms. Level represents the root of the Biological Process namespace.</p

Functional coherence of modules.

<p><b>A</b>. The cumulative distribution of functional coherence p-values of the responding modules identified by different methods: MBSEC with module-based input graphs (red); SAMBA with module-based input graphs (green); and SAMBA with the global input graph (blue). <b>B</b>. The cumulative distribution of functional coherence p-values of the perturbation modules identified by different methods: MBSEC with module-based input graphs (red); SAMBA with module-based input graphs (green); and SAMBA with the global input graph (blue).</p

Organizing perturbation instances and responding modules.

<p>In this graph, responding modules are represented as green oval nodes, with each being annotated by a GO term. The rectangle nodes are perturbation nodes, which may contain one or more genes that share a common set of responding modules.</p

Greedy algorithm to find the highly dense bipartite subgraph.

<p>Greedy algorithm to find the highly dense bipartite subgraph.</p

Protein-protein physical and genetic interactions within modules.

<p><b>A</b>. The cumulative distribution of the within module PPI/GI connectivity ratios of responding modules identified by different methods: MBSEC with module-based input graphs (red); SAMBA with module-based input graphs (green); and SAMBA with the global input graph (blue). <b>B</b>. The cumulative distribution of the connectivity ratios within perturbation modules identified by different methods: MBSEC with module-based input graphs (red); SAMBA with module-based input graphs (green); and SAMBA with the global input graph (blue).</p

DHA has no effect on SM hydrolysis and ASMase activity stimulated by LPS and PA.

A. RAW264.7 macrophages were treated with 1 ng/ml of LPS, 100 μM of PA or both 1 ng/ml LPS and 100 μM of PA in the absence or presence of 100 μM of DHA for 12 h. After treatment, cellular sphingomyelin was quantified using lipidomics. * vs. #, pppp<0.05.</p

Myriocin inhibits IL-6 secretion stimulated by LPS or LPS plus PA.

<p>A-C. RAW264.7 macrophages were treated with 1 ng/ml of LPS, 100 μM of PA or both LPS and PA in the absence or presence of 10 μM of myriocin for 12 h. After the incubation, total (A), C16-CER (B) and dhC16-CER (C) were quantified using lipidomics. D. RAW264.7 macrophages were treated with 1 ng/ml of LPS, 100 μM of PA or both LPS and PA in the absence or presence of 10 μM of myriocin for 24 h. After treatment, IL-6 in culture medium was quantified using ELISA. * vs. +, <i>p</i><0.01; * vs. #, <i>p</i><0.01; ** vs. *, <i>p</i><0.01; ^^ vs. ^, <i>p</i><0.01; ++ vs. +, <i>p</i><0.01; ## vs. #, <i>p</i><0.01.</p