3 research outputs found

    Inferred MAPK networks on HRV infection data.

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    <p>Best networks of the 5 top scoring siRNAs from the MAPK pathway for HRV infection for the different compared methods are displayed. (A) shows the known KEGG pathway. (B) is the inferred NEM and (C) the sc-NEM. (D) left shows the known network with the most likely attachment of the hidden variable <i>Z</i> (blue) and (E) is the inferred NEMix. For all networks their performance is summarized in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004078#pcbi.1004078.t001" target="_blank">Table 1</a>. Subfigure (F) summarizes robustness of the MAPK network inference. For the inferred MAPK signaling networks on the HRV infection data, we assessed robustness of the accuracy for edge recovery. Box-plots display the result of 50 bootstrap samples for the three compared methods, on the 5 gene (<i>n</i> = 5) and 8 gene (<i>n</i> = 8) network.</p

    Performance summary of the 5 gene MAPK network.

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    <p>The first column gives the log-likelihood for each model, showing that the true network is much less likely than the inferred networks. The second and third column show performance of the networks in terms of accuracy (ACC) and area under curve (AUC). The inferred <i>p</i><sub>0</sub> for the NEMix models is displayed in column four. Column five indicates the corresponding sub-figure of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004078#pcbi.1004078.g003" target="_blank">Fig. 3</a>. The network ‘KEGG Graph + Z’ denotes the structure of the known KEGG network, where only the position of <i>Z, p</i><sub>0</sub>, and <i>θ</i> are inferred.</p><p>Performance summary of the 5 gene MAPK network.</p

    NEM versus NEMix.

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    <p>A schematic example is shown comparing the classical nested effects model (NEM; panel <b>A</b>) with the new nested effects mixture model (NEMix; panel <b>B</b>) on six features observed in 15 individual cells. Blue nodes in the graph depict the signaling genes <i>S</i><sub>1</sub>, <i>S</i><sub>2</sub>, and <i>S</i><sub>3</sub> that have been silenced and whose dependency structure is sought. The observed features <i>E</i><sub>1</sub>, …, <i>E</i><sub>6</sub> are shown in green. Each box below the graphs indicates the observed (noisy) features (e.g., image-based read-outs) for a single cell. Within each box, dark entries indicate an effect of the knock-down on the feature, light entries indicate no effect. In cells 1 and 2 (left in both <b>A</b> and <b>B</b>), the pathway has been activated via <i>S</i><sub>2</sub>, whereas in cells 3, 4, and 5 (right in both <b>A</b> and <b>B</b>) it has remained inactivated. In the latter case, the effects of silencing <i>S</i><sub>2</sub> are masked and the resulting silencing scheme then differs from the one where the pathway is stimulated. Classic NEMs (<b>A</b>) could explain such a heterogeneous cell population only by two different signaling graphs Φ. By contrast, with the NEMix model proposed in this work (<b>B</b>), both observed patterns can be explained by the same signaling graph Φ, because the hidden pathway stimulation <i>Z</i> (shown in red) is modeled explicitly. In the NEMix model, <i>Z</i> is a hidden binary random variable indicating pathway activation (<i>Z</i> = 1), which occurs with probability <i>P</i>(<i>Z</i> = 1) = <i>p</i><sub>1</sub>.</p
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