Inferring causal molecular networks: empirical assessment through a community-based effort.

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Inferring causal molecular networks: empirical assessment through a community-based effort

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks

Inferring causal molecular networks: empirical assessment through a community-based effort

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense

Prophetic Granger Causality to infer gene regulatory networks.

We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring

Prophetic Granger Causality to infer gene regulatory networks

<div><p>We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.</p></div

Impact of mutations on local network.

<p>Impact of mutations on local network.</p

Cell-type vs. Stimulus ligand influence on the inferred HPN consensus network reveals a preponderance of cell-type interactions.

<p>Here we show the top 10 percent of interactions in the consensus network. ANOVA analysis on Granger coefficients was used to determine if interactions were cell-type dependent (red lines) or independent (grey) and if they were stimulus ligand-dependent (dotted) versus stimulus ligand-independent (solid). Line thickness reflects the inferred interaction strength. Cell-type-dependent interactions were much more common over stimulus ligand-dependent interactions suggesting that cellular context has an important influence on the underlying GRN. Proteins with more than one phosphorylation site are disambiguated with lower case letters following the protein name. Disambiguation of the identity of these probes appears in Supplemental <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170340#pone.0170340.s005" target="_blank">S4 Table</a>.</p

Evidence of mutational disruption network activity of MAPK8.

<p>Interaction strengths involving JUN N-terminal Kinase (MAPK8) in the mutant UACC812 cell line are lower than in wild type cell lines. Interaction strengths were calculated as the normalized Granger coefficients derived in each cellular context. Each point is an interaction and points that appear above the line of equality (<i>Y</i> = <i>X</i>) indicate loss of function. Interaction strengths derived from all other interactions not involving MAPK8 are shown as the background (grey dots). Both the upstream and downstream interactions of MAPK8 (red) are significantly disrupted.</p

Prophetic Granger Causality method.

<p>(A) The method is given a set of probes (rows; y-axis) measuring the level of a particular phospho-protein state at particular time points (columns; x-axis). Each probe value at each time point) is considered in turn as a linear regression of all other feature times and probes. Depicted is probe <i>A</i> being considered at time <i>t</i> (green). The penalty parameter L1 is chosen such that autoregression contributions (red) are set to zero. Any remaining non-zero regression coefficients for other probes suggest causality; past or concurrent time point probes (blue) are considered <i>causal of</i> the target; future time point probes (yellow) are considered to be <i>caused by</i> the target. The different inhibitor conditions are treated as different examples in the regression task. This process was repeated for each time and probe, with each regression task contributing to the final connectivity matrix. (B) Overview of the overall PGC plus network prior approach for the HPN DREAM8 submission. Shown is a prediction for a single (cell line, ligand) pair task. (i.) 263 Pathway Commons pathways having at least two proteins in the DREAM dataset (colored shapes). (ii.) Heat diffusion kernel used to measure closeness between protein pairs in each pathway (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170340#pone.0170340.s001" target="_blank">S1 File</a>) were combined into a single weighted “network prior,” represented as an adjacency matrix. (iii.) The Prophetic Granger solution, obtained as shown in part A. (iv.) The final solution for the (cell line, ligand stimulus)-pair is produced by averaging the network prior with the absolute value of the Prophetic Granger solution.</p