Network statistics and major hubs in consensus modules visualized in Fig 7.

<p>Only intra-module interactions are considered (N = 640). The three consecutive numbers in parentheses in rows 1 and 4 indicate the number of edges in the ‘positive dominant’, ‘negative dominant, and the ‘heterogeneous’ groups respectively. The two consecutive numbers in parentheses in row 2 denote the number of non-phosphospecific and phosphospecific antibodies respectively. The sum of antibody counts in row 2 (185) is one less than the number of unique antibodies in the discovery set because one antibody (14.3.3_zeta) has no intra-module interactions despite having multiple inter-module interactions (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004765#pcbi.1004765.s010" target="_blank">S1A Table</a>).</p

Hierarchical clustering of the 1008 edges in the <i>discovery set</i>.

<p>Three edge groups can be observed on the dendrogram: (1) positive dominant, (2) negative dominant and (3) heterogeneous. Hierarchical clustering was performed with Ward linkage and Euclidean distance. <b>Blue</b> and <b>red</b> denote negative and positive consensus edge weights respectively. The shades of <b>green</b> denote the recurrence of each edge, <i>i</i>.<i>e</i>. the number of tumor types (between 1 and 11) where that edge has consensus rank smaller (more significant) than the threshold of 425.</p

Workflow for the performance evaluation of network inference methods on a proteomic dataset.

<p>The workflow is comprised of a computational inference component and a pathway knowledgebase component that are used to generate separate PPI network models. Caveats involved in Steps 1–6 are discussed in Discussion and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004765#pcbi.1004765.s001" target="_blank">S1A Text</a>.</p

PANCAN11 tumor types, the abbreviations used in the study, and the number of samples in each tumor type.

<p>PANCAN11 tumor types, the abbreviations used in the study, and the number of samples in each tumor type.</p

Pan-cancer similarities and differences for Reactome top-level events with the highest numbers of matching gene lists.

<p>Per tumor type average interaction strength for the top-level events 1) signal transduction, 2) cell cycle, 3) immune system, 4) metabolism, 5) DNA repair, and 6) disease are shown. The number of significant (consensus rank < 425) interactions that match to each gene list is broken down by module or group, averaged over tumor types, and tracked on the left of the heatmap. Heat map orders for gene lists (rows) and tumor types (columns) are both obtained from Ward-linkage Euclidean-distance hierarchical clustering. The numbers for the ‘module’ lines may be zero if the matching interactions are inter-module (linking two modules).</p

Performance comparison and unsupervised clustering for 13 network inference methods.

<p>(<b>A</b>) AUPR for each method in individual tumor types. Tumors are ordered according to increasing coefficient of variation. (<b>B</b>) Ranking of methods according to (left panel) overall AUPR and (right panel) overall AUPR rank in 11 tumor types. (<b>C</b>) Unsupervised hierarchical clustering of the Spearman correlations between methods. (<b>D</b>) Principal component analysis of edge weights from the methods by stacking edge lists from the investigated tumor types.</p

Tested network inference methods.

<p>Methods can be grouped according to the algorithm family or the regularization type. Algorithm families include correlation, partial correlation with inverse covariance, partial covariance with regression, and mutual information. Regularization types employed by methods can be shrinkage, sparsity, or a combination of shrinkage and sparsity as in ELASTICNET. The abbreviations used in this study are given in parentheses in the Method column.</p

Network visualization of the six modules discovered in Fig 6.

<p>Only intra-module interactions are visualized. Each interaction is colored red, blue, or black based on membership in the positive dominant, negative dominant, or the heterogeneous groups respectively. The vertex colors are adopted from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004765#pcbi.1004765.g006" target="_blank">Fig 6</a> and denote module number. Network statistics are provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004765#pcbi.1004765.t003" target="_blank">Table 3</a>, and module membership information for all discovery set interactions is provided in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004765#pcbi.1004765.s010" target="_blank">S1A Table</a>.</p

Principal component analysis and unsupervised clustering of 11 tumor types using consensus edge weights from the TOP6 methods.

<p>(<b>A</b>) Four major groups of tumor types can be observed in the PC1 vs. PC2 (left) and PC2 vs. PC3 (right) plots: 1) COAD, READ; 2) LUAD, LUSC, HNSC; 3) GBM, KIRC; 4) OV, BRCA, UCEC, BLCA. (<b>B</b>) Hierarchical clustering (Ward linkage and Euclidean distance) on consensus edge weights places tumor types into the same four groups on the dendrogram (left). The heat map on the right is constructed from the percentages of overlapping edges between tumor types. The order of tumor types in the heat map is taken from the dendrogram on the left.</p

Precision–recall (PR) curves optimized for full versus limited range of recall values.

<p>(<b>A</b>) <b><i>Top panel</i>:</b> PR curves for the 13 methods in the BRCA and GBM cohorts. PR curves are constructed by cumulatively increasing the number of edges from a ranked edge list. For each method, the relevant curve is computed with a choice of parameters that maximize AUPR in the recall range [0,1] (<i>i</i>.<i>e</i>. full-recall). <b><i>Bottom panel</i>:</b> A zoomed-in version for recall in [0,0.1] and precision in [0,0.5]. (<b>B</b>) PR curves when the parameters are chosen to optimize AUPR specifically in the [0,0.1] recall range (<i>i</i>.<i>e</i>. limited-recall). We choose the limited-recall case for subsequent analysis because of two reasons. Beyond the 10% recall level, (1) the difference among methods become indiscernible, and (2) the precision level is very low suggesting network predictions are more likely to be affected by noise.</p