Comparison between the results from HighEdgeS (left columns) and the classical approach (right columns) for the Pdx1 dataset.

<p>The three rows show the results of the pathway analysis methods ORA, SPIA, and LEGO. For each barplot, the x-axes show various values of the thresholds for each method. For the classical approach, the x-axes show various combinations of fold change and p-value thresholds. For the proposed approach the x-axes show the range from the point determined by change point analysis to the maximum value of edge scores. The y-axes in each graph show the scales for the false positive rate (FPR) and true positive rate (TPR). The right y-axes in each graph represent the number of DE genes shown by the gray bars. Blue dashed line represents the change point safety margin from edge point analysis (the default for the proposed method). In each bar plot, the green bars represent the TPR and red bars represent the FPR for pathways ranking when the significance threshold <i>α</i> = 0.1. True Positive pathways are those containing the KO gene. The figure shows that the proposed approach yields a higher TPR than the classical approaches overall. For ORA, the classical approach fails to identity any of the 3 pathways containing the KO gene independently of the threshold combination used (top right panel). The classical approach used with SPIA identifies 1, 2 or 3 of the 3 true positive pathways depending on the threshold combination used (middle right panel). Finally, the classical approach used with LEGO identifies only one of the 3 true positive pathways and that only for about 1/3 of the range of thresholds explored. In contrast, the proposed approach used with ORA (top left) and SPIA (middle left) identifies all true positive pathways for any edge scores. When combined with LEGO (bottom left), the proposed approach identifies 2 out of the 3 true positives, still much better than the classical approach with LEGO (bottom right).</p

The workflow of the proposed method, HighEdgeS.

<p>a) The global graph constructed from all interactions present in all KEGG pathways; b) The edge score histogram constructed from the input data. A change point analysis [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176950#pone.0176950.ref049" target="_blank">49</a>] is used to determine the beginning of the flat area of the curve. The selected edges will be those in the top 75% of the remaining scores. c) The global graph showing the edges with high scores in red; d) The subgraph with the high scoring edges only representing the putative mechanism involved in the given phenotype.</p

The comparison between the putative mechanisms constructed by HighEdgeS and the classical approach for the Pdx1 dataset.

<p>a.) The putative mechanism constructed by HighEdgeS. The graph shows the knocked out gene Pdx1 regulating the Ins1 gene. b.) The results for the same dataset when the classical approach was applied. The results of the classical approach lack any connection among the genes and the KO. However, the results of the proposed approach show the interaction between the Pdx1 gene and the Ins1. In fact, the authors of the dataset discussed this interaction in their work indicating the role of the Ins1 in this condition.</p

A comparison between the results of HighEdgeS, the classical approach, and GAGE as they would be used in practice.

<p>Three pathway analysis methods (ORA, SPIA and LEGO) were applied on the genes yielded by HighEdgeS and the classical approach. GAGE uses the entire set of genes to directly identify pathways so ORA, SPIA and LEGO are not applicable for GAGE. The green background indicates the best results obtained for each dataset (each row). For the classical approach we selected the DE genes using an absolute fold change greater than 2 and FDR-corrected p-value less than 0.05. “No DE” means no genes met those thresholds. The performance was measured using the true positive rate (TPR) and false positive rate (FPR) for each data set. The results show that HighEdgeS yields the best TPR for all datasets. HighEdgeS also yield the best FPR for 2 out of the 3 datasets. Furthermore, for the Pdx1 dataset, even though the classical approach has a lower FPR values, its sensitivity is very low (not being able to identify any true positive (TP) pathway in 2 out of 3 cases, and identifying only one of the 3 true positive pathway in the remaining case). In contrast, HighEdgeS identified all true positive pathways in 2 out of 3 cases (when combined with ORA and SPIA) and 2 out of the 3 TP pathways when combined with LEGO.</p

Comparison between the results from HighEdgeS (left columns) and the classical approach (right columns) for the Myd88 dataset.

<p>The three rows show the results of the pathway analysis methods ORA, SPIA, and LEGO. For each barplot, the x-axes show various values of the thresholds for each method. For the classical approach, the x-axes show various combinations of fold change and p-value thresholds. For the proposed approach the x-axes show the range from the point determined by change point analysis to the maximum value of edge scores. The y-axes in each graph show the scales for the false positive rate (FPR) and true positive rate (TPR). The right y-axes in each graph represent the number of DE genes shown by the gray bars. Blue dashed line represents the change point safety margin from edge point analysis (the default for the proposed method). In each bar plot, the green bars represent the TPR and red bars represent the FPR for pathways ranking when the significance threshold <i>α</i> = 0.1. True positive pathways are those containing the KO gene. The proposed method yields a perfect true positive rate of 100% in every case for its default threshold (blue dashed line). In contrast, the classical approach yields a TPR of less than 40% for all threshold combinations used with ORA (top right panel) and SPIA (middle right panel). The classical approach used with LEGO (lower right panel) yields a TPR varying between 0 and 93%. The figure also shows how the results of the classical approach depend very much on the combination of thresholds used for fold change and p-values, while the results obtained with the proposed method are much more stable.</p

Figure shows the results for the dataset NeuroD1.

<p>The KO gene is shown in red in both panels. a) The mechanism found using HighEdgeS shows NeuroD1 is regulating Ins2, Iapp, Gck and Ins1. b) The results obtained using the classical approach for the same dataset. The classical approach results include the KO gene, but provides no explanation on how the suppression of this gene propagates further and affects the rest of the system.</p

Figure shows the results for the dataset Myd88.

<p>The KO gene is shown in red in both panels. a) Mechanism results for HighEdgeS when we applied it on the Myd88 dataset. The results show two subgraphs. The one with the most genes shows the KO gene regulates the Cxcl1 gene and shows that the subgraph includes many Cxcl1 downstream genes. b) The results of the classical approach using DE genes. The Myd88 gene is connected to only one downstream gene. The results of the classical approach has many genes that are not connected indicating that the classical approach missed important interactions.</p

Schematic flow chart of the generation and evaluation of the MITIN network.

<p>The different sources of functional interaction are combined to generate a functional interactome. The resulting network is used to identify the direct and indirect interactions between the insulin signaling and mitochondria systems. The relevance of the MITIN network is tested analyzing gene expression data of models perturbing either insulin signaling or mitochondria function, and testing the variability within or near the MITIN network genes using GWA meta-analyses from DIAGRAM consortium. *In all PPIhigh and PPIcorr, both pair of interacting proteins have to be simultaneously expressed in any of the insulin-targeted tissues (adipose tissue, muscle, liver and heart).</p

Strong candidates linking both insulin and mitochondria genes.

<p>The internode genes listed in the table have at least three lines of evidence that link them to the mitochondria and three to insulin signaling.</p>#<p>Above 95% percentile of T2D association gene scores based on DIAGRAM meta-analysis (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003046#pgen-1003046-t002" target="_blank">Table 2</a>).</p>*<p>Associated to HOMA-IR(9.74E–6) <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003046#pgen.1003046-Dupuis1" target="_blank">[42]</a>.</p

Gene set enrichment analysis.

<p>Gene set enrichment analysis of models with impaired Insulin (a, c) or mitochondrial (b) function. In all cases there was enrichment of upregulated genes within the internodes, except for the case when internodes were generated from a random network (d).</p