PMC

PPI network and module analysis network. (a) (left) Normal group-common target PPI network between hepatitis B data and predicted targets of YZHG; (right) module analysis of the CytoHubba plug-in identified the top 10 genes to construct key module network 1. (b) (left) The hepatitis B-hepatitis B-related hepatocellular carcinoma data and the PPI network of YZHG; (right) module analysis was performed using the CytoHubba plug-in to obtain the top 10 genes and construct key module network 2. (c) (left) Hepatitis C PPI data and PPI network of YZHG; (right) the top 10 genes were obtained to construct key module network 3 after the CytoHubba plug-in module analysis. (d) (left) PPI network of hepatitis C-related cirrhosis-hepatitis C-related hepatocellular carcinoma; (right) module analysis of the CytoHubba plug-in identified the top 10 genes to construct key module network 4. Left: orange circle represents a common target, and purple means related protein information obtained by PPI. Right: the top five genes with the highest scores are represented by yellow to red, and the redder the color, the higher the MCC score.

A. The heatmap of lncRNAs and mRNAs in the module 1 based on expression. The columns represented 6 samples and the rows represented lncRNAs and their mRNA neighbors. Labels on the right were the names of lncRNAs and mRNAs. B. The heatmap of lncRNAs and mRNAs in the module 2 based on expression data. The columns represented 6 samples and the rows represented lncRNAs and their mRNA neighbors. Labels on the right were the names of lncRNAs and mRNAs. C. The ceRNA module network of module 1 extracted from global triple network. D. The ceRNA module network of module 2 extracted from global triple network. F. The triple network of lncRNA FAM13A-AS1 extracted from global triple network. E. The triple network of lncRNA LINC00648 extracted from global triple network.

(A) Entire PPI network. (B) PPI network of module 1. (C) PPI network of module 2. (D) PPI network of module 3.

Construction of co-expression modules of ovarian cancers based on lncRNA data (left column) and mRNA data (right column). A Analysis of network topology for various soft-threshold powers, including the scale-free fit index (y-axis) and the mean connectivity (degree, y-axis). B Check scale-free topology, and here the adjacency matrix was defined using soft-thresholds with beta = 3 for lncRNA data, and with beta = 4 for mRNA data. C Clustering dendrograms of lncRNAs or mRNAs, with dissimilarity based on topological overlap, together with assigned module colors. As a result, six co-expression modules (co-expression green module, co-expression turquoise module, co-expression yellow module, co-expression blue module, co-expression brown module, co-expression gray module) were constructed from lnRNA data, and 14 co-expression modules from mRNA data (co-expression magenta module, co-expression tan module, co-expression green module, co-expression black module, co-expression yellow module, co-expression green-yellow module, co-expression red module, co-expression turquoise module, co-expression purple module, co-expression blue module, co-expression pink module, co-expression brown module, co-expression salmon module, co-expression gray module). D The heatmap depicts the topological overlap matrix (TOM) among all lncRNAs or all mRNAs. E Visualizing the gene network using a heatmap plot

Overview of The Cancer Proteome Atlas
(A) Information flow of RPPA data; (B) Overview of TCPA portal; (C) “Summary” module; (D) “Download” module; (E) “My Protein” module for patient-cohort data; (F) “My Protein” module for cell-line data; (G) “Correlation Analysis” for patient-cohort data; (H) “Correlation Analysis” module for cell-line data; (I) “Differential Expression” module for patient-cohort data; (J) “Patient Survival” module; (K) “Drug-Protein” module by protein; (L) “Drug-Protein” module by drug; (M) Zoomed-in view of “Network” visualization for patient-cohort data with a snapshot of the full view at the top-left corner; (N) Zoomed-in view of “Network” visualization for cell-line data; and (O) “NG-CHM” visualization with a snapshot of the full view at the top-left corner.

BIANA Architecture. BIANA is composed of 4 different modules: Database Module, Parser Module, Network Module and Session Management Module. Database Module handles communication between BIANA and MySQL database. Parser Module imports data into BIANA database. Network Module performs all network procedures using NetworkX package. Session Management Module to handle biological data sets and networks. BIANA Cytoscape Plugin is a separate interface that communicates Cytoscape with BIANA through a socket. BIANA framework can be executed with Python interpreter (as well as command line python scripts) or in Cytoscape with a plugin.

WGCNA of plasma exosome samples uncovered dynamic changes of exosome mRNAs and lncRNAs signatures during substance withdrawal. (A) Hierarchical cluster dendrogram using WGCNA analyzing RNA sequencing data. A total of 5 modules were identified after 0.25 threshold merging. (B) Module trait correlation analysis revealed that 5 key modules were significantly correlated with Heroin AW and Heroin PW. (C) The eigengene dendrogram and heatmap showed the relevancies of cytokines and identified RNA-seq modules. TNF-a, IL-2, IL-4, IL-10 and IL-17A were selected. (D) The correlation and connectivity of the turquoise module and TNF-a (r = 0.48, p = 2.6E-69). (E) The hub-gene network of turquoise module. The network of Top 50 lncRNAs/mRNAs (Rankings related to turquoise module) were showed. (F) The correlation and connectivity of the red module and IL-4 (r = 0.35, p = 1.1E-10). (G) The hub-gene network of red module. The network of Top 50 lncRNAs/mRNAs (Rankings related to red module) were showed. (H) The correlation and connectivity of the blue module and IL-10 (r = 0.59, p = 1.1E-45). (I) The hub-gene network of blue module. The network of Top 50 lncRNAs/mRNAs (Rankings related to blue module) were showed.

(A) (Left) Heat map representation of the relationship between module eigenproteins and different phenotypes of SAE. (Right upper) Synthetic eigenprotein analysis for the pink module, which is highly correlated with SAE patients, except the turquoise module. (Right bottom) Gene Ontology terms enriched in pink modules. (A, B) Similar to (A), association of network modules in sepsis-associated AKI. (Left) Heat map representation of the relationship between module eigenproteins and different phenotypes of AKI. (Right upper) Synthetic eigenprotein analysis for the black module, which is highly correlated with sepsis-associated AKI patients, except the turquoise module. (Right bottom) Gene ontology terms enriched in the black module. Data information: In (A, B), data are presented as median with IQR. ****P-value < 0.0001; ***P-value < 0.001; **P-value < 0.01; *P-value < 0.05 (Kruskal–Wallis test).

Each module (Module (A) PIK3CA module; Module (B) RB1 module; Module (C) CPSF1 module; Module (D) PRSS23 module; Module (E) MAPK12 module) is annotated with chromosome location, statistical significance between copy number and mRNA expression, and genomic status across ovarian cancer subtype one samples. represents gene expression correlates with copy number, as determined by ANOVA analysis across 222 ovarian cancer cases with copy number and expression data. represents the percentage of ovarian cancer cases in which gene is altered.

Cooperation types of correlated genes. Figure 3 shows the types of cooperative correlations between a module pair and its correlated genes predicted by our method. We predicted genes that mediate cooperative interactions between a pair of modules by evaluating the significance of the number of direct physical interactions between each module of a module pair and genes in the weighted interactions network (WPI network) with random networks (Figure 2). We used a yellow circle to indicate a gene and a green box to indicate a module. Consider a gene x in the WPI network and an identified module pair. If the number of protein-protein interactions (or regulatory interactions from ChIP-chip data) between x and a module is significant, the association was presented by a blue undirected line (or a red directed line) between the circle and a box. We identified five types of correlative associations: (A) A significant number of undirected links between x and genes in one of the modules and a significant number of directed links from genes in the other module to x in the WPI network. (B) A significant number of undirected links between x and genes in one of the modules and a significant number of directed links from x to genes in the other modules in the WPI network. (C) A significant number of undirected links between x and genes in each of the two modules in the WPI network. (D) A significant number of directed links from genes in each of the two modules to x in the WPI network. (E) A significant number of directed links from x to genes in each of the two modules in the WPI network.

Unsigned and Signed Mouse ES cell Networks in Ivanova et al. a, left, Dendrogram of the unsigned network of the Ivanova et al (2006) data set with color bands below indicating module membership for the unsigned network (U) and the signed network (S). a, right, heat map for visualizing standardized gene expressions (rows) across samples (columns) for genes in the turquoise module in the unsigned network. b, left, Dendrogram of the signed network of the Ivanova et al (2006) data set with color bands below indicating module membership for the signed network (S) and the unsigned network (U). b, right, heat map of expression profiles across samples for genes in the turquoise, black, and blue modules in the signed network. Note, modules are not scaled to reflect the number of genes in each module. c, scatter plot of module membership, k_ME, (x-axis) plotted against gene significance, GS, (y-axis) for the black and blue modules in the signed network with known ES cell regulators and differentiation genes labelled.

Each panel presents a scatterplot of module significance for HDL in one test set (y-axis) vs. module significance for HDL in the female CASTxB6 data (x-axis). The corresponding test data set is indicated in the title. Each point corresponds to a module represented by its eigengene. HDL-related module eigengenes are labeled by their number. These plots indicate that module significance for HDL is preserved in the CASTxB6 male, BxH female, HxB male and female, and MDP data. Module significance for HDL is not preserved in the BxH ApoE ^−/− female and male data, and in the BxH male data.

COPD disease network module, including experimentally determined FAM13A interactors, and gene-expression changes in COPD-specific data. (A) COPD disease network module connecting 11 seed genes including FAM13A. (B) Fold change difference between module differentially expressed genes (p < 0.05) and non-module differentially expressed genes.

Overview of the methodology developed in this study. (A) Construction of the weighted physical interaction network (WPI network). The WPI network was established from protein-protein interaction (PPI) data, cell cycle expression data, and genome-wide location (ChIP-chip) data. Each node of the network indicates a gene, and each link indicates an interaction between two genes according to protein-protein interaction data and ChIP-chip data. The weight of each node was estimated by the correlations of gene pairs. We first established a co-expression network of nodes corresponding to genes and links corresponding to gene pairs with a Pearson correlation above 0.683 or below -0.683, and then we used the degree of each node in the network as its weight. (B) Identification of cooperative module pairs and correlated genes in each pair. Significantly cooperative module pairs were identified in the WPI network by a spanning algorithm. For each cooperative module pair, genes significantly correlated with both modules were also reported. (C) Cooperative module network and phase-related cooperation analyses. In this step, we evaluated our results and analyzed the structure and properties of the network generated by the cooperative module pairs, their interactions through correlated phase-regulated genes, and crosstalk mediated by phase-specific regulators. In addition, functionally cooperative module pairs and relationships in each phase of the cell cycle were inferred.

Flow chart of the nQTL framework. It includes upstream nQTL identification and downstream biological and biomedical significance analyses of nQTL gene, network, and module. (A) Concept of nQTL compared to conventional eQTL. (B) Organization of input data for eQTL and nQTL analyses. (C) Computational model for eQTL and nQTL analyses. (D) The output of nQTL analysis as a network trait, which is different from the conventional expression/gene trait of eQTL analysis. (E) Downstream output of network signatures in the nQTL analysis framework, including nQTL module identification, biological enrichment of module, correlation between module and relevant factors, network structure of module, discriminative model, and Cox model based on the nQTL module.

A subnetwork highlighting disease module (AD seed module) characterized by both amyloidosis and tauopathy under the human protein-protein interactome model. The AD seed module includes 227 protein–protein interactions (PPIs) (edges or links) connecting 102 unique proteins (nodes).

Snapshots of outputs of module “data mapping and network analysis.” (A) Page showing link for filtered network and probability distribution function. (B) Filtered network. (C) Probability distribution function for network analysis. (D) Output page of a single omics data. (E) Network analysis page for multi-omics data. (F) Network of important interacting nodes. (G) Network of important nodes. (H) Network of pathway mapping.

The pipeline of our system, which includes data module, data analysis module, visual design module. Data module includes some work of data crawling and preprocessing, and stores the data in the database. Data analysis module includes three important parts, topic mining, sentiment network training and aspect-based sentiment analysis. Visual design module introduces the design principles of our visualization system and the use of visualization components

Network edges displayed in green and red indicate positive and negative hazard ratios, respectively, for the DNAm network correlation measure corresponding to that interaction; these correspond, respectively, to an increase and decrease in ‘network interaction co-ordinatedness’ for worse disease prognosis. (a) Wound healing module (KIRC). (b) Immune module (UCEC). (c) Mitochondial module (LUAD). (d) MAP-kinase module (LUSC). (e) Largest biologically significant subnetwork in the BLCA data set. (f) Largest biologically significant subnetwork in the BRCA data set. Further details about the corresponding network nodes (genes) and significantly enriched gene sets appear in – and –.

Module-Module Association Determination (M-MAD) reveals module connections. (A) Scheme of the M-MAD methodology in detecting module connections. Intermediate results of G-MAD for all modules are further processed and used as the basis of M-MAD. The −log₁₀(P) values of G-MAD for the target module against all genes in each data set are used as the gene statistic for the module, and connections between the target module and all modules are calculated using CAMERA. The results are then meta-analyzed by taking the sample sizes and inter-gene correlations of all data sets to compute the module-module association score (MMAS) between modules. (B) Module association network showing the connections across all modules. Colors of nodes represent the modules defined in the global module similarity network in B. Module clusters with respective colors are identified and labeled. Modules used as examples in the following figure panels are highlighted with a circle. (C) Comparison of pairwise module connections derived from module similarities in B and associations (from M-MAD) in B. A red dashed line is plotted when the pairwise module similarity equals association. The distributions of module similarity and association scores are illustrated in the top and at the right of the plot and are colored in red and blue, respectively. Two examples of novel module connections are encircled. (D,E) Subnetworks showing the association between mitochondrial and proteasomal modules (D), and mitochondrial and histone demethylation modules (E). Edge colors indicate the significance of module connections, with red as positive and blue as negative.