Differential network data analysis software tools | Protein interaction
Analogous to differential expression analysis, differential network analysis involves pairwise subtraction of interactions that have been mapped in different experimental conditions. The subtractive process filters out ubiquitous interactions (so‑called ‘housekeeping’ interactions) that are common to all static conditions of interest. By selectively extracting interactions that are relevant to the studied condition or phenotype, this reduces the typical complexity of static networks.
A caBIG (cancer Biomedical Informatics Grid) analytical tool for detecting and visualizing statistically significant topological changes in transcriptional networks representing two biological conditions. DDN enables differential network analysis and provides an alternative way for defining network biomarkers predictive of phenotypes. DDN also serves as a useful systems biology tool for users across biomedical research communities to infer how genetic, epigenetic or environment variables may affect biological networks and clinical phenotypes.
Quantifies the variability in the co-regulation probability of genes across tissues or conditions. DINA is an approach based on detecting differences in the number of edges among genes in a pathway across a set of networks. It can be applied to any kind of network, independently of how this is generated. This method is also able to predict which transcription factors (TFs) may be responsible for the condition-specific coregulation.
A pathway-based differential network analysis in genomics model for estimating group-specific networks as well as making inference on the differential networks. DINGO jointly estimates the group-specific conditional dependencies by decomposing them into global and group-specific components. The delineation of these components allows for a more refined picture of the major driver and passenger events in the elucidation of cancer progression and development.
A Cytoscape plug-in that visualizes differences among multiple networks. DyNet can be used for analysing how networks change over time or across multiple conditions (dynamic networks). DyNet utilizes Cytoscape’s own built-in network data structure, so there is no need for a specialized file format. Users can just import multiple networks separately as they would normally do and quickly use DyNet to highlight and identify differences that are present. In addition, DyNet also introduces a new method to highlight nodes that are most ‘rewired’. It takes into account actual changes in nodes’ connectivity (their connections to each neighbor separately). Therefore, this method can identify a node that is more strongly connected to different neighbors in different networks, even if its degree or the sum of its edge weights stays the same.
A Cytoscape app to learn biological network topology and its changes using profiling data and domain knowledge. It takes input data and runs KDDN algorithm to construct the dependency network. When two conditions data are available, KDDN identifies the statistically significant condition-specific edges to provide insights into system dynamics.
Aims to explore and analyze dynamic networks. TVNViewer is a program designed for the visualization of small to moderate datasets of up to 500 nodes for gene-gene interactions. It can handle up to 5000 nodes classified by up to 100 descriptors and supports three visualization paradigms: (1) a gene-gene interaction paradigm; (2) a two-tiered gene-gene interaction paradigm; and (3) a descriptor paradigm where nodes are grouped according to a descriptor category.
Improves the understanding of complex molecular interactions and disease mechanisms for integrative analysis, differential network analysis, and community detection. xMWAS recognizes and displays associations between genes, cytokines, and metabolites. It is based on existing algorithms and provides an automated framework for integrative and differential network analysis of up to four datasets from unpaired or paired study designs.
Allows users to estimate group-specific dependencies. iDINGO is an R program and consists of an extension of DINGO package. It integrates relationships between different omics levels in the analysis using a chain graph model. It assists users in the investigation of the differential network between the integrative dependencies for random variables from multiple platforms.
Serves for dynamic network construction analysis and visualization. DyNetViewer allows users to observe how the network changes over time. Node centrality analysis and cluster analysis can also be implemented for dynamic networks in this tool. Moreover, users can add to this program dynamic network construction algorithms, centrality analysis algorithms and cluster analysis algorithms.
Identifies differential patterns of network activation between condition-specific groups. JDINAC is an R application that as many advantages. It can (1) achieve differential network analysis and classification simultaneously, (2) adjust confounding factors in the differential network analysis, and (3) it is a nonparametric approach and can identify the nonlinear relationship among variables. Besides, it does not require any conditions on the distribution of the data, which makes it more robust.
Builds a sparse differential network based on partial correlation for better visualization, and integrates differential expression (DE) and differential network (DN) analyses for biomarker discovery. INDEED includes four steps: (i) performing DE analysis to obtain p-value for each biomolecule, (ii) building a differential network, (iii) computing the activity score for each biomolecule and, (iv) prioritizing the biomolecules with the activity score. Future work includes developing an R package and extending it to integrate multiple omic data of various types for biomarker discovery.
Reveals hidden patterns of plant signaling dynamics. DiNAR is a program that presents three main functions: dynamic visualisation of complex multi-conditional experiments, identification of strong differential interactions, and recall of latent effects that are present in multi-conditional experiments. Furthermore, it can manage other background knowledge networks in combination with experimental dataset of interest such as transcriptomics, proteomics, metabolomics.
A package for conducting a differential analysis of networks constructed from microarray data under two experimental settings. Examining changes in network structure can provide valuable information about the underlying biochemical pathways. Differential network analysis with appropriate connectivity scores is a useful tool in exploring changes in network structures under different biological conditions.
A generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies.
Detects significantly rewired interactions between samples of two groups of condition-specific protein-protein interaction networks (PPINs). PPICompare is a differential PPIN tool that statistically determines significant between-group rewiring events and annotates each rewiring process with the underlying cause. It also constructs a small set of the most relevant alterations to the transcriptome that explain all systematic differences in the networks. It is designed to be used as an extension to PPIXpress, but can also be applied to suitable input data generated in alternative ways.
Infers together gene regulatory networks (GRNs) under two conditions, and then identifies difference of the two GRN. FSSEM integrates genetic perturbations with gene expression data under two different conditions with the structural equation model. It was used for the analysis of a gene expression and single nucleotide polymorphism (SNP) dataset of lung cancer and normal lung tissues and identified a differential GRN, whose genes with largest degrees were reported to be implicated in lung cancer.
Performs differential network analysis to infer disease specific gene networks. dc3net is a package that infers direct physical interactions of differential gene networks from gene expression datasets of multiple conditions. DC3NET algorithm is based on the very conservative gene regulatory network (GNR) inference algorithm C3NET.
Provides an implementation of statistically significant differential sub-network analysis for paired biological networks. DiffNet performs differential network analysis for paired biological networks to identify statistically significant changes between two graphs. This approach available for doing this includes the "closed-form", "original" (dGHD) and the "fast-approx" techniques. The method works better for large-scale complex biological networks (in pairs).