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.
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.
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.
Enables differential network analysis of genomic data. Differential Network Analysis is an R package that includes pre-processing tools, procedures for computing connectivity scores between pairs of genes based on several statistical techniques, tools for handling modules of genes based on these scores, procedures for performing permutation tests based on these scores. This software focuses on gene-gene networks, but the methods are adaptable for more general biological processes.
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.
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).
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.
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.