wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool

@article{Gysi2018wTOAR,
  title={wTO: an R package for computing weighted topological overlap and a consensus network with integrated visualization tool},
  author={Deisy Morselli Gysi and Andr{\'e} Voigt and Tiago de Miranda Fragoso and Eivind Almaas and Katja Nowick},
  journal={BMC Bioinformatics},
  year={2018},
  volume={19}
}
BackgroundNetwork analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. [] Key Method The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and…

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References

SHOWING 1-10 OF 87 REFERENCES

WGCNA: an R package for weighted correlation network analysis

The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.

Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks

Application of these techniques can allow a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes.

Comparing Statistical Methods for Constructing Large Scale Gene Networks

Different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed are compared to aid in choosing statistical methods for constructing large scale GRNs.

A General Framework for Weighted Gene Co-Expression Network Analysis

  • Bin ZhangS. Horvath
  • Computer Science
    Statistical applications in genetics and molecular biology
  • 2005
A general framework for `soft' thresholding that assigns a connection weight to each gene pair is described and several node connectivity measures are introduced and provided empirical evidence that they can be important for predicting the biological significance of a gene.

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information

The package minet provides a series of tools for inferring transcriptional networks from microarray data and integrates accuracy assessment tools, like F-scores, PR-curves and ROC-Curves in order to compare the inferred network with a reference one.

Integrating high-throughput and computational data elucidates bacterial networks

This model is able not only to predict the outcomes of high-throughput growth phenotyping and gene expression experiments, but also to indicate knowledge gaps and identify previously unknown components and interactions in the regulatory and metabolic networks.

Reverse engineering cellular networks

A computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data, which envision that predictions made by ARACne, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks.

RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond

The semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for ‘neighborhood’ genes to known operons and regulons, and computational developments are described.

On Mining Biological Signals Using Correlation Networks

The results show that the majority (81-100%) of genes in any given cluster will share at least one predicted transcription factor binding site, and new regulatory relationships can be proposed using known transcription factors and their binding sites by integrating regulatory information and the network model itself.
...