Evidence map›Paper›PMID 30355288›Full record

ArticleBMC bioinformatics2018

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

Deisy Morselli Gysi, Andre Voigt, Tiago de Miranda Fragoso, Eivind Almaas, Katja Nowick

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Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

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  10. Noncoding RNAs improve the predictive power of network medicine.Proceedings of the National Academy of Sciences of the United States of America · 2023
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  15. Oral administration ofFrontiers in veterinary science · 2023
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  20. Gene Co-expression Network Analysis.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Deisy Morselli GysiDepartment of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, Haertelstrasse 16-18, Leipzig, 04109, Germany. deisy@bioinf.uni-leipzig.de.
Andre VoigtDepartment of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7049, Norway.
Tiago de Miranda FragosoFundação Cesgranrio, Rio de Janeiro, Rio de Janeiro, 20261-903, Brazil.
Eivind AlmaasDepartment of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, N-7049, Norway.
Katja NowickFreie Universität Berlin, Human Biology Group, Institute for Zoology, Department of Biology, Chemistry and Pharmacy, Königin-Luise-Straße 1-3, Berlin, D-14195, Germany.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico GDE 204111/2014-5
6 · The paper itself

Abstract

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. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network.

resultsHere, we present an R package for calculating the weighted topological overlap (wTO), that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. 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 biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. To graphically inspect the resulting networks, the R package contains a visualization tool, which allows for the direct network manipulation and access of node and link information. When testing the package on a standard laptop computer, we can conduct all calculations for systems of more than 20,000 genes in under two hours. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set.

conclusionIn this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL -2 Open Source License ( https://cran.r-project.org/web/packages/wTO/ ).

Indexed as

ConsensusGene Regulatory NetworksMetabolic Networks and PathwaysSoftwareAlgorithmsComputational BiologyEscherichia coliGene OntologyHumansMetagenomicsOceans and SeasROC CurveTime FactorsTranscription FactorsTranscription FactorsCo-expression networkConsensus NetworkCo-occurrence networkExpressionMeta analysisMetagenomicsNetworkR packageSoftwarewTO

Identifiers

PMID30355288
PMCPMC6201546

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.