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      CEMiTool: a Bioconductor package for performing comprehensive modular co-expression analyses

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          Abstract

          Background

          The analysis of modular gene co-expression networks is a well-established method commonly used for discovering the systems-level functionality of genes. In addition, these studies provide a basis for the discovery of clinically relevant molecular pathways underlying different diseases and conditions.

          Results

          In this paper, we present a fast and easy-to-use Bioconductor package named CEMiTool that unifies the discovery and the analysis of co-expression modules. Using the same real datasets, we demonstrate that CEMiTool outperforms existing tools, and provides unique results in a user-friendly html report with high quality graphs. Among its features, our tool evaluates whether modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group, as well as it integrates transcriptomic data with interactome information, identifying the potential hubs on each network. We successfully applied CEMiTool to over 1000 transcriptome datasets, and to a new RNA-seq dataset of patients infected with Leishmania, revealing novel insights of the disease’s physiopathology.

          Conclusion

          The CEMiTool R package provides users with an easy-to-use method to automatically implement gene co-expression network analyses, obtain key information about the discovered gene modules using additional downstream analyses and retrieve publication-ready results via a high-quality interactive report.

          Electronic supplementary material

          The online version of this article (10.1186/s12859-018-2053-1) contains supplementary material, which is available to authorized users.

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          Most cited references18

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          A random variance model for detection of differential gene expression in small microarray experiments.

          Microarray techniques provide a valuable way of characterizing the molecular nature of disease. Unfortunately expense and limited specimen availability often lead to studies with small sample sizes. This makes accurate estimation of variability difficult, since variance estimates made on a gene by gene basis will have few degrees of freedom, and the assumption that all genes share equal variance is unlikely to be true. We propose a model by which the within gene variances are drawn from an inverse gamma distribution, whose parameters are estimated across all genes. This results in a test statistic that is a minor variation of those used in standard linear models. We demonstrate that the model assumptions are valid on experimental data, and that the model has more power than standard tests to pick up large changes in expression, while not increasing the rate of false positives. This method is incorporated into BRB-ArrayTools version 3.0 (http://linus.nci.nih.gov/BRB-ArrayTools.html). ftp://linus.nci.nih.gov/pub/techreport/RVM_supplement.pdf
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules

            Background Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns. Results We present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset. Conclusions DiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.
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              Guidance for RNA-seq co-expression network construction and analysis: safety in numbers.

              RNA-seq co-expression analysis is in its infancy and reasonable practices remain poorly defined. We assessed a variety of RNA-seq expression data to determine factors affecting functional connectivity and topology in co-expression networks. We examine RNA-seq co-expression data generated from 1970 RNA-seq samples using a Guilt-By-Association framework, in which genes are assessed for the tendency of co-expression to reflect shared function. Minimal experimental criteria to obtain performance on par with microarrays were >20 samples with read depth >10 M per sample. While the aggregate network constructed shows good performance (area under the receiver operator characteristic curve ∼0.71), the dependency on number of experiments used is nearly identical to that present in microarrays, suggesting thousands of samples are required to obtain 'gold-standard' co-expression. We find a major topological difference between RNA-seq and microarray co-expression in the form of low overlaps between hub-like genes from each network due to changes in the correlation of expression noise within each technology. jgillis@cshl.edu or sballouz@cshl.edu Networks are available at: http://gillislab.labsites.cshl.edu/supplements/rna-seq-networks/ and supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

                Author and article information

                Contributors
                pedro.russo@usp.br
                gustavo.rodrigues.ferreira@usp.br
                lucas.cardozo@usp.br
                burger@usp.br
                raul.ibi2013@gmail.com
                srmaruyama@gmail.com
                thiagodch@gmail.com
                diogenes.lima@usp.br
                fmarcon@usp.br
                ferreirafk@gmail.com
                melissalever@usp.br
                jsdsilva@fmrp.usp.br
                maracaja.coutinho@gmail.com
                hnakaya@usp.br
                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                20 February 2018
                20 February 2018
                2018
                : 19
                : 56
                Affiliations
                [1 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Department of Clinical and Toxicological Analyses, , School of Pharmaceutical Sciences, University of São Paulo, ; São Paulo, SP 05508-900 Brazil
                [2 ]ISNI 0000 0004 0385 4466, GRID grid.443909.3, Advanced Center for Chronic Diseases (ACCDiS), Facultad de Ciencias Químicas y Farmacéuticas, , Universidad de Chile, ; Santiago, Chile
                [3 ]ISNI 0000 0004 1937 0722, GRID grid.11899.38, Department of Biochemistry, Immunology, and Cell Biology, , University of São Paulo, ; Ribeirão Preto, São Paulo Brazil
                Article
                2053
                10.1186/s12859-018-2053-1
                5819234
                29458351
                731a2095-fc62-4c40-846b-cb95e244a609
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 20 September 2017
                : 7 February 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo;
                Award ID: 2012/19278-6
                Award ID: 2013/08216-2
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003593, Conselho Nacional de Desenvolvimento Científico e Tecnológico;
                Award ID: 443719/2014-4
                Award ID: 305985/2014-0
                Award Recipient :
                Categories
                Software
                Custom metadata
                © The Author(s) 2018

                Bioinformatics & Computational biology
                co-expression modules,gene networks,modular analysis,leishmaniasis,transcriptomics

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