RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations.

Castro, Mauro A. A.; Wang, Xin; Fletcher, Michael; Meyer, Kerstin B.; Markowetz, Florian

doi:10.1186/gb-2012-13-4-r29

Cited by 92 publications

(80 citation statements)

References 13 publications

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“…The BLCA regulator-target associations are inferred using the R package RTN (Castro et al, 2016b), which is extensively described elsewhere for reconstructing regulatory units for transcriptions factors and upstream regulators (Campbell et al, 2016; Castro et al, 2016a; Fletcher et al, 2013). Briefly, gene expression matrices for a set of samples are used to estimate the associations between a regulator and all potential targets.…”

Section: Star Methodsmentioning

confidence: 99%

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Robertson¹,

Kim²,

Al‐Ahmadie³

et al. 2017

Cell

1,696

1,624

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Summary We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival ‘neuronal’ subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, lncRNA, and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma-in-situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

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Section: Star Methodsmentioning

confidence: 99%

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Robertson¹,

Kim²,

Al‐Ahmadie³

et al. 2017

Cell

1,696

1,624

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“…RedeR is an R package interfaces with Java core engine to construct nested and hierarchical biological networks to address modularity [3]. In order to organize and manage hierarchical association, the data structure emulates mixed graphs in two layers and multiple levels.…”

Section: Redermentioning

confidence: 99%

“…In order to achieve this we selected networking algorithms, RedeR [3], Weighted correlation network analysis (WGCNA) [11] and Statistical Inference for MOdular Networks (SIMoNe) [4] which aim to detect modular groups from microarray gene expression data.…”

Section: Network Analysismentioning

confidence: 99%

“…In order to address this concern we selected three networking algorithms with hierarchical topology for a comparative study. These methods are weighted correlation network analysis (WGCNA) [11], RedeR [3] and statistical inference of modular networks (SIMoNe) [4]. WGCNA and RedeR provide a simplistic way for detecting functional modules mapped to external traits in hierarchical layout.…”

Section: Introductionmentioning

confidence: 99%

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Comparative analysis of network algorithms to address modularity with gene expression temporal data

Mohammed

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

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In recent years, hierarchical networks have received comparatively less attention to explore microarray gene expression data although hierarchical modularity of biological networks has been demonstrated. We compare three networking algorithms for the study of complex biological network modularity: RedeR, weighted correlation network analysis (WGCNA) and statistical inference of modular networks (SIMoNe). Our main contributions in this work include a filtering process, which filters out non-differentially expressed genes and a novel score for performance measurement. We show in this paper how the performance of algorithms can be improved using this filtering process.

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“…It is an environment dedicated for statistical computing and graphics involving linear and non-linear modeling, classification, and clustering of the obtained data. Its application through Bioconductor involves several packages like HTqPCR for HT quantitative real-time PCR assays, MassSpecWavelet, and PROcess for mass spectrometric data processing, VegaMC for comparative genome hybridization datasets (Morganella and Ceccarelli, 2012), easyRNAseq for RNA sequence datasets (Delhomme et al, 2012), RedeR (Castro et al, 2012), and graphite (Sales et al, 2012) for biological networks amongst others.…”

Section: Bioinformatics In Systems Biologymentioning

confidence: 99%

Systems Biology, Bioinformatics, and Biomarkers in Neuropsychiatry

Alawieh¹,

Zaraket²,

Li³

et al. 2012

Front. Neurosci.

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Although neuropsychiatric (NP) disorders are among the top causes of disability worldwide with enormous financial costs, they can still be viewed as part of the most complex disorders that are of unknown etiology and incomprehensible pathophysiology. The complexity of NP disorders arises from their etiologic heterogeneity and the concurrent influence of environmental and genetic factors. In addition, the absence of rigid boundaries between the normal and diseased state, the remarkable overlap of symptoms among conditions, the high inter-individual and inter-population variations, and the absence of discriminative molecular and/or imaging biomarkers for these diseases makes difficult an accurate diagnosis. Along with the complexity of NP disorders, the practice of psychiatry suffers from a “top-down” method that relied on symptom checklists. Although checklist diagnoses cost less in terms of time and money, they are less accurate than a comprehensive assessment. Thus, reliable and objective diagnostic tools such as biomarkers are needed that can detect and discriminate among NP disorders. The real promise in understanding the pathophysiology of NP disorders lies in bringing back psychiatry to its biological basis in a systemic approach which is needed given the NP disorders’ complexity to understand their normal functioning and response to perturbation. This approach is implemented in the systems biology discipline that enables the discovery of disease-specific NP biomarkers for diagnosis and therapeutics. Systems biology involves the use of sophisticated computer software “omics”-based discovery tools and advanced performance computational techniques in order to understand the behavior of biological systems and identify diagnostic and prognostic biomarkers specific for NP disorders together with new targets of therapeutics. In this review, we try to shed light on the need of systems biology, bioinformatics, and biomarkers in neuropsychiatry, and illustrate how the knowledge gained through these methodologies can be translated into clinical use providing clinicians with improved ability to diagnose, manage, and treat NP patients.

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RedeR: R/Bioconductor package for representing modular structures, nested networks and multiple levels of hierarchical associations.

Cited by 92 publications

References 13 publications

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Comparative analysis of network algorithms to address modularity with gene expression temporal data

Systems Biology, Bioinformatics, and Biomarkers in Neuropsychiatry

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