Assessment of Subnetwork Detection Methods for Breast Cancer

Jiang, Biaobin; Gribskov, Michael

doi:10.4137/cin.s17641

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…Significant pathways that are identified by different clustering methods often yield tens or hundreds of genes, making biological interpretation and validation challenging. Further, many clustering techniques such as Dynamic Tree Cut utilized in WGCNA rely on usersettable parameters, including minimum module size, and are sensitive to cluster splitting [8,9]. While many of these module detection methods perform optimally on some datasets, they may fail to effectively detect patterns in other datasets.…”

Section: Introductionmentioning

confidence: 99%

Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice

et al. 2020

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Background: mRNA interaction with other mRNAs and other signaling molecules determine different biological pathways and functions. Gene co-expression network analysis methods have been widely used to identify correlation patterns between genes in various biological contexts (e.g., cancer, mouse genetics, yeast genetics). A challenge remains to identify an optimal partition of the networks where the individual modules (clusters) are neither too small to make any general inferences, nor too large to be biologically interpretable. Clustering thresholds for identification of modules are not systematically determined and depend on usersettable parameters requiring optimization. The absence of systematic threshold determination may result in suboptimal module identification and a large number of unassigned features. Results: In this study, we propose a new pipeline to perform gene co-expression network analysis. The proposed pipeline employs WGCNA, a software widely used to perform different aspects of gene coexpression network analysis, and Modularity Maximization algorithm, to analyze novel RNA-Seq data to understand the effects of low-dose 56 Fe ion irradiation on the formation of hepatocellular carcinoma in mice. The network results, along with experimental validation, show that using WGCNA combined with Modularity Maximization, provides a more biologically interpretable network in our dataset, than that obtainable using WGCNA alone. The proposed pipeline showed better performance than the existing clustering algorithm in WGCNA, and identified a module that was biologically validated by a mitochondrial complex I assay. Conclusions: We present a pipeline that can reduce the problem of parameter selection that occurs with the existing algorithm in WGCNA, for applicable RNA-Seq datasets. This may assist in the future discovery of novel mRNA interactions, and elucidation of their potential downstream molecular effects.

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Section: Introductionmentioning

confidence: 99%

Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice

et al. 2020

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“…As a consequence, comparative studies on medically relevant data have reported poor consistency between individual methods ( Jiang and Gribskov, 2014 ) and questioned the merit of applying subnetwork-based methods altogether ( Staiger et al , 2013 )…”

Section: Introductionmentioning

confidence: 99%

Network-based analysis of omics data: the LEAN method

et al. 2016

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MotivationMost computational approaches for the analysis of omics data in the context of interaction networks have very long running times, provide single or partial, often heuristic, solutions and/or contain user-tuneable parameters.ResultsWe introduce local enrichment analysis (LEAN) for the identification of dysregulated subnetworks from genome-wide omics datasets. By substituting the common subnetwork model with a simpler local subnetwork model, LEAN allows exact, parameter-free, efficient and exhaustive identification of local subnetworks that are statistically dysregulated, and directly implicates single genes for follow-up experiments.Evaluation on simulated and biological data suggests that LEAN generally detects dysregulated subnetworks better, and reflects biological similarity between experiments more clearly than standard approaches. A strong signal for the local subnetwork around Von Willebrand Factor (VWF), a gene which showed no change on the mRNA level, was identified by LEAN in transcriptome data in the context of the genetic disease Cerebral Cavernous Malformations (CCM). This signal was experimentally found to correspond to an unexpected strong cellular effect on the VWF protein. LEAN can be used to pinpoint statistically significant local subnetworks in any genome-scale dataset.Availability and ImplementationThe R-package LEANR implementing LEAN is supplied as supplementary material and available on CRAN (https://cran.r-project.org).Supplementary information Supplementary data are available at Bioinformatics online.

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“…It has recently been demonstrated that model-based biomarker based on the activity of the JNK pathway robustly stratified neuroblastoma patients across different molecular backgrounds [ 3 ]. Computational models have already been used to provide an understanding of the dynamics of one or a few specific signaling pathways [ 16 – 18 ], however, the availability of comprehensive pathway-wide models [ 5 ] that transform decontextualized transcriptomics gene expression data into signaling activities, which in turn trigger cell functions that can be linked to cancer hallmarks, provide a quantitative framework to identify neuroblastoma functional drivers. Thus, we were not only able to reproduce the results of previous modeling studies that linked the inability of activating the JNK pathway to neuroblastoma bad prognostic but also to discover the pathways upstream responsible of its inhibition.…”

Section: Discussionmentioning

confidence: 99%

Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome

et al. 2018

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BackgroundDespite the progress in neuroblastoma therapies the mortality of high-risk patients is still high (40–50%) and the molecular basis of the disease remains poorly known. Recently, a mathematical model was used to demonstrate that the network regulating stress signaling by the c-Jun N-terminal kinase pathway played a crucial role in survival of patients with neuroblastoma irrespective of their MYCN amplification status. This demonstrates the enormous potential of computational models of biological modules for the discovery of underlying molecular mechanisms of diseases.ResultsSince signaling is known to be highly relevant in cancer, we have used a computational model of the whole cell signaling network to understand the molecular determinants of bad prognostic in neuroblastoma. Our model produced a comprehensive view of the molecular mechanisms of neuroblastoma tumorigenesis and progression.ConclusionWe have also shown how the activity of signaling circuits can be considered a reliable model-based prognostic biomarker.ReviewersThis article was reviewed by Tim Beissbarth, Wenzhong Xiao and Joanna Polanska. For the full reviews, please go to the Reviewers’ comments section.Electronic supplementary materialThe online version of this article (10.1186/s13062-018-0219-4) contains supplementary material, which is available to authorized users.

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Assessment of Subnetwork Detection Methods for Breast Cancer

Cited by 9 publications

References 40 publications

Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice

Efficient identification of multiple pathways: RNA-Seq analysis of livers from 56Fe ion irradiated mice

Network-based analysis of omics data: the LEAN method

Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome

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