Quantitative assessment of gene expression network module-validation methods

Li, Bing; Zhang, Yingying; Yu, Yanan; Wang, Pengqian; Wang, Yongcheng; Wang, Zhong; Wang, Yongyan

doi:10.1038/srep15258

Cited by 18 publications

(17 citation statements)

References 102 publications

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“…A specificity score was introduced to identify more accurate biologically relevant and specific modules. Functionally homogeneity was earlier used to evaluate functional value of topological modules detected in biological networks [24,25] but failed to consider the heterogeneity of functional modules of biological networks due to a protein or gene mapping to a number of cellular processes. Thus, a set of proteins (in a module) are involved in more than one function and also a biological function is mapped to more than one module.…”

Section: Discussionmentioning

confidence: 99%

“…Usual approach to evaluate the functional significance of topological modules is to perform functional enrichment analysis and decide on the significantly enriched biological functions [21,23,24]. This approach is however inconclusive of determining functional coherence and specificity of topological modules [25]. In present work, we introduce a novel functional specificity measure that encompasses both functional homogeneity and heterogeneity of the topological modules.…”

Section: Introductionmentioning

confidence: 99%

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Functional homogeneity and specificity of topological modules in human proteome

Kaalia

Rajapakse

2019

BMC Bioinformatics

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Background: Functional modules in protein-protein interaction networks (PPIN) are defined by maximal sets of functionally associated proteins and are vital to understanding cellular mechanisms and identifying disease associated proteins. Topological modules of the human proteome have been shown to be related to functional modules of PPIN. However, the effects of the weights of interactions between protein pairs and the integration of physical (direct) interactions with functional (indirect expression-based) interactions have not been investigated in the detection of functional modules of the human proteome. Results: We investigated functional homogeneity and specificity of topological modules of the human proteome and validated them with known biological and disease pathways. Specifically, we determined the effects on functional homogeneity and heterogeneity of topological modules (i) with both physical and functional protein-protein interactions; and (ii) with incorporation of functional similarities between proteins as weights of interactions. With functional enrichment analyses and a novel measure for functional specificity, we evaluated functional relevance and specificity of topological modules of the human proteome. Conclusions: The topological modules ranked using specificity scores show high enrichment with gene sets of known functions. Physical interactions in PPIN contribute to high specificity of the topological modules of the human proteome whereas functional interactions contribute to high homogeneity of the modules. Weighted networks result in more number of topological modules but did not affect their functional propensity. Modules of human proteome are more homogeneous for molecular functions than biological processes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Functional homogeneity and specificity of topological modules in human proteome

Kaalia

Rajapakse

2019

BMC Bioinformatics

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“…Hence, 200 permutation tests were applied to assess the significance of the preservation statistics. Taking advantage of aggregating multiple preservation statistics into summary preservation statistics, all of the density and connectivity based preservation measures were summarized using three composite Z statistics Z density , Z connectivity and Z summary [ 40 , 41 ]. In case that modules are defined as clusters, the in group proportion (IGP) statistic, a benchmark statistic for module preservation assessment [ 42 ], was also calculated.…”

Section: Methodsmentioning

confidence: 99%

Bioinformatics analysis of common key genes and pathways of intracranial, abdominal, and thoracic aneurysms

Liu

et al. 2021

BMC Cardiovasc Disord

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Background Aneurysm is a severe and fatal disease. This study aims to comprehensively identify the highly conservative co-expression modules and hub genes in the abdominal aortic aneurysm (AAA), thoracic aortic aneurysm (TAA) and intracranial aneurysm (ICA) and facilitate the discovery of pathogenesis for aneurysm. Methods GSE57691, GSE122897, and GSE5180 microarray datasets were downloaded from the Gene Expression Omnibus database. We selected highly conservative modules using weighted gene co‑expression network analysis before performing the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway and Reactome enrichment analysis. The protein–protein interaction (PPI) network and the miRNA-hub genes network were constructed. Furtherly, we validated the preservation of hub genes in three other datasets. Results Two modules with 193 genes and 159 genes were identified as well preserved in AAA, TAA, and ICA. The enrichment analysis identified that these genes were involved in several biological processes such as positive regulation of cytosolic calcium ion concentration, hemostasis, and regulation of secretion by cells. Ten highly connected PPI networks were constructed, and 55 hub genes were identified. In the miRNA-hub genes network, CCR7 was the most connected gene, followed by TNF and CXCR4. The most connected miRNAs were hsa-mir-26b-5p and hsa-mir-335-5p. The hub gene module was proved to be preserved in all three datasets. Conclusions Our study highlighted and validated two highly conservative co-expression modules and miRNA-hub genes network in three kinds of aneurysms, which may promote understanding of the aneurysm and provide potential therapeutic targets and biomarkers of aneurysm.

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“…Andy et al has developed a spectral clustering approach for coexpression network partition [8]. Other co-expression network analysis methods partition the genes using algorithm such as k-mean or self-organizing maps [9,10]. These methods can efficiently identify large coexpression modules in a co-expression network and reflect the topological features of the large modules.…”

Section: Introductionmentioning

confidence: 99%

MRHCA: a nonparametric statistics based method for hub and co‐expression module identification in large gene co‐expression network

et al. 2018

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Background: Gene co-expression and differential co-expression analysis has been increasingly used to study cofunctional and co-regulatory biological mechanisms from large scale transcriptomics data sets. Methods: In this study, we develop a nonparametric approach to identify hub genes and modules in a large coexpression network with low computational and memory cost, namely MRHCA. Results: We have applied the method to simulated transcriptomics data sets and demonstrated MRHCA can accurately identify hub genes and estimate size of co-expression modules. With applying MRHCA and differential coexpression analysis to E. coli and TCGA cancer data, we have identified significant condition specific activated genes in E. coli and distinct gene expression regulatory mechanisms between the cancer types with high copy number variation and small somatic mutations. Conclusion: Our analysis has demonstrated MRHCA can (i) deal with large association networks, (ii) rigorously assess statistical significance for hubs and module sizes, (iii) identify co-expression modules with low associations, (iv) detect small and significant modules, and (v) allow genes to be present in more than one modules, compared with existing methods.Keywords: gene co-expression network; algorithm for large scale networks analysis; statistical significance of gene coexpression; Mutual Rank Author summary: MRHCA-a non-parametric co-expression analysis method for large association network is developed in this work. The advantage of MRHCA includes: (i) it can be generally applied to association networks of most assessment methods; (ii) it outputs exact significance level for each identified hub; (iii) the method are with relative small computational consumption that can be applied to large association networks; (iv) it is sensitive to the modules of weak associations; and (v) it enables overlapped modules in its outputs. R codes and a C package of the MRHCA are released by the following GitHub link: https://github.com/zy26/mrct. 40

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Quantitative assessment of gene expression network module-validation methods

Cited by 18 publications

References 102 publications

Functional homogeneity and specificity of topological modules in human proteome

Functional homogeneity and specificity of topological modules in human proteome

Bioinformatics analysis of common key genes and pathways of intracranial, abdominal, and thoracic aneurysms

MRHCA: a nonparametric statistics based method for hub and co‐expression module identification in large gene co‐expression network

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