Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design

Li, Jianqiang; Zhou, Doudou; Qiu, Weiliang; Shi, Yuliang; Yang, Ji-Jiang; Chen, Shi; Wang, Qing; Pan, Hui

doi:10.1038/s41598-017-18705-z

Cited by 130 publications

(114 citation statements)

References 29 publications

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“…DETs were grouped into 45 co-expression modules undergoing similar changes in their expression patterns. Unlike conventional clustering methods (such as k-means and hierarchical clustering), which are based on geometric distances, WGCNA is a graph-based approach relying on network topology as inferred from the correlation among expression values (Li et al, 2018). In our hands, the WGCNA algorithm robustly and accurately defined modules within a complex multi-condition dataset.…”

Section: Discussionmentioning

confidence: 99%

Motif analysis in co-expression networks reveals regulatory elements in plants: The peach as a model

Ksouri

Montardit-Tardà

et al. 2020

Preprint

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Identification of functional regulatory elements encoded in plant genomes is a fundamental need to understand gene regulation. While much attention has been given to model species as Arabidopsis thaliana, little is known about regulatory motifs in other plant genera. Here, we describe an accurate bottom-up approach using the online workbench RSAT::Plants for a versatile ab-initio motif discovery taking Prunus persica as a model. These predictions rely on the construction of a co-expression network to generate modules with similar expression trends and assess the effect of increasing upstream region length on the sensitivity of motif discovery. Applying two discovery algorithms, 18 out of 45 modules were found to be enriched in motifs typical of well-known transcription factor families (bHLH, bZip, BZR, CAMTA, DOF, E2FE, AP2-ERF, Myb-like, NAC, TCP, WRKY) and a novel motif. Our results indicate that small number of input sequences and short promoter length are preferential to minimize the amount of uninformative signals in peach. The spatial distribution of TF binding sites revealed an unbalanced distribution where motifs tend to lie around the transcriptional start site region. The reliability of this approach was also benchmarked in Arabidopsis thaliana, where it recovered the expected motifs from promoters of genes containing ChIPseq peaks. Overall, this paper presents a glimpse of the peach regulatory components at genome scale and provides a general protocol that can be applied to many other species. Additionally, a RSAT Docker container was released to facilitate similar analyses on other species or to reproduce our results.One sentence summaryMotifs prediction depends on the promoter size. A proximal promoter region defined as an interval of -500 bp to +200 bp seems to be the adequate stretch to predict de novo regulatory motifs in peach

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Section: Discussionmentioning

confidence: 99%

Motif analysis in co-expression networks reveals regulatory elements in plants: The peach as a model

Ksouri

Montardit-Tardà

et al. 2020

Preprint

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“…The weighted gene co-expression network analysis (WGCNA) package [18] in R was utilized to generate weighted correlation networks for the up-and downregulated genes. Brie y, gene clustering was performed using the expression matrix of the DEGs.…”

Section: Weighted Correlation Network Constructionmentioning

confidence: 99%

SPRR2C, DEFB4A, WIF1, CRY2, and KRT19 are correlated with the development of atopic eczema

Zhao

2020

Preprint

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BackgroundAtopic eczema (AE) is a chronic relapsing inflammatory skin disease. The objective of this study was to identify key genes related to the development of AE.MethodsThe GSE6012 dataset was obtained from the Gene Expression Omnibus (GEO) database. The limma package was used to analyze differentially expressed genes (DEGs). Then, the weighted gene co-expression network analysis (WGCNA) package was utilized to generate weighted correlation networks of up- and downregulated genes. Additionally, the WGCNA package was used for enrichment analyses to explore the underlying functions of DEGs in modules (weighted correlation sub-networks) significantly associated with AE.ResultsA total of 515 DEGs were identified between lesional and non-lesional skin samples. For the upregulated genes, the blue module was found to have a significant positive correlation with AE. Importantly, small proline-rich protein 2C (SPRR2C) and defensin, beta 4A (DEFB4A) exhibited higher |log fold change (FC)| values and were the key nodes of the network. Moreover, KEGG pathway analysis revealed that the upregulated genes in the blue module were primarily involved in cytokine-cytokine receptor interaction. Additionally, for the downregulated genes, the brown module was found to have a significant positive correlation with AE. Further, WNT inhibitory factor 1 (WIF1), cryptochrome 2 (CRY2), and keratin 19 (KRT19) had higher |log FC| values and were key nodes of the network.ConclusionSPRR2C, DEFB4A, WIF1, CRY2, KRT19 and cytokine-cytokine receptor interaction might be correlated with the development of AE.

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“…An approach to identify these co-expression subnetworks is to mine frequent subgraphs over multiple gene expression networks. Careful study of these frequent subgraphs can lead to the identification of functional modules and the discovery of significant genes interactions playing key roles in complex diseases [6].…”

Section: Introductionmentioning

confidence: 99%

RASMA: A Reverse Search Algorithm for Mining Frequent Subgraphs

Salem¹,

Alokshiya²,

Ma³

2020

Preprint

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Background: Mining frequent co-expression networks enables the discovery of interesting network motifs that elucidate important interactions among genes. Such interaction subnetworks have been shown to enhance the discovery of biological modules and subnetwork signatures for gene expression and disease classification. Results: We propose a reverse search algorithm for mining frequent and maximal subgraphs over a collection of graphs. We develop an approach for enumerating connected edge-induced subgraphs of an undirected graph by using a reverse-search algorithm, and then use this enumeration strategy for mining all maximal frequent subgraphs. To overcome the computationally prohibitive task of enumerating all frequent subgraphs while mining for maximal subgraphs, the proposed algorithm employs several pruning strategies, which substantially improve its overall runtime performance. Experimental results show that on large gene coexpression networks, the proposed algorithm efficiently mines biologically relevant maximal frequent subgraphs. Conclusion: Extracting recurrent gene coexpression subnetworks from multiple gene expression experiments enables the discovery of functional modules and subnetwork biomarkers. We have proposed a reverse search algorithm for mining maximal frequent subnetworks. Enrichment analysis of the extracted maximal frequent subnetworks reveals that subnetworks that are more frequent are more likely to be enriched with biological ontologies.

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Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design

Cited by 130 publications

References 29 publications

Motif analysis in co-expression networks reveals regulatory elements in plants: The peach as a model

Motif analysis in co-expression networks reveals regulatory elements in plants: The peach as a model

SPRR2C, DEFB4A, WIF1, CRY2, and KRT19 are correlated with the development of atopic eczema

RASMA: A Reverse Search Algorithm for Mining Frequent Subgraphs

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