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weighted gene co-expression network analysis (wgcna) package 26  (RStudio)

 
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    RStudio weighted gene co-expression network analysis (wgcna) package 26
    Weighted Gene Co Expression Network Analysis (Wgcna) Package 26, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/weighted+gene+co-expression+network+analysis+(wgcna)+package/weighted+gene+co+expression+network+analysis++wgcna++package/pmc09712610__41467_2022_35095_MOESM2_ESM-289-24-33
    Average 90 stars, based on 1 article reviews
    weighted gene co-expression network analysis (wgcna) package 26 - by Bioz Stars, 2026-10
    90/100 stars

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    Related Articles

    Construct:

    Article Title: Comparative Transcriptomic Profiles of Differentiated Adipocytes Provide Insights into Adipogenesis Mechanisms of Subcutaneous and Intramuscular Fat Tissues in Pigs
    Article Snippet: All annotated genes (FPKM > 0.01) were analyzed using the weighted gene co-expression network analysis (WGCNA) package in R studio software.

    Article Title: Analysis of lncRNA-Associated ceRNA Network Reveals Potential lncRNA Biomarkers in Human Colon Adenocarcinoma.
    Article Snippet: Results of OS are shown by Kaplan-Meier survival curves. mRNA targets of key lncRNAs related to survival We used the Weighted Gene Co-expression Network Analysis (WGCNA) package in R Studio (R version 3.4.1) to build a co-expression network to analyze the potential mRNA targets of lncRNAs.

    Article Title: Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
    Article Snippet: As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects., In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/web/packages/WGCNA/index.html ) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues
    Article Snippet: The parameters for WGCNA were set as follows (in previous manuscript): A total of 277 PRR-signaling-pathway-related proteins were clustered into functional modules using a weighted gene co-expression network analysis (WGCNA) package 26 in RStudio.

    Article Title: Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation
    Article Snippet: As a systematic bioinformatics analysis method, we used the Rstudio weighted gene co-expression network analysis (WGCNA) package ( https://cran.r-project.org/web/packages/WGCNA/ ) in WGCNA.

    Article Title: <p>Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression</p>
    Article Snippet: Construction of Weighted Gene Coexpression Networks As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects.21,22 In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/ web/packages/WGCNA/index.html) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Astragaloside IV attenuates podocyte apoptosis through ameliorating mitochondrial dysfunction by up-regulated Nrf2-ARE/TFAM signaling in diabetic kidney disease.
    Article Snippet: To screen the target gene regulated by DA treatment more accurately, transcriptome data were processed using the weighted gene co-expression network analysis (WGCNA) package of R-studio software.24,41 The genes relevant to the group traits were specially screened and the co-expression modules characterized as biological significance was obtained by this algorithm.

    Expressing:

    Article Title: Comparative Transcriptomic Profiles of Differentiated Adipocytes Provide Insights into Adipogenesis Mechanisms of Subcutaneous and Intramuscular Fat Tissues in Pigs
    Article Snippet: All annotated genes (FPKM > 0.01) were analyzed using the weighted gene co-expression network analysis (WGCNA) package in R studio software.

    Article Title: Analysis of lncRNA-Associated ceRNA Network Reveals Potential lncRNA Biomarkers in Human Colon Adenocarcinoma.
    Article Snippet: Results of OS are shown by Kaplan-Meier survival curves. mRNA targets of key lncRNAs related to survival We used the Weighted Gene Co-expression Network Analysis (WGCNA) package in R Studio (R version 3.4.1) to build a co-expression network to analyze the potential mRNA targets of lncRNAs.

    Article Title: Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
    Article Snippet: As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects., In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/web/packages/WGCNA/index.html ) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues
    Article Snippet: The parameters for WGCNA were set as follows (in previous manuscript): A total of 277 PRR-signaling-pathway-related proteins were clustered into functional modules using a weighted gene co-expression network analysis (WGCNA) package 26 in RStudio.

    Article Title: Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation
    Article Snippet: As a systematic bioinformatics analysis method, we used the Rstudio weighted gene co-expression network analysis (WGCNA) package ( https://cran.r-project.org/web/packages/WGCNA/ ) in WGCNA.

    Article Title: <p>Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression</p>
    Article Snippet: Construction of Weighted Gene Coexpression Networks As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects.21,22 In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/ web/packages/WGCNA/index.html) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Astragaloside IV attenuates podocyte apoptosis through ameliorating mitochondrial dysfunction by up-regulated Nrf2-ARE/TFAM signaling in diabetic kidney disease.
    Article Snippet: To screen the target gene regulated by DA treatment more accurately, transcriptome data were processed using the weighted gene co-expression network analysis (WGCNA) package of R-studio software.24,41 The genes relevant to the group traits were specially screened and the co-expression modules characterized as biological significance was obtained by this algorithm.

    Generated:

    Article Title: Comparative Transcriptomic Profiles of Differentiated Adipocytes Provide Insights into Adipogenesis Mechanisms of Subcutaneous and Intramuscular Fat Tissues in Pigs
    Article Snippet: All annotated genes (FPKM > 0.01) were analyzed using the weighted gene co-expression network analysis (WGCNA) package in R studio software.

    Article Title: Analysis of lncRNA-Associated ceRNA Network Reveals Potential lncRNA Biomarkers in Human Colon Adenocarcinoma.
    Article Snippet: Results of OS are shown by Kaplan-Meier survival curves. mRNA targets of key lncRNAs related to survival We used the Weighted Gene Co-expression Network Analysis (WGCNA) package in R Studio (R version 3.4.1) to build a co-expression network to analyze the potential mRNA targets of lncRNAs.

    Article Title: Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
    Article Snippet: As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects., In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/web/packages/WGCNA/index.html ) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues
    Article Snippet: The parameters for WGCNA were set as follows (in previous manuscript): A total of 277 PRR-signaling-pathway-related proteins were clustered into functional modules using a weighted gene co-expression network analysis (WGCNA) package 26 in RStudio.

    Article Title: Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation
    Article Snippet: As a systematic bioinformatics analysis method, we used the Rstudio weighted gene co-expression network analysis (WGCNA) package ( https://cran.r-project.org/web/packages/WGCNA/ ) in WGCNA.

    Article Title: <p>Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression</p>
    Article Snippet: Construction of Weighted Gene Coexpression Networks As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects.21,22 In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/ web/packages/WGCNA/index.html) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Astragaloside IV attenuates podocyte apoptosis through ameliorating mitochondrial dysfunction by up-regulated Nrf2-ARE/TFAM signaling in diabetic kidney disease.
    Article Snippet: To screen the target gene regulated by DA treatment more accurately, transcriptome data were processed using the weighted gene co-expression network analysis (WGCNA) package of R-studio software.24,41 The genes relevant to the group traits were specially screened and the co-expression modules characterized as biological significance was obtained by this algorithm.

    Isolation:

    Article Title: Comparative Transcriptomic Profiles of Differentiated Adipocytes Provide Insights into Adipogenesis Mechanisms of Subcutaneous and Intramuscular Fat Tissues in Pigs
    Article Snippet: All annotated genes (FPKM > 0.01) were analyzed using the weighted gene co-expression network analysis (WGCNA) package in R studio software.

    Article Title: Analysis of lncRNA-Associated ceRNA Network Reveals Potential lncRNA Biomarkers in Human Colon Adenocarcinoma.
    Article Snippet: Results of OS are shown by Kaplan-Meier survival curves. mRNA targets of key lncRNAs related to survival We used the Weighted Gene Co-expression Network Analysis (WGCNA) package in R Studio (R version 3.4.1) to build a co-expression network to analyze the potential mRNA targets of lncRNAs.

    Article Title: Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
    Article Snippet: As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects., In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/web/packages/WGCNA/index.html ) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues
    Article Snippet: The parameters for WGCNA were set as follows (in previous manuscript): A total of 277 PRR-signaling-pathway-related proteins were clustered into functional modules using a weighted gene co-expression network analysis (WGCNA) package 26 in RStudio.

    Article Title: Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation
    Article Snippet: As a systematic bioinformatics analysis method, we used the Rstudio weighted gene co-expression network analysis (WGCNA) package ( https://cran.r-project.org/web/packages/WGCNA/ ) in WGCNA.

    Article Title: <p>Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression</p>
    Article Snippet: Construction of Weighted Gene Coexpression Networks As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects.21,22 In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/ web/packages/WGCNA/index.html) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Astragaloside IV attenuates podocyte apoptosis through ameliorating mitochondrial dysfunction by up-regulated Nrf2-ARE/TFAM signaling in diabetic kidney disease.
    Article Snippet: To screen the target gene regulated by DA treatment more accurately, transcriptome data were processed using the weighted gene co-expression network analysis (WGCNA) package of R-studio software.24,41 The genes relevant to the group traits were specially screened and the co-expression modules characterized as biological significance was obtained by this algorithm.

    Software:

    Article Title: Comparative Transcriptomic Profiles of Differentiated Adipocytes Provide Insights into Adipogenesis Mechanisms of Subcutaneous and Intramuscular Fat Tissues in Pigs
    Article Snippet: All annotated genes (FPKM > 0.01) were analyzed using the weighted gene co-expression network analysis (WGCNA) package in R studio software.

    Article Title: Analysis of lncRNA-Associated ceRNA Network Reveals Potential lncRNA Biomarkers in Human Colon Adenocarcinoma.
    Article Snippet: Results of OS are shown by Kaplan-Meier survival curves. mRNA targets of key lncRNAs related to survival We used the Weighted Gene Co-expression Network Analysis (WGCNA) package in R Studio (R version 3.4.1) to build a co-expression network to analyze the potential mRNA targets of lncRNAs.

    Article Title: Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
    Article Snippet: As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects., In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/web/packages/WGCNA/index.html ) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Integrated proteomic and transcriptomic landscape of macrophages in mouse tissues
    Article Snippet: The parameters for WGCNA were set as follows (in previous manuscript): A total of 277 PRR-signaling-pathway-related proteins were clustered into functional modules using a weighted gene co-expression network analysis (WGCNA) package 26 in RStudio.

    Article Title: Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation
    Article Snippet: As a systematic bioinformatics analysis method, we used the Rstudio weighted gene co-expression network analysis (WGCNA) package ( https://cran.r-project.org/web/packages/WGCNA/ ) in WGCNA.

    Article Title: <p>Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression</p>
    Article Snippet: Construction of Weighted Gene Coexpression Networks As a systems biology method, the construction of gene coexpression networks and the identification of gene clusters or modules is especially useful in identifying transcriptional alterations in multigene diseases, where the phenotypic state emerges from the convergence of numerous small changes, rather than from isolated single-gene effects.21,22 In the present study, the Weighted Gene Co-expression Network Analysis (WGCNA) package (Version 1.68, https://cran.rstudio.com/ web/packages/WGCNA/index.html) within R software was used to construct groups of strongly coexpressed genes into coexpression networks according to the DEG expression matrix, which included 3276 selected genes.

    Article Title: Astragaloside IV attenuates podocyte apoptosis through ameliorating mitochondrial dysfunction by up-regulated Nrf2-ARE/TFAM signaling in diabetic kidney disease.
    Article Snippet: To screen the target gene regulated by DA treatment more accurately, transcriptome data were processed using the weighted gene co-expression network analysis (WGCNA) package of R-studio software.24,41 The genes relevant to the group traits were specially screened and the co-expression modules characterized as biological significance was obtained by this algorithm.



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    The constructed gene co-expression modules of CRSwNP by <t>WGCNA</t> in R. (A) Gene-module tree diagram. Each branch represents one gene, and every color below represents one co-expression module. (B) Module-trait relationship diagram. 14 modules were generated; the MEgreen (RS = 0.53, P = 3e-06) and MEturquoise (RS = −0.74, P = 2e-13) modules were most significantly related to CRSwNP.
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    The constructed gene co-expression modules of CRSwNP by WGCNA in R. (A) Gene-module tree diagram. Each branch represents one gene, and every color below represents one co-expression module. (B) Module-trait relationship diagram. 14 modules were generated; the MEgreen (RS = 0.53, P = 3e-06) and MEturquoise (RS = −0.74, P = 2e-13) modules were most significantly related to CRSwNP.

    Journal: Frontiers in Molecular Biosciences

    Article Title: Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation

    doi: 10.3389/fmolb.2022.843580

    Figure Lengend Snippet: The constructed gene co-expression modules of CRSwNP by WGCNA in R. (A) Gene-module tree diagram. Each branch represents one gene, and every color below represents one co-expression module. (B) Module-trait relationship diagram. 14 modules were generated; the MEgreen (RS = 0.53, P = 3e-06) and MEturquoise (RS = −0.74, P = 2e-13) modules were most significantly related to CRSwNP.

    Article Snippet: As a systematic bioinformatics analysis method, we used the Rstudio weighted gene co-expression network analysis (WGCNA) package ( https://cran.r-project.org/web/packages/WGCNA/ ) in WGCNA.

    Techniques: Construct, Expressing, Generated