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WGCNA: an R package for weighted correlation network analysis

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Alireza Doustmohammadi

This document presents an overview of weighted correlation network analysis (WGCNA), an R package used to identify clusters (modules) of highly correlated genes in a biological network. It describes the main steps of WGCNA, including data preprocessing, constructing a weighted correlation network, identifying modules of co-expressed genes, relating modules to external traits, studying relationships between modules, and finding key driver genes. The goal is to discover how groups of interacting genes work together to impact phenotypic traits.

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Biological Sciences faculty Biophysics Department WGCNA: an R package for weighted correlation network analysis Presented By Alireza Doustmohammadi Graduate Student in Bioinformatics December 2019 WGCNA Tarbiat Modares University 1 of 37
Contents Introduction weighted network Analysis 2 of 37WGCNA Tarbiat Modares University
Introduction 3 of 37 weighted correlation network: gene โ€“ gene similarity network WGCNA Tarbiat Modares University
Introduction 4 of 37 Identify modules : [Daniel H. Geschwind & Genevieve Konopka. Neuroscience in the era of functional genomics and systems biology, Nature 461, 908-915] WGCNA Tarbiat Modares University Gene group A Gene group C Gene group B
Introduction 5 of 37WGCNA Tarbiat Modares University weight glucose insulin Blood pressure cholesterol ๏ถ Relation modules and clinical traits ๏ถ Identify driver genes
Introduction 6 of 37WGCNA Tarbiat Modares University Data Cleaning & Preprocessing Construct weighted correlation network Identify modules Relate modules to external information Study module relationships Find the key drivers in interesting modules WGCNA workflow:
Introduction 7 of 37 Data description: WGCNA Tarbiat Modares University livers of female & male mouse of a specific F2 intercross [https://www.slideserve.com/cais/statistical-methods-for-quantitative-trait-loci-qtl-mapping] livers of female mouse of a specific F2 intercross livers of male mouse of a specific F2 intercross Clinical Traits Gene Annotation [https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/ Rpackages/WGCNA/Tutorials/index.html]
Data PreprocessingConstruct Network Data Cleaning & Preprocessing WGCNA 8 of 37WGCNA Tarbiat Modares University Preprocessing: ๏ƒผ Missing values ๏ƒผ Outlier microarray samples Modules Analysis
Data Cleaning & Preprocessing WGCNA 9 of 37WGCNA Tarbiat Modares University Preprocessing: ๏ƒผ Missing values Data PreprocessingConstruct NetworkModules Analysis
Data input and cleaning WGCNA 10 of 37WGCNA Tarbiat Modares University Preprocessing: ๏ƒผ Outlier microarray samples Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network WGCNA 11 of 37WGCNA Tarbiat Modares University ๏ƒผ Automatic, one-step network construction ๏ƒผ Step-by-step network construction ๏ƒผ block-wise network construction WGCNA can uses parallel computation. Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network WGCNA 12 of 37WGCNA Tarbiat Modares University correlation matrix adjacency matrix Weighted correlation network Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network WGCNA 13 of 37WGCNA Tarbiat Modares University Construct adjacency matrix: Strong correlation weak correlation Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network WGCNA 14 of 37WGCNA Tarbiat Modares University Construct weighted correlation network: gene 1 gene 2 gene 3 gene 4 gene 1 1 0.55 0.39 0.09 gene 2 0.55 1 0.48 0.11 gene 3 0.39 0.48 1 0.21 gene 4 0.09 0.11 0.21 1 Adjacency matrix fully connected network 0.09 0.21 0.48 0.55 0.11 0.39 Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network WGCNA 15 of 37WGCNA Tarbiat Modares University Objective function: Pick lowest possible that leads to an approximately scale-free network topology [https://www.researchgate.net/figure/Scale-Free-Network-Left-Power-Law-Degree-Distribution-curve-Right-on-log-log-scale_fig1_310261624] [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559] Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network WGCNA 16 of 37WGCNA Tarbiat Modares University Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 17 of 37WGCNA Tarbiat Modares University Modules = co-express genes ๏ƒผ Topological Overlap Measure(TOM) = Similarity between genes Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 18 of 37WGCNA Tarbiat Modares University ๐‘ช๐’๐’Ž๐’‘๐’–๐’•๐’† ๐’…๐’Š๐’”๐’”๐’Š๐’Ž๐’Š๐’๐’‚๐’“๐’Š๐’•๐’š ๐’ƒ๐’†๐’•๐’˜๐’†๐’†๐’ ๐’ˆ๐’†๐’๐’†๐’”: Topological Overlap Measure (TOM) matrix: [https://www.researchgate.net/post/What_do_adjacency_matrix_and_Topology_Overlap_Matrix_from_WGCNA_package_tell_about_the_data] Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 19 of 37WGCNA Tarbiat Modares University ๐‘ช๐’๐’Ž๐’‘๐’–๐’•๐’† ๐’…๐’Š๐’”๐’”๐’Š๐’Ž๐’Š๐’๐’‚๐’“๐’Š๐’•๐’š ๐’ƒ๐’†๐’•๐’˜๐’†๐’†๐’ ๐’ˆ๐’†๐’๐’†๐’”: Topological Overlap Measure (TOM) matrix: [https://link.springer.com/article/10.3786/s12859-019-2598-7] Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 20 of 37WGCNA Tarbiat Modares University Perform hierarchical clustering of genes Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 21 of 37WGCNA Tarbiat Modares University Divide clustered genes into modules ๏ƒผ Fix height branch cut ๏ƒผ Dynamic Tree Cut [https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/BranchCutting/Supplement.pdf] Data PreprocessingConstruct NetworkModules Analysis
Identify modules : Divide clustered genes into modules WGCNA 22 of 37WGCNA Tarbiat Modares University Dynamic Tree Cut algorithm [https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/BranchCutting/Supplement.pdf] Data PreprocessingConstruct NetworkModules Analysis
Identify modules : Divide clustered genes into modules WGCNA 22 of 37WGCNA Tarbiat Modares University Dynamic Tree Cut [https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/BranchCutting/Supplement.pdf] 0.2 0.3 0.4 0.4 0.5 0.6 0.7 0.8 Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 23 of 37WGCNA Tarbiat Modares University Divide clustered genes into modules 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 88 614 316 311 257 235 225 212 158 153 121 106 102 100 94 91 78 76 65 58 58 48 34 modules Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 24 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ eigengene 1st principal component of the expression data 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 88 614 316 311 257 235 225 212 158 153 121 106 102 100 94 91 78 76 65 58 58 48 34 modules [https://www.quora.com/What-are-eigengenes-and-gene-modules] Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 25 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ Calculate eigengene โ€ข Example: module z eigengene genA genB gen C โ€ฆ.. gen N S1 S2 S3 โ€ฆ.. S123 PC 1 S1 S2 S3 โ€ฆ.. S123 PCA Module z eigengene Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 26 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ Modules eigengene Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 27 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ Perform hierarchical clustering of eigengenes Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 28 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ Merge modules Data PreprocessingConstruct NetworkModules Analysis
Identify modules WGCNA 29 of 37WGCNA Tarbiat Modares University Compare before and after Merge very similar modules cluster 0 1 2 3 5 7 8 9 10 11 13 14 15 16 17 18 20 21 22 88 614 316 311 460 212 158 410 223 106 100 94 91 78 76 123 58 48 34 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 88 614 316 311 257 235 225 212 158 153 121 106 102 100 94 91 78 76 65 58 58 48 34 Dynamic tree cut modules Merged dynamic modules Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network & Identify modules WGCNA 30 of 37WGCNA Tarbiat Modares University ๏ƒผ Step-by-step network construction ๏ƒผ Automatic, one-step network construction ๏ƒผ block-wise network construction Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network & Identify modules WGCNA 31 of 37WGCNA Tarbiat Modares University ๏ƒผ Step-by-step network construction ๏ƒผ Automatic, one-step network construction ๏ƒผ block-wise network construction Relation between block size and memory space: Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network & Identify modules WGCNA 32 of 37WGCNA Tarbiat Modares University block-wise network construction: ๏ƒผ Split block Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network & Identify modules WGCNA 33 of 37WGCNA Tarbiat Modares University block-wise network construction: Data PreprocessingConstruct NetworkModules Analysis
Construct weighted correlation network WGCNA 34 of 37WGCNA Tarbiat Modares University block-wise network construction 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 88 614 316 311 257 235 225 212 158 153 121 106 102 100 94 91 78 76 65 58 58 48 34 Single block 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 148 448 474 538 305 270 130 210 142 120 110 98 142 81 77 78 65 42 34 88 2 blocks Data PreprocessingConstruct NetworkModules Analysis
Relate modules to external information WGCNA 35 of 37WGCNA Tarbiat Modares University Data PreprocessingConstruct NetworkModules Analysis
Study module relationships WGCNA 36 of 37WGCNA Tarbiat Modares University Data PreprocessingConstruct NetworkModules Analysis
Find the key drivers in interesting modules WGCNA 37 of 37WGCNA Tarbiat Modares University Data PreprocessingConstruct NetworkModules Analysis

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WGCNA: an R package for weighted correlation network analysis

  • 1. Biological Sciences faculty Biophysics Department WGCNA: an R package for weighted correlation network analysis Presented By Alireza Doustmohammadi Graduate Student in Bioinformatics December 2019 WGCNA Tarbiat Modares University 1 of 37
  • 2. Contents Introduction weighted network Analysis 2 of 37WGCNA Tarbiat Modares University
  • 3. Introduction 3 of 37 weighted correlation network: gene โ€“ gene similarity network WGCNA Tarbiat Modares University
  • 4. Introduction 4 of 37 Identify modules : [Daniel H. Geschwind & Genevieve Konopka. Neuroscience in the era of functional genomics and systems biology, Nature 461, 908-915] WGCNA Tarbiat Modares University Gene group A Gene group C Gene group B
  • 5. Introduction 5 of 37WGCNA Tarbiat Modares University weight glucose insulin Blood pressure cholesterol ๏ถ Relation modules and clinical traits ๏ถ Identify driver genes
  • 6. Introduction 6 of 37WGCNA Tarbiat Modares University Data Cleaning & Preprocessing Construct weighted correlation network Identify modules Relate modules to external information Study module relationships Find the key drivers in interesting modules WGCNA workflow:
  • 7. Introduction 7 of 37 Data description: WGCNA Tarbiat Modares University livers of female & male mouse of a specific F2 intercross [https://www.slideserve.com/cais/statistical-methods-for-quantitative-trait-loci-qtl-mapping] livers of female mouse of a specific F2 intercross livers of male mouse of a specific F2 intercross Clinical Traits Gene Annotation [https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/ Rpackages/WGCNA/Tutorials/index.html]
  • 8. Data PreprocessingConstruct Network Data Cleaning & Preprocessing WGCNA 8 of 37WGCNA Tarbiat Modares University Preprocessing: ๏ƒผ Missing values ๏ƒผ Outlier microarray samples Modules Analysis
  • 9. Data Cleaning & Preprocessing WGCNA 9 of 37WGCNA Tarbiat Modares University Preprocessing: ๏ƒผ Missing values Data PreprocessingConstruct NetworkModules Analysis
  • 10. Data input and cleaning WGCNA 10 of 37WGCNA Tarbiat Modares University Preprocessing: ๏ƒผ Outlier microarray samples Data PreprocessingConstruct NetworkModules Analysis
  • 11. Construct weighted correlation network WGCNA 11 of 37WGCNA Tarbiat Modares University ๏ƒผ Automatic, one-step network construction ๏ƒผ Step-by-step network construction ๏ƒผ block-wise network construction WGCNA can uses parallel computation. Data PreprocessingConstruct NetworkModules Analysis
  • 12. Construct weighted correlation network WGCNA 12 of 37WGCNA Tarbiat Modares University correlation matrix adjacency matrix Weighted correlation network Data PreprocessingConstruct NetworkModules Analysis
  • 13. Construct weighted correlation network WGCNA 13 of 37WGCNA Tarbiat Modares University Construct adjacency matrix: Strong correlation weak correlation Data PreprocessingConstruct NetworkModules Analysis
  • 14. Construct weighted correlation network WGCNA 14 of 37WGCNA Tarbiat Modares University Construct weighted correlation network: gene 1 gene 2 gene 3 gene 4 gene 1 1 0.55 0.39 0.09 gene 2 0.55 1 0.48 0.11 gene 3 0.39 0.48 1 0.21 gene 4 0.09 0.11 0.21 1 Adjacency matrix fully connected network 0.09 0.21 0.48 0.55 0.11 0.39 Data PreprocessingConstruct NetworkModules Analysis
  • 15. Construct weighted correlation network WGCNA 15 of 37WGCNA Tarbiat Modares University Objective function: Pick lowest possible that leads to an approximately scale-free network topology [https://www.researchgate.net/figure/Scale-Free-Network-Left-Power-Law-Degree-Distribution-curve-Right-on-log-log-scale_fig1_310261624] [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559] Data PreprocessingConstruct NetworkModules Analysis
  • 16. Construct weighted correlation network WGCNA 16 of 37WGCNA Tarbiat Modares University Data PreprocessingConstruct NetworkModules Analysis
  • 17. Identify modules WGCNA 17 of 37WGCNA Tarbiat Modares University Modules = co-express genes ๏ƒผ Topological Overlap Measure(TOM) = Similarity between genes Data PreprocessingConstruct NetworkModules Analysis
  • 18. Identify modules WGCNA 18 of 37WGCNA Tarbiat Modares University ๐‘ช๐’๐’Ž๐’‘๐’–๐’•๐’† ๐’…๐’Š๐’”๐’”๐’Š๐’Ž๐’Š๐’๐’‚๐’“๐’Š๐’•๐’š ๐’ƒ๐’†๐’•๐’˜๐’†๐’†๐’ ๐’ˆ๐’†๐’๐’†๐’”: Topological Overlap Measure (TOM) matrix: [https://www.researchgate.net/post/What_do_adjacency_matrix_and_Topology_Overlap_Matrix_from_WGCNA_package_tell_about_the_data] Data PreprocessingConstruct NetworkModules Analysis
  • 19. Identify modules WGCNA 19 of 37WGCNA Tarbiat Modares University ๐‘ช๐’๐’Ž๐’‘๐’–๐’•๐’† ๐’…๐’Š๐’”๐’”๐’Š๐’Ž๐’Š๐’๐’‚๐’“๐’Š๐’•๐’š ๐’ƒ๐’†๐’•๐’˜๐’†๐’†๐’ ๐’ˆ๐’†๐’๐’†๐’”: Topological Overlap Measure (TOM) matrix: [https://link.springer.com/article/10.3786/s12859-019-2598-7] Data PreprocessingConstruct NetworkModules Analysis
  • 20. Identify modules WGCNA 20 of 37WGCNA Tarbiat Modares University Perform hierarchical clustering of genes Data PreprocessingConstruct NetworkModules Analysis
  • 21. Identify modules WGCNA 21 of 37WGCNA Tarbiat Modares University Divide clustered genes into modules ๏ƒผ Fix height branch cut ๏ƒผ Dynamic Tree Cut [https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/BranchCutting/Supplement.pdf] Data PreprocessingConstruct NetworkModules Analysis
  • 22. Identify modules : Divide clustered genes into modules WGCNA 22 of 37WGCNA Tarbiat Modares University Dynamic Tree Cut algorithm [https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/BranchCutting/Supplement.pdf] Data PreprocessingConstruct NetworkModules Analysis
  • 23. Identify modules : Divide clustered genes into modules WGCNA 22 of 37WGCNA Tarbiat Modares University Dynamic Tree Cut [https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/BranchCutting/Supplement.pdf] 0.2 0.3 0.4 0.4 0.5 0.6 0.7 0.8 Data PreprocessingConstruct NetworkModules Analysis
  • 24. Identify modules WGCNA 23 of 37WGCNA Tarbiat Modares University Divide clustered genes into modules 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 88 614 316 311 257 235 225 212 158 153 121 106 102 100 94 91 78 76 65 58 58 48 34 modules Data PreprocessingConstruct NetworkModules Analysis
  • 25. Identify modules WGCNA 24 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ eigengene 1st principal component of the expression data 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 88 614 316 311 257 235 225 212 158 153 121 106 102 100 94 91 78 76 65 58 58 48 34 modules [https://www.quora.com/What-are-eigengenes-and-gene-modules] Data PreprocessingConstruct NetworkModules Analysis
  • 26. Identify modules WGCNA 25 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ Calculate eigengene โ€ข Example: module z eigengene genA genB gen C โ€ฆ.. gen N S1 S2 S3 โ€ฆ.. S123 PC 1 S1 S2 S3 โ€ฆ.. S123 PCA Module z eigengene Data PreprocessingConstruct NetworkModules Analysis
  • 27. Identify modules WGCNA 26 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ Modules eigengene Data PreprocessingConstruct NetworkModules Analysis
  • 28. Identify modules WGCNA 27 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ Perform hierarchical clustering of eigengenes Data PreprocessingConstruct NetworkModules Analysis
  • 29. Identify modules WGCNA 28 of 37WGCNA Tarbiat Modares University Merge very similar modules ๏ƒผ Merge modules Data PreprocessingConstruct NetworkModules Analysis
  • 30. Identify modules WGCNA 29 of 37WGCNA Tarbiat Modares University Compare before and after Merge very similar modules cluster 0 1 2 3 5 7 8 9 10 11 13 14 15 16 17 18 20 21 22 88 614 316 311 460 212 158 410 223 106 100 94 91 78 76 123 58 48 34 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 88 614 316 311 257 235 225 212 158 153 121 106 102 100 94 91 78 76 65 58 58 48 34 Dynamic tree cut modules Merged dynamic modules Data PreprocessingConstruct NetworkModules Analysis
  • 31. Construct weighted correlation network & Identify modules WGCNA 30 of 37WGCNA Tarbiat Modares University ๏ƒผ Step-by-step network construction ๏ƒผ Automatic, one-step network construction ๏ƒผ block-wise network construction Data PreprocessingConstruct NetworkModules Analysis
  • 32. Construct weighted correlation network & Identify modules WGCNA 31 of 37WGCNA Tarbiat Modares University ๏ƒผ Step-by-step network construction ๏ƒผ Automatic, one-step network construction ๏ƒผ block-wise network construction Relation between block size and memory space: Data PreprocessingConstruct NetworkModules Analysis
  • 33. Construct weighted correlation network & Identify modules WGCNA 32 of 37WGCNA Tarbiat Modares University block-wise network construction: ๏ƒผ Split block Data PreprocessingConstruct NetworkModules Analysis
  • 34. Construct weighted correlation network & Identify modules WGCNA 33 of 37WGCNA Tarbiat Modares University block-wise network construction: Data PreprocessingConstruct NetworkModules Analysis
  • 35. Construct weighted correlation network WGCNA 34 of 37WGCNA Tarbiat Modares University block-wise network construction 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 88 614 316 311 257 235 225 212 158 153 121 106 102 100 94 91 78 76 65 58 58 48 34 Single block 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 148 448 474 538 305 270 130 210 142 120 110 98 142 81 77 78 65 42 34 88 2 blocks Data PreprocessingConstruct NetworkModules Analysis
  • 36. Relate modules to external information WGCNA 35 of 37WGCNA Tarbiat Modares University Data PreprocessingConstruct NetworkModules Analysis
  • 37. Study module relationships WGCNA 36 of 37WGCNA Tarbiat Modares University Data PreprocessingConstruct NetworkModules Analysis
  • 38. Find the key drivers in interesting modules WGCNA 37 of 37WGCNA Tarbiat Modares University Data PreprocessingConstruct NetworkModules Analysis