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annotate wgcna_eigengene_visualization.Rmd @ 3:240af4b07b92 draft

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planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit a23e23222252167ef7c3338a4872e84706df8f83-dirty
author mingchen0919
date Tue, 08 Aug 2017 15:06:26 -0400
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4275479ada3a planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
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1 ---
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2 title: 'WGCNA: eigengene visualization'
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3 output:
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4 html_document:
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5 number_sections: true
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6 toc: true
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7 theme: cosmo
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8 highlight: tango
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9 ---
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11 ```{r setup, include=FALSE, warning=FALSE, message=FALSE}
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12 knitr::opts_chunk$set(
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13 echo = ECHO
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14 )
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15 ```
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17 # Import workspace
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19 This step imports workspace from the **WGCNA: construct network** step.
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21 ```{r}
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22 fcp = file.copy("CONSTRUCT_NETWORK_WORKSPACE", "deseq.RData")
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23 load("deseq.RData")
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24 ```
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27 # Gene modules {.tabset}
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29 ```{r}
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30 if(!is.na(SOFT_THRESHOLD_POWER)) soft_threshold_power = SOFT_THRESHOLD_POWER
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31 ```
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33 ## Identify gene modules
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35 The gene network is constructed based on **soft threshold power = `r soft_threshold_power`**
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37 ```{r}
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38 gene_network = blockwiseModules(expression_data, power = soft_threshold_power,
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39 TOMType = "unsigned", minModuleSize = 30,
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40 reassignThreshold = 0, mergeCutHeight = 0.25,
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41 numericLabels = TRUE, pamRespectsDendro = FALSE,
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42 verbose = 3)
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43 ```
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46 ```{r}
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47 modules = table(gene_network$colors)
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48 n_modules = length(modules) - 1
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49 module_size_upper = modules[2]
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50 module_size_lower = modules[length(modules)]
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52 module_table = data.frame(model_label = c(0, 1:n_modules),
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53 gene_size = as.vector(modules))
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54 datatable(t(module_table))
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55 ```
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57 The results above indicates that there are **`r n_modules` gene modules**, labeled 1 through `r length(n_modules)` in order of descending size. The largest module has **`r module_size_upper` genes**, and the smallest module has **`r module_size_lower` genes**. The label 0 is reserved for genes outside of all modules.
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60 ## Dendrogram and module plot
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62 ```{r}
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63 # Convert labels to colors for plotting
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64 module_colors = labels2colors(gene_network$colors)
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65 # Plot the dendrogram and the module colors underneath
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66 plotDendroAndColors(gene_network$dendrograms[[1]], module_colors[gene_network$blockGenes[[1]]],
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67 "Module colors",
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68 dendroLabels = FALSE, hang = 0.03,
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69 addGuide = TRUE, guideHang = 0.05)
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70 ```
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73 # Gene module correlation
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75 We can calculate eigengenes and use them as representative profiles to quantify similarity of found gene modules.
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77 ```{r}
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78 n_genes = ncol(expression_data)
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79 n_samples = nrow(expression_data)
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80 ```
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82 ```{r}
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83 diss_tom = 1-TOMsimilarityFromExpr(expression_data, power = soft_threshold_power)
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84 set.seed(123)
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85 select_genes = sample(n_genes, size = PLOT_GENES)
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86 select_diss_tom = diss_tom[select_genes, select_genes]
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87
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88 # calculate gene tree on selected genes
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89 select_gene_tree = hclust(as.dist(select_diss_tom), method = 'average')
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90 select_module_colors = module_colors[select_genes]
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91
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92 # transform diss_tom with a power to make moderately strong connections more visiable in the heatmap.
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93 plot_diss_tom = select_diss_tom^7
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94 # set diagonal to NA for a nicer plot
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95 diag(plot_diss_tom) = NA
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96 ```
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97
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98
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99 ```{r fig.align='center'}
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100 TOMplot(plot_diss_tom, select_gene_tree, select_module_colors, main = "Network heatmap")
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101 ```
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102
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103
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104 # Eigengene visualization {.tabset}
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105
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106 ## Eigengene dendrogram
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107
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108 ```{r fig.align='center'}
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109 module_eigengenes = moduleEigengenes(expression_data, module_colors)$eigengenes
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110 plotEigengeneNetworks(module_eigengenes, "Eigengene dendrogram",
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111 plotHeatmaps = FALSE)
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112 ```
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113
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114 ## Eigengene adjacency heatmap
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115
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116 ```{r fig.align='center'}
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117 plotEigengeneNetworks(module_eigengenes, "Eigengene adjacency heatmap",
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118 marHeatmap = c(2, 3, 2, 2),
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119 plotDendrograms = FALSE, xLabelsAngle = 90)
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120 ```
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121