Mercurial > repos > mingchen0919 > rmarkdown_wgcna

annotate wgcna_construct_network.Rmd @ 2:237210176a2b 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 14:33:59 -0400
parents 4275479ada3a
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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 ---
4275479ada3a planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
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2 title: 'WGCNA: construct network'
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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: preprocessing** step.
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21 ```{r}
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22 fcp = file.copy("PREPROCESSING_WORKSPACE", "deseq.RData")
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23 load("deseq.RData")
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24 ```
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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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27 # Processing outliers {.tabset}
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29 ## Before removing outliers
4275479ada3a planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
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31 ```{r}
4275479ada3a planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
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32 plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="", cex.lab = 1.5,
4275479ada3a planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
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33 cex.axis = 1, cex.main = 1, cex = 0.5)
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34 if(!is.na(HEIGHT_CUT)) {
4275479ada3a planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
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35 # plot a line to show the cut
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36 abline(h = HEIGHT_CUT, col = "red")
4275479ada3a planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
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37 # determine cluster under the line
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38 clust = cutreeStatic(sampleTree, cutHeight = HEIGHT_CUT, minSize = 10)
4275479ada3a planemo upload for repository https://github.com/statonlab/docker-GRReport/tree/master/my_tools/rmarkdown_wgcna commit d91f269e8bc09a488ed2e005122bbb4a521f44a0-dirty
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39 keepSamples = (clust==1)
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40 expression_data = expression_data[keepSamples, ]
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41 }
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42 ```
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44 ## After removing outliers
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46 ```{r}
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47 sampleTree = hclust(dist(expression_data), method = "average");
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48 plot(sampleTree, main = "Sample clustering to detect outliers", sub="", xlab="",
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49 cex.axis = 1, cex.main = 1, cex = 0.5)
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50 ```
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53 # Trait data {.tabeset}
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54
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55 If trait data is provided, the first 100 rows from the data will be displayed here. A plot consisting of sample cluster dendrogram and trait heatmap will also be gerenated.
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57 ## Trait data table
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58
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59 ```{r}
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60 trait_data = data.frame()
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61 if ("TRAIT_DATA" != 'None') {
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62 trait_data = read.csv("TRAIT_DATA", header = TRUE, row.names = 1)
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63 # form a data frame analogous to expression data that will hold the traits.
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64 sample_names = rownames(expression_data)
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65 trait_rows = match(sample_names, rownames(trait_data))
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66 trait_data = trait_data[trait_rows, ]
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67 datatable(head(trait_data, 100), style="bootstrap", filter = 'top',
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68 class="table-condensed", options = list(dom = 'tp', scrollX = TRUE))
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69 }
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70 ```
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71
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72 ## Dendrogram and heatmap
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73
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74 ```{r fig.align='center', fig.width=8, fig.height=9}
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75 if (nrow(trait_data) != 0) {
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76 traitColors = numbers2colors(trait_data, signed = FALSE)
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77 plotDendroAndColors(sampleTree, traitColors,
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78 groupLabels = names(trait_data),
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79 main = "Sample dendrogram and trait heatmap",
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80 cex.dendroLabels = 0.5)
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81 }
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82 ```
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83
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84
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85 # The thresholding power
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86
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87 ```{r}
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88 powers = c(1:10, seq(12, 20, 2))
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89 soft_threshold = pickSoftThreshold(expression_data, powerVector = powers, verbose = 5)
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90 ```
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91
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92 ```{r fig.align='center'}
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93 par(mfrow=c(1,2))
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94 plot(soft_threshold$fitIndices[,1], -sign(soft_threshold$fitIndices[,3])*soft_threshold$fitIndices[,2],
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95 xlab="Soft Threshold (power)",
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96 ylab="Scale Free Topology Model Fit,signed R^2",type="n",
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97 main = paste("Scale independence"),
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98 cex.lab = 0.5);
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99 text(soft_threshold$fitIndices[,1], -sign(soft_threshold$fitIndices[,3])*soft_threshold$fitIndices[,2],
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100 labels=powers,cex=0.5,col="red");
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101
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102 # calculate soft threshold power
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103 y = -sign(soft_threshold$fitIndices[,3])*soft_threshold$fitIndices[,2]
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104 r2_cutoff = 0.9
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105 for(i in 1:length(powers)) {
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106 if(y[i] > r2_cutoff) {
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107 soft_threshold_power = soft_threshold$fitIndices[,1][i]
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108 r2_cutoff_new = y[i]
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109 break
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110 }
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111 soft_threshold_power = soft_threshold$fitIndices[,1][length(powers)]
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112 }
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113 abline(h=r2_cutoff, col="red")
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114 abline(v=soft_threshold_power, col="blue")
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115 text(soft_threshold_power+1, r2_cutoff-0.1,
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116 paste0('R^2 cutoff = ', round(r2_cutoff_new,2)),
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117 cex = 0.5, col = "red")
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118
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119 plot(soft_threshold$fitIndices[,1], soft_threshold$fitIndices[,5],
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120 xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
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121 main = paste("Mean connectivity"),
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122 cex.lab = 0.5)
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123 text(soft_threshold$fitIndices[,1], soft_threshold$fitIndices[,5], labels=powers, cex=0.5,col="red")
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124 par(mfrow=c(1,1))
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125 ```
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126
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127
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128 # Construct network
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129
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130 The gene network is constructed based on **soft threshold power = `r soft_threshold_power`**
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131
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132 ```{r}
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133 gene_network = blockwiseModules(expression_data, power = soft_threshold_power,
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134 TOMType = "unsigned", minModuleSize = 30,
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135 reassignThreshold = 0, mergeCutHeight = 0.25,
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136 numericLabels = TRUE, pamRespectsDendro = FALSE,
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137 verbose = 3)
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138 ```
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139
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140
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141 # Gene modules {.tabset}
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142
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143 ## Idenfity gene modules
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144
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145 ```{r}
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146 modules = table(gene_network$colors)
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147 n_modules = length(modules) - 1
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148 module_size_upper = modules[2]
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149 module_size_lower = modules[length(modules)]
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150
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151 module_table = data.frame(model_label = c(0, 1:n_modules),
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152 gene_size = as.vector(modules))
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153 datatable(t(module_table))
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154 ```
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155
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156 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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157
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158
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159 ## Dendrogram and module plot
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160
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161 ```{r}
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162 # Convert labels to colors for plotting
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163 module_colors = labels2colors(gene_network$colors)
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164 # Plot the dendrogram and the module colors underneath
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165 plotDendroAndColors(gene_network$dendrograms[[1]], module_colors[gene_network$blockGenes[[1]]],
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166 "Module colors",
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167 dendroLabels = FALSE, hang = 0.03,
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168 addGuide = TRUE, guideHang = 0.05)
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169 ```
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170
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171
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172 ```{r echo=FALSE}
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173 # save workspace
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174 rm("opt")
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175 save(list=ls(all.names = TRUE), file='CONSTRUCT_NETWORK_WORKSPACE')
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176 ```
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177
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178