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author mingchen0919
date Fri, 14 Dec 2018 00:21:26 -0500
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1 ---
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2 title: 'Aurora DESeq2 Report'
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3 output:
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4 html_document:
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5 highlight: pygments
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6 ---
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7
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8 ```{r setup, include=FALSE, warning=FALSE, message=FALSE}
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9 knitr::opts_chunk$set(error = TRUE, echo = FALSE)
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10 ```
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11
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12 ```{css, echo=FALSE}
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13 pre code, pre, code {
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14 white-space: pre !important;
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15 overflow-x: scroll !important;
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16 word-break: keep-all !important;
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17 word-wrap: initial !important;
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18 }
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19 ```
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20
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21 ```{r, echo=FALSE}
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22 # to make the css theme to work, <link></link> tags cannot be added directly
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23 # as <script></script> tags as below.
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24 # it has to be added using a code chunk with the htmltool functions!!!
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25 css_link = tags$link()
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26 css_link$attribs = list(rel="stylesheet", href="vakata-jstree-3.3.5/dist/themes/default/style.min.css")
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27 css_link
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28 ```
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29
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30 ```{r, eval=FALSE, echo=FALSE}
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31 # this code chunk is purely for adding comments
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32 # below is to add jQuery and jstree javascripts
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33 ```
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34
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35 <script src="vakata-jstree-3.3.5/dist/jstree.min.js"></script>
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36
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37
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38 ```{r, eval=FALSE, echo=FALSE}
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39 # this code chunk is purely for adding comments
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40 # javascript code below is to build the file tree interface
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41 # see this for how to implement opening hyperlink: https://stackoverflow.com/questions/18611317/how-to-get-i-get-leaf-nodes-in-jstree-to-open-their-hyperlink-when-clicked-when
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42 ```
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43 <script>
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44 jQuery(function () {
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45 // create an instance when the DOM is ready
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46 jQuery('#jstree').jstree().bind("select_node.jstree", function (e, data) {
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47 window.open( data.node.a_attr.href, data.node.a_attr.target )
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48 });
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49 });
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50 </script>
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51
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52
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53 ```{r, eval=FALSE, echo=FALSE}
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54 ---
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55 # ADD YOUR DATA ANALYSIS CODE AND MARKUP TEXT BELOW TO EXTEND THIS R MARKDOWN FILE
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56 ---
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57 ```
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58
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59 ## DESeq2 analysis
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60
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61 ```{r echo=FALSE}
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62 # import count data
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63 count_data = read.csv(opt$X_A, row.names = 1, header = TRUE)
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64 # import column data
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65 coldata = read.csv(opt$X_B, row.names = 1, header = TRUE)[colnames(count_data),,drop=FALSE]
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66 ```
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67
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68 ```{r}
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69 f = gsub('~', '~ 1 +', opt$X_C) # build formula
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70 dds = DESeqDataSetFromMatrix(countData = count_data,
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71 colData = coldata,
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72 design = formula(f))
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73
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74
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75 # prefiltering
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76 keep <- rowSums(counts(dds)) >= 10
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77 dds <- dds[keep,]
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78
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79 # Run DESeq
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80 if (opt$X_T == 'LRT') {
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81 reduced_f = gsub(paste0('\\+\\s*', opt$X_D), '', f)
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82 dds = DESeq(dds, test=opt$X_T, fitType = opt$X_H, reduced = formula(reduced_f))
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83 } else {
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84 dds = DESeq(dds, test=opt$X_T, fitType = opt$X_H)
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85 }
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86
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87 ## Differential expression test results
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88 res = results(dds, contrast = c(opt$X_D, opt$X_E, opt$X_F), alpha = opt$X_I)
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89 DT::datatable(as.data.frame(res))
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90 ```
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91
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92
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93 ```{r}
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94 # save all padj sorted res to tool output directory
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95 padj_sorted_res = res[order(res$padj), ]
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96 write.table(padj_sorted_res,
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97 file = paste0(opt$X_d, '/padj-sorted-genes.txt'),
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98 quote = FALSE)
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99
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100 # save significant genes to a file in tool output directory
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101 sig_res = res[(res$padj < opt$X_I) & !is.na(res$padj), ]
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102 sig_res_sorted = sig_res[order(sig_res$padj), ]
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103 sig_res_sorted$feature_id = rownames(sig_res_sorted)
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104 n_col = ncol(sig_res_sorted)
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105 sig_res_sorted = sig_res_sorted[, c(n_col, 1:(n_col - 1))]
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106 write.table(sig_res_sorted,
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107 file = paste0(opt$X_d, '/padj-sorted-significant-genes.txt'),
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108 quote = FALSE, row.names = FALSE)
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109 ```
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110
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111 ## MA-plot
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112
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113 ```{r warning=FALSE}
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114 log_fold_change = res$log2FoldChange
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115 base_mean = res$baseMean
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116 significant = res$padj
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117 significant[significant < 0.1] = 'yes'
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118 significant[significant != 'yes'] = 'no'
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119
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120 maplot_df = data.frame(log_fold_change, base_mean, significant)
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121 maplot_df = maplot_df[!is.na(maplot_df$significant), ]
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122 p = ggplot(data = maplot_df) +
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123 geom_point(mapping = aes(log(base_mean), log_fold_change, color = significant),
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124 size = 0.5) +
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125 scale_color_manual(name = 'Significant',
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126 values = c('no' = 'black', 'yes' = 'red'),
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127 labels = c('No', 'Yes')) +
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128 xlab('Log base mean') +
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129 ylab('Log fold change') +
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130 theme_classic()
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131
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132 plotly::ggplotly(p)
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133 ```
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134
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135
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136 ## Heatmap of count matrix
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137
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138 ```{r}
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139 ntd <- normTransform(dds)
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140 select <- order(rowMeans(counts(dds,normalized=TRUE)),
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141 decreasing=TRUE)[1:20]
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142 df <- as.data.frame(colData(dds)[, -ncol(colData(dds))])
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143 pheatmap(assay(ntd)[select,], annotation_col=df)
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144 ```
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145
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146
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147 ## Principle component analysis plot
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148
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149 ```{r}
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150 vsd <- vst(dds, blind=FALSE)
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151 p = plotPCA(vsd, intgroup=c(opt$X_D)) +
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152 scale_color_discrete(name = 'Group') +
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153 theme_classic()
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154 ggplotly(p)
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155 ```
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156