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author mingchen0919
date Fri, 14 Dec 2018 00:38:44 -0500
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1 ---
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2 title: '[FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) 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
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9 ```{r setup, include=FALSE, warning=FALSE, message=FALSE}
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10 knitr::opts_chunk$set(error = TRUE, echo = FALSE)
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11 ```
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12
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13 ```{css, echo=FALSE}
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14 pre code, pre, code {
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15 white-space: pre !important;
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16 overflow-x: scroll !important;
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17 word-break: keep-all !important;
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18 word-wrap: initial !important;
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19 }
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20 ```
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21
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22 ```{r, echo=FALSE}
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23 # to make the css theme to work, <link></link> tags cannot be added directly
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24 # as <script></script> tags as below.
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25 # it has to be added using a code chunk with the htmltool functions!!!
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26 css_link = tags$link()
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27 css_link$attribs = list(rel="stylesheet", href="vakata-jstree-3.3.5/dist/themes/default/style.min.css")
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28 css_link
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29 ```
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30
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31 ```{r, eval=FALSE, echo=FALSE}
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32 # this code chunk is purely for adding comments
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33 # below is to add jQuery and jstree javascripts
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34 ```
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35
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36 <script src="vakata-jstree-3.3.5/dist/jstree.min.js"></script>
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37
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38
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39 ```{r, eval=FALSE, echo=FALSE}
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40 # this code chunk is purely for adding comments
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41 # javascript code below is to build the file tree interface
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42 # 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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43 ```
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44 <script>
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45 jQuery(function () {
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46 // create an instance when the DOM is ready
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47 jQuery('#jstree').jstree().bind("select_node.jstree", function (e, data) {
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48 window.open( data.node.a_attr.href, data.node.a_attr.target )
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49 });
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50 });
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51 </script>
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52
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53
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54 ```{r, eval=FALSE, echo=FALSE}
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55 ---
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56 # ADD YOUR DATA ANALYSIS CODE AND MARKUP TEXT BELOW TO EXTEND THIS R MARKDOWN FILE
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57 ---
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58 ```
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59
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60
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61 # Run FastQC
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62
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63 ```{bash}
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64 sh ${TOOL_INSTALL_DIR}/build-and-run-job-scripts.sh
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65 ```
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66
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67 ```{r echo=FALSE,results='asis'}
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68 # display fastqc job script
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69 cat('```bash\n')
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70 cat(readLines(paste0(Sys.getenv('REPORT_FILES_PATH'), '/job-1-script.sh')), sep = '\n')
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71 cat('\n```')
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72 ```
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73
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74 # Fastqc Output Visualization
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75
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76 ## Overview
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77
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78 ```{r eval=TRUE}
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79 read_1_summary = read.csv(paste0(opt$X_d, '/read_1_fastqc/summary.txt'),
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80 stringsAsFactors = FALSE,
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81 header = FALSE, sep = '\t')[, 2:1]
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82 read_2_summary = read.csv(paste0(opt$X_d, '/read_2_fastqc/summary.txt'),
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83 stringsAsFactors = FALSE,
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84 header = FALSE, sep = '\t')[, 1]
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85 combined_summary = data.frame(read_1_summary, read_2_summary, stringsAsFactors = FALSE)
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86 names(combined_summary) = c('MODULE', 'Pre-trimming', 'Post-trimming')
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87 combined_summary[combined_summary == 'FAIL'] = 'FAIL (X)'
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88 combined_summary[combined_summary == 'WARN'] = 'WARN (!)'
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89 DT::datatable(combined_summary)
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90 ```
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91
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92 ```{r 'function definition', echo=FALSE}
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93 extract_data_module = function(fastqc_data, module_name, header = TRUE, comment.char = "") {
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94 f = readLines(fastqc_data)
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95 start_line = grep(module_name, f)
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96 end_module_lines = grep('END_MODULE', f)
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97 end_line = end_module_lines[which(end_module_lines > start_line)[1]]
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98 module_data = f[(start_line+1):(end_line-1)]
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99 writeLines(module_data, '/tmp/temp.txt')
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100 read.csv('/tmp/temp.txt', sep = '\t', header = header, comment.char = comment.char)
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101 }
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102 ```
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103
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104
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105 ### Basic Statistics {.tabset}
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106
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107 #### Before
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108
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109 ```{r}
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110 fastqc_data_1 = paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt')
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111 module_name = 'Basic Statistics pass'
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112
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113 basic_statistics = extract_data_module(fastqc_data_1, module_name)
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114 colnames(basic_statistics) = c('Measure', 'Value')
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115 DT::datatable(basic_statistics)
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116 ```
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117
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118 #### After
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119
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120 ```{r}
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121 fastqc_data_2 = paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt')
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122 module_name = 'Basic Statistics pass'
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123
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124 basic_statistics = extract_data_module(fastqc_data_2, module_name)
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125 colnames(basic_statistics) = c('Measure', 'Value')
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126 DT::datatable(basic_statistics)
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127 ```
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128
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129
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130 ### Per base sequence quality
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131
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132 ```{r 'per base sequence quality'}
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133 ## reads 1
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134 pbsq_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Per base sequence quality')
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135 pbsq_1$id = 1:length(pbsq_1$X.Base)
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136 pbsq_1$trim = 'before'
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137
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138 ## reads 2
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139 pbsq_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Per base sequence quality')
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140 pbsq_2$id = 1:length(pbsq_2$X.Base)
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141 pbsq_2$trim = 'after'
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142
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143 comb_pbsq = rbind(pbsq_1, pbsq_2)
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144 comb_pbsq$trim = factor(levels = c('before', 'after'), comb_pbsq$trim)
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145
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146 p = ggplot(data = comb_pbsq) +
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147 geom_boxplot(mapping = aes(x = id,
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148 lower = Lower.Quartile,
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149 upper = Upper.Quartile,
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150 middle = Median,
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151 ymin = X10th.Percentile,
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152 ymax = X90th.Percentile,
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153 fill = "yellow"),
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154 stat = 'identity') +
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155 geom_line(mapping = aes(x = id, y = Mean, color = "red")) +
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156 scale_x_continuous(name = '\nPosition in read (bp)', breaks = pbsq_2$id, labels = pbsq_2$X.Base) +
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157 scale_y_continuous(limits = c(0, max(comb_pbsq$Upper.Quartile) + 5)) +
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158 scale_fill_identity() +
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159 scale_color_identity() +
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160 facet_grid(. ~ trim) +
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161 theme(axis.text.x = element_text(size = 5),
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162 panel.background = element_rect(fill = NA),
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163 panel.grid.major.y = element_line(color = 'blue', size = 0.1))
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164 p
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165 ```
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166
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167
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168 ### Per tile sequence quality
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169
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170 ```{r 'per tile sequence quality'}
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171 ## check if 'per tile sequence quality' module exits or not
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172 check_ptsq = grep('Per tile sequence quality', readLines(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt')))
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173 if (length(check_ptsq) > 0) {
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174 ## reads 1
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175 ptsq_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Per tile sequence quality')
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176 ptsq_1$trim = 'before'
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177
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178 ## reads 2
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179 ptsq_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Per tile sequence quality')
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180 ptsq_2$trim = 'after'
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181
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182 comb_ptsq = rbind(ptsq_1, ptsq_2)
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183 comb_ptsq$trim = factor(levels = c('before', 'after'), comb_ptsq$trim)
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184 comb_ptsq$Base = factor(levels = unique(comb_ptsq$Base), comb_ptsq$Base)
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185
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186 # convert integers to charaters
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187 # comb_ptsq$Tile = as.character(comb_ptsq$X.Tile)
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188
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189 p = ggplot(data = comb_ptsq) +
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190 geom_raster(mapping = aes(x = Base, y = X.Tile, fill = Mean)) +
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191 facet_grid(. ~ trim) +
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192 scale_x_discrete(name = "\nPosition in read (bp)") +
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193 scale_y_continuous(name = "") +
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194 scale_fill_gradient(low = "blue", high = "red") +
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195 theme(axis.text.x = element_text(size = 5, angle = 90),
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196 axis.text.y = element_text(size = 5),
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197 panel.background = element_rect(fill = NA))
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198 ggplotly(p)
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199 } else {
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200 print('No "per tile sequence quality" data')
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201 }
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202 ```
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203
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204 ### Per sequence quality score
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205
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206 ```{r 'Per sequence quality score'}
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207 ## reads 1
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208 psqs_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Per sequence quality scores')
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209 psqs_1$trim = 'before'
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210
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211 ## reads 2
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212 psqs_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Per sequence quality scores')
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213 psqs_2$trim = 'after'
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214
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215 comb_psqs = rbind(psqs_1, psqs_2)
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216 comb_psqs$trim = factor(levels = c('before', 'after'), comb_psqs$trim)
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217
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218 p = ggplot(data = comb_psqs) +
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219 geom_line(mapping = aes(x = X.Quality, y = Count), color = 'red') +
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220 facet_grid(. ~ trim) +
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221 scale_x_continuous(name = '\nMean Sequence Qaulity (Phred Score)',
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222 limits = c(min(comb_psqs$X.Quality), max(comb_psqs$X.Quality))) +
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223 scale_y_continuous(name = '') +
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224 theme(panel.background = element_rect(fill = NA),
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225 axis.line = element_line(),
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226 panel.grid.major.y = element_line(color = 'blue', size = 0.1))
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227 p
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228 ```
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229
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230 ### Per base sequence content
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231
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232 ```{r 'Per base sequence content'}
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233 ## reads 1
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234 pbsc_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Per base sequence content')
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235 pbsc_1$id = 1:length(pbsc_1$X.Base)
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236
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237 melt_pbsc_1 = melt(pbsc_1, id=c('X.Base', 'id'))
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238 melt_pbsc_1$trim = 'before'
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239
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240
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241 ## reads 2
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242 pbsc_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Per base sequence content')
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243 pbsc_2$id = 1:length(pbsc_2$X.Base)
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244
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245 melt_pbsc_2 = melt(pbsc_2, id=c('X.Base', 'id'))
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246 melt_pbsc_2$trim = 'after'
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247
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248 comb_pbsc = rbind(melt_pbsc_1, melt_pbsc_2)
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249 comb_pbsc$trim = factor(levels = c('before', 'after'), comb_pbsc$trim)
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250
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251 p = ggplot(data = comb_pbsc) +
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252 geom_line(mapping = aes(x = id, y = value, color = variable)) +
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253 facet_grid(. ~ trim) +
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254 xlim(min(comb_pbsc$id), max(comb_pbsc$id)) +
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255 ylim(0, 100) +
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256 xlab('\nPosition in read (bp)') +
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257 ylab('') +
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258 scale_color_discrete(name = '') +
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259 theme_classic()
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260 p
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261 ```
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262
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263 ### Per sequence GC content
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264
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265 ```{r 'Per sequence GC content'}
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266 ## reads 1
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267 psGCc_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Per sequence GC content')
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268 psGCc_1$trim = 'before'
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269
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270 ## reads 2
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271 psGCc_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Per sequence GC content')
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272 psGCc_2$trim = 'after'
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273
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274 comb_psGCc = rbind(psGCc_1, psGCc_2)
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275 comb_psGCc$trim = factor(levels = c('before', 'after'), comb_psGCc$trim)
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276
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277 p = ggplot(data = comb_psGCc, aes(x = X.GC.Content, y = Count)) +
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278 geom_line(color = 'red') +
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279 facet_grid(. ~ trim) +
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280 xlab('\nMean Sequence Qaulity (Phred Score)') +
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281 ylab('') +
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282 scale_color_discrete(name = '') +
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283 theme_classic()
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284 p
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285 ```
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286
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287
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288 ### Per base N content
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289
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290 ```{r 'Per base N content'}
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291 ## reads 1
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292 pbNc_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Per base N content')
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293 pbNc_1$id = 1:length(pbNc_1$X.Base)
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294 pbNc_1$trim = 'before'
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295
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296 ## reads 2
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297 pbNc_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Per base N content')
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298 pbNc_2$id = 1:length(pbNc_2$X.Base)
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299 pbNc_2$trim = 'after'
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300
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301 comb_pbNc = rbind(pbNc_1, pbNc_2)
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302 comb_pbNc$trim = factor(levels = c('before', 'after'), comb_pbNc$trim)
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303
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304 p = ggplot(data = comb_pbNc, aes(x = id, y = N.Count)) +
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305 geom_line(color = 'red') +
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306 scale_x_continuous(breaks = pbNc_2$id, labels = pbNc_2$X.Base) +
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307 facet_grid(. ~ trim) +
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308 ylim(0, 1) +
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309 xlab('\nN-Count') +
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310 ylab('') +
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311 theme(axis.text.x = element_text(size = 5),
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312 axis.line = element_line(),
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313 panel.background = element_rect(fill = NA))
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314 p
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315 ```
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316
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317
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318 ### Sequence Length Distribution
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319
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320 ```{r 'Sequence Length Distribution'}
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321 ## reads 1
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322 sld_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Sequence Length Distribution')
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323 sld_1$id = 1:length(sld_1$X.Length)
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324 sld_1$trim = 'before'
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325
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326 ## reads 2
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327 sld_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Sequence Length Distribution')
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328 sld_2$id = 1:length(sld_2$X.Length)
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329 sld_2$trim = 'after'
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330
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331 comb_sld = rbind(sld_1, sld_2)
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332 comb_sld$trim = factor(levels = c('before', 'after'), comb_sld$trim)
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333
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334 p = ggplot(data = comb_sld, aes(x = id, y = Count)) +
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335 geom_line(color = 'red') +
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336 scale_x_continuous(breaks = sld_2$id, labels = sld_2$X.Length) +
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337 facet_grid(. ~ trim) +
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338 xlab('\nSequence Length (bp)') +
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339 ylab('') +
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340 theme(axis.text.x = element_text(size = 5),
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341 panel.background = element_rect(fill = NA),
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342 axis.line = element_line(),
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343 plot.margin = margin(2,2,2,10) )
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344 p
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345 ```
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346
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347 ### Sequence Duplication Levels
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348
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349 ```{r 'Sequence Duplication Levels'}
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350 ## reads 1
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351 sdl_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Sequence Duplication Levels', header = FALSE, comment.char = '#')
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352 names(sdl_1) = c('Duplication_Level', 'Percentage_of_deduplicated', 'Percentage_of_total')
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353 sdl_1$id = 1:length(sdl_1$Duplication_Level)
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354
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355 melt_sdl_1 = melt(sdl_1, id=c('Duplication_Level', 'id'))
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356 melt_sdl_1$trim = 'before'
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357
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358
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359 ## reads 2
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360 sdl_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Sequence Duplication Levels', header = FALSE, comment.char = '#')
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361 names(sdl_2) = c('Duplication_Level', 'Percentage_of_deduplicated', 'Percentage_of_total')
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362 sdl_2$id = 1:length(sdl_2$Duplication_Level)
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363
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364 melt_sdl_2 = melt(sdl_2, id=c('Duplication_Level', 'id'))
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365 melt_sdl_2$trim = 'after'
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366
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367 comb_sdl = rbind(melt_sdl_1, melt_sdl_2)
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368 comb_sdl$trim = factor(levels = c('before', 'after'), comb_sdl$trim)
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369
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370 p = ggplot(data = comb_sdl) +
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371 geom_line(mapping = aes(x = id, y = value, color = variable)) +
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372 scale_x_continuous(breaks = sdl_2$id, labels = sdl_2$Duplication_Level) +
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373 facet_grid(. ~ trim) +
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374 xlab('\nSequence Duplication Level') +
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375 ylab('') +
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376 scale_color_discrete(name = '') +
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377 theme(axis.text.x = element_text(size = 5),
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378 panel.background = element_rect(fill = NA),
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379 axis.line = element_line(),
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380 legend.position="top")
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381 p
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382 ```
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383
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384
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385 ### Overrepresented sequences {.tabset}
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386
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387 #### Before
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388
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389 ```{r}
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390 fastqc_data_1 = paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt')
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391 module_name = 'Overrepresented sequences'
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392
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393 overrepresented_seq = extract_data_module(fastqc_data_1, module_name)
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394 colnames(overrepresented_seq) = c('Sequence', 'Count', 'Percentage', 'Possible Source')
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395 DT::datatable(overrepresented_seq)
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396 ```
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397
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398 #### After
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399
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400 ```{r}
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401 fastqc_data_2 = paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt')
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402 module_name = 'Overrepresented sequences'
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403
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404 overrepresented_seq = extract_data_module(fastqc_data_2, module_name)
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405 colnames(overrepresented_seq) = c('Sequence', 'Count', 'Percentage', 'Possible Source')
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406 DT::datatable(overrepresented_seq)
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407 ```
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408
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409
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410 ### Adapter Content
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411
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412 ```{r 'Adapter Content'}
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413 ## reads 1
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414 ac_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Adapter Content')
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415 ac_1$id = 1:length(ac_1$X.Position)
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416
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417 melt_ac_1 = melt(ac_1, id=c('X.Position', 'id'))
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418 melt_ac_1$trim = 'before'
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419
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420 ## reads 2
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421 ac_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Adapter Content')
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422 ac_2$id = 1:length(ac_2$X.Position)
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423
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424 melt_ac_2 = melt(ac_2, id=c('X.Position', 'id'))
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425 melt_ac_2$trim = 'after'
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426
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427 comb_ac = rbind(melt_ac_1, melt_ac_2)
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428 comb_ac$trim = factor(levels = c('before', 'after'), comb_ac$trim)
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429
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430 p = ggplot(data = comb_ac, aes(x = id, y = value, color = variable)) +
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431 geom_line() +
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432 facet_grid(. ~ trim) +
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433 xlim(min(comb_ac$id), max(comb_ac$id)) +
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434 ylim(0, 1) +
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435 xlab('\nPosition in read (bp)') +
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436 ylab('') +
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437 scale_color_discrete(name = '') +
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438 theme(axis.text.x = element_text(size = 5),
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439 panel.background = element_rect(fill = NA),
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440 axis.line = element_line())
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441 ggplotly(p)
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442 ```
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443
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444 ### Kmer Content {.tabset}
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445
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446 #### Before
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447
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448 ```{r 'Kmer Content (before)'}
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449 kc_1 = extract_data_module(paste0(opt$X_d, '/read_1_fastqc/fastqc_data.txt'), 'Kmer Content')
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450 DT::datatable(kc_1)
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451 ```
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452
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453 #### After
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454 ```{r 'Kmer Content (after)'}
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455 kc_2 = extract_data_module(paste0(opt$X_d, '/read_2_fastqc/fastqc_data.txt'), 'Kmer Content')
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456 DT::datatable(kc_2)
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457 ```
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458