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1 #!/usr/bin/env Rscript
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2 # ARGS: 1.inputType -String specifying format of input (fastq or table)
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3 # IF inputType is "fastq" or "pairedFastq:
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4 # 2*.fastqPath -One or more strings specifying path to fastq files
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5 # 2.annoPath -String specifying path to hairpin annotation table
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6 # 3.samplePath -String specifying path to sample annotation table
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7 # 4.barStart -Integer specifying starting position of barcode
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8 # 5.barEnd -Integer specifying ending position of barcode
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9 # ###
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10 # IF inputType is "pairedFastq":
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11 # 6.barStartRev -Integer specifying starting position of barcode
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12 # on reverse end
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13 # 7.barEndRev -Integer specifying ending position of barcode
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14 # on reverse end
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15 # ###
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16 # 8.hpStart -Integer specifying startins position of hairpin
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17 # unique region
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18 # 9.hpEnd -Integer specifying ending position of hairpin
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19 # unique region
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20 # IF inputType is "counts":
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21 # 2.countPath -String specifying path to count table
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22 # 3.annoPath -String specifying path to hairpin annotation table
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23 # 4.samplePath -String specifying path to sample annotation table
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24 # ###
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25 # 10.secFactName -String specifying name of secondary factor
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26 # 11.cpmReq -Float specifying cpm requirement
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27 # 12.sampleReq -Integer specifying cpm requirement
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28 # 13.readReq -Integer specifying read requirement
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29 # 14.fdrThresh -Float specifying the FDR requirement
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30 # 15.lfcThresh -Float specifying the log-fold-change requirement
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31 # 16.workMode -String specifying exact test or GLM usage
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32 # 17.htmlPath -String specifying path to HTML file
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33 # 18.folderPath -String specifying path to folder for output
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34 # IF workMode is "classic" (exact test)
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35 # 19.pairData[2] -String specifying first group for exact test
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36 # 20.pairData[1] -String specifying second group for exact test
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37 # ###
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38 # IF workMode is "glm"
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39 # 19.contrastData -String specifying contrasts to be made
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40 # 20.roastOpt -String specifying usage of gene-wise tests
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41 # 21.hairpinReq -String specifying hairpin requirement for gene-
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42 # wise test
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43 # 22.selectOpt -String specifying type of selection for barcode
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44 # plots
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45 # 23.selectVals -String specifying members selected for barcode
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46 # plots
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47 # ###
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48 #
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49 # OUT: Bar Plot of Counts Per Index
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50 # Bar Plot of Counts Per Hairpin
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51 # MDS Plot
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52 # BCV Plot
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53 # Smear Plot
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54 # Barcode Plots (If Genewise testing was selected)
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55 # Top Expression Table
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56 # Feature Counts Table
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57 # HTML file linking to the ouputs
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58 #
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59 # Author: Shian Su - registertonysu@gmail.com - Jan 2014
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60
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61 # Record starting time
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62 timeStart <- as.character(Sys.time())
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63 options(bitmapType='cairo')
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64 # needed to prevent missing x11 errors for png()
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65 # Loading and checking required packages
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66 library(methods, quietly=TRUE, warn.conflicts=FALSE)
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67 library(statmod, quietly=TRUE, warn.conflicts=FALSE)
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68 library(splines, quietly=TRUE, warn.conflicts=FALSE)
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69 library(edgeR, quietly=TRUE, warn.conflicts=FALSE)
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70 library(limma, quietly=TRUE, warn.conflicts=FALSE)
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71
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72 if (packageVersion("edgeR") < "3.7.17") {
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73 stop("Please update 'edgeR' to version >= 3.7.17 to run this tool")
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74 }
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75
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76 if (packageVersion("limma")<"3.21.16") {
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77 message("Update 'limma' to version >= 3.21.16 to see updated barcode graphs")
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78 }
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79
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80 ################################################################################
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81 ### Function declarations
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82 ################################################################################
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83
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84 # Function to load libaries without messages
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85 silentLibrary <- function(...) {
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86 list <- c(...)
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87 for (package in list){
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88 suppressPackageStartupMessages(library(package, character.only=TRUE))
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89 }
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90 }
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91
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92 # Function to sanitise contrast equations so there are no whitespaces
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93 # surrounding the arithmetic operators, leading or trailing whitespace
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94 sanitiseEquation <- function(equation) {
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95 equation <- gsub(" *[+] *", "+", equation)
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96 equation <- gsub(" *[-] *", "-", equation)
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97 equation <- gsub(" *[/] *", "/", equation)
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98 equation <- gsub(" *[*] *", "*", equation)
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99 equation <- gsub("^\\s+|\\s+$", "", equation)
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100 return(equation)
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101 }
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102
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103 # Function to sanitise group information
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104 sanitiseGroups <- function(string) {
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105 string <- gsub(" *[,] *", ",", string)
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106 string <- gsub("^\\s+|\\s+$", "", string)
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107 return(string)
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108 }
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109
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110 # Function to change periods to whitespace in a string
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111 unmake.names <- function(string) {
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112 string <- gsub(".", " ", string, fixed=TRUE)
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113 return(string)
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114 }
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115
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116 # Function has string input and generates an output path string
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117 makeOut <- function(filename) {
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118 return(paste0(folderPath, "/", filename))
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119 }
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120
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121 # Function has string input and generates both a pdf and png output strings
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122 imgOut <- function(filename) {
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123 assign(paste0(filename, "Png"), makeOut(paste0(filename,".png")),
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124 envir=.GlobalEnv)
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125 assign(paste0(filename, "Pdf"), makeOut(paste0(filename,".pdf")),
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126 envir=.GlobalEnv)
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127 }
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128
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129 # Create cat function default path set, default seperator empty and appending
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130 # true by default (Ripped straight from the cat function with altered argument
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131 # defaults)
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132 cata <- function(..., file=htmlPath, sep="", fill=FALSE, labels=NULL,
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133 append=TRUE) {
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134 if (is.character(file))
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135 if (file == "")
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136 file <- stdout()
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137 else if (substring(file, 1L, 1L) == "|") {
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138 file <- pipe(substring(file, 2L), "w")
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139 on.exit(close(file))
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140 }
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141 else {
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142 file <- file(file, ifelse(append, "a", "w"))
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143 on.exit(close(file))
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144 }
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145 .Internal(cat(list(...), file, sep, fill, labels, append))
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146 }
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147
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148 # Function to write code for html head and title
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149 HtmlHead <- function(title) {
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150 cata("<head>\n")
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151 cata("<title>", title, "</title>\n")
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152 cata("</head>\n")
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153 }
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154
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155 # Function to write code for html links
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156 HtmlLink <- function(address, label=address) {
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157 cata("<a href=\"", address, "\" target=\"_blank\">", label, "</a><br />\n")
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158 }
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159
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160 # Function to write code for html images
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161 HtmlImage <- function(source, label=source, height=600, width=600) {
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162 cata("<img src=\"", source, "\" alt=\"", label, "\" height=\"", height)
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163 cata("\" width=\"", width, "\"/>\n")
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164 }
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165
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166 # Function to write code for html list items
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167 ListItem <- function(...) {
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168 cata("<li>", ..., "</li>\n")
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169 }
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170
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171 TableItem <- function(...) {
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172 cata("<td>", ..., "</td>\n")
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173 }
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174
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175 TableHeadItem <- function(...) {
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176 cata("<th>", ..., "</th>\n")
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177 }
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178 ################################################################################
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179 ### Input Processing
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180 ################################################################################
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181
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182 # Grabbing arguments from command line
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183 argv <- commandArgs(T)
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184
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185 inputType <- as.character(argv[1])
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186 if (inputType == "fastq") {
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187
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188 fastqPath <- as.character(gsub("fastq::", "", argv[grepl("fastq::", argv)],
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189 fixed=TRUE))
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190
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191 # Remove fastq paths
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192 argv <- argv[!grepl("fastq::", argv, fixed=TRUE)]
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193
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194 fastqPathRev <- NULL
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195 annoPath <- as.character(argv[2])
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196 samplePath <- as.character(argv[3])
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197 barStart <- as.numeric(argv[4])
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198 barEnd <- as.numeric(argv[5])
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199 barStartRev <- NULL
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200 barStartRev <- NULL
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201 hpStart <- as.numeric(argv[8])
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202 hpEnd <- as.numeric(argv[9])
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203 } else if (inputType=="pairedFastq") {
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204
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205 fastqPath <- as.character(gsub("fastq::", "", argv[grepl("fastq::", argv)],
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206 fixed=TRUE))
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207
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208 fastqPathRev <- as.character(gsub("fastqRev::", "",
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209 argv[grepl("fastqRev::", argv)], fixed=TRUE))
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210
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211 # Remove fastq paths
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212 argv <- argv[!grepl("fastq::", argv, fixed=TRUE)]
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213 argv <- argv[!grepl("fastqRev::", argv, fixed=TRUE)]
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214
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215 annoPath <- as.character(argv[2])
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216 samplePath <- as.character(argv[3])
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217 barStart <- as.numeric(argv[4])
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218 barEnd <- as.numeric(argv[5])
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219 barStartRev <- as.numeric(argv[6])
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220 barEndRev <- as.numeric(argv[7])
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221 hpStart <- as.numeric(argv[8])
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222 hpEnd <- as.numeric(argv[9])
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223 } else if (inputType == "counts") {
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224 countPath <- as.character(argv[2])
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225 annoPath <- as.character(argv[3])
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226 samplePath <- as.character(argv[4])
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227 }
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228
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229 secFactName <- as.character(argv[10])
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230 cpmReq <- as.numeric(argv[11])
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231 sampleReq <- as.numeric(argv[12])
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232 readReq <- as.numeric(argv[13])
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233 fdrThresh <- as.numeric(argv[14])
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234 lfcThresh <- as.numeric(argv[15])
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235 selectDirection <- as.character(argv[16])
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236 workMode <- as.character(argv[17])
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237 htmlPath <- as.character(argv[18])
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238 folderPath <- as.character(argv[19])
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239
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240 if (workMode == "classic") {
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241 pairData <- character()
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242 pairData[2] <- as.character(argv[20])
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243 pairData[1] <- as.character(argv[21])
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244 } else if (workMode == "glm") {
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245 contrastData <- as.character(argv[20])
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246 roastOpt <- as.character(argv[21])
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247 hairpinReq <- as.numeric(argv[22])
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248 selectOpt <- as.character(argv[23])
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249 selectVals <- as.character(argv[24])
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250 }
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251
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252 # Read in inputs
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253
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254 samples <- read.table(samplePath, header=TRUE, sep="\t")
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255
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256 anno <- read.table(annoPath, header=TRUE, sep="\t")
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257
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258 if (inputType == "counts") {
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259 counts <- read.table(countPath, header=TRUE, sep="\t")
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260 }
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261
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262 ###################### Check inputs for correctness ############################
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263 samples$ID <- make.names(samples$ID)
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264
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265 if ( !any(grepl("group", names(samples))) ) {
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266 stop("'group' column not specified in sample annotation file")
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267 } # Check if grouping variable has been specified
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268
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269 if (secFactName != "none") {
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270 if ( !any(grepl(secFactName, names(samples))) ) {
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271 tempStr <- paste0("Second factor specified as \"", secFactName, "\" but ",
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272 "no such column name found in sample annotation file")
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273 stop(tempStr)
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274 } # Check if specified secondary factor is present
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275 }
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276
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277
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278 if ( any(table(samples$ID) > 1) ){
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279 tab <- table(samples$ID)
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280 offenders <- paste(names(tab[tab > 1]), collapse=", ")
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281 offenders <- unmake.names(offenders)
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282 stop("'ID' column of sample annotation must have unique values, values ",
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283 offenders, " are repeated")
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284 } # Check that IDs in sample annotation are unique
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285
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286 if (inputType == "fastq" || inputType == "pairedFastq") {
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287
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288 if ( any(table(anno$ID) > 1) ){
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289 tab <- table(anno$ID)
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290 offenders <- paste(names(tab[tab>1]), collapse=", ")
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291 stop("'ID' column of hairpin annotation must have unique values, values ",
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292 offenders, " are repeated")
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293 } # Check that IDs in hairpin annotation are unique
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294
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295 } else if (inputType == "counts") {
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296 # The first element of the colnames will be 'ID' and should not match
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297 idFromSample <- samples$ID
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298 idFromTable <- colnames(counts)[-1]
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299 if (any(is.na(match(idFromTable, idFromSample)))) {
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300 stop("not all samples have groups specified")
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301 } # Check that a group has be specifed for each sample
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302
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303 if ( any(table(counts$ID) > 1) ){
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304 tab <- table(counts$ID)
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305 offenders <- paste(names(tab[tab>1]), collapse=", ")
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306 stop("'ID' column of count table must have unique values, values ",
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307 offenders, " are repeated")
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308 } # Check that IDs in count table are unique
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309 }
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310 if (workMode == "glm") {
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311 if (roastOpt == "yes") {
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312 if (is.na(match("Gene", colnames(anno)))) {
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313 tempStr <- paste("Gene-wise tests selected but'Gene' column not",
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314 "specified in hairpin annotation file")
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315 stop(tempStr)
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316 }
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317 }
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318 }
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319
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320 if (secFactName != "none") {
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321 if (workMode != "glm") {
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322 tempStr <- paste("only glm analysis type possible when secondary factor",
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323 "used, please change appropriate option.")
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324 }
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325 }
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326
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327 ################################################################################
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328
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329 # Process arguments
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330 if (workMode == "glm") {
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331 if (roastOpt == "yes") {
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332 wantRoast <- TRUE
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333 } else {
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334 wantRoast <- FALSE
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335 }
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336 }
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337
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338 # Split up contrasts seperated by comma into a vector and replace spaces with
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339 # periods
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340 if (exists("contrastData")) {
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341 contrastData <- unlist(strsplit(contrastData, split=","))
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342 contrastData <- sanitiseEquation(contrastData)
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343 contrastData <- gsub(" ", ".", contrastData, fixed=TRUE)
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344 }
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345
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346 # Replace spaces with periods in pair data
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347 if (exists("pairData")) {
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348 pairData <- make.names(pairData)
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349 }
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350
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351 # Generate output folder and paths
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352 dir.create(folderPath, showWarnings=FALSE)
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353
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354 # Generate links for outputs
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355 imgOut("barHairpin")
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356 imgOut("barIndex")
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357 imgOut("mds")
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358 imgOut("bcv")
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359 if (workMode == "classic") {
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360 smearPng <- makeOut(paste0("smear(", pairData[2], "-", pairData[1],").png"))
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361 smearPdf <- makeOut(paste0("smear(", pairData[2], "-", pairData[1],").pdf"))
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362 topOut <- makeOut(paste0("toptag(", pairData[2], "-", pairData[1],").tsv"))
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363 } else if (workMode == "glm") {
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364 smearPng <- character()
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365 smearPdf <- character()
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366 topOut <- character()
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367 roastOut <- character()
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368 barcodePng <- character()
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369 barcodePdf <- character()
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370 for (i in 1:length(contrastData)) {
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371 smearPng[i] <- makeOut(paste0("smear(", contrastData[i], ").png"))
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372 smearPdf[i] <- makeOut(paste0("smear(", contrastData[i], ").pdf"))
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373 topOut[i] <- makeOut(paste0("toptag(", contrastData[i], ").tsv"))
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374 roastOut[i] <- makeOut(paste0("gene_level(", contrastData[i], ").tsv"))
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375 barcodePng[i] <- makeOut(paste0("barcode(", contrastData[i], ").png"))
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376 barcodePdf[i] <- makeOut(paste0("barcode(", contrastData[i], ").pdf"))
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377 }
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378 }
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379 countsOut <- makeOut("counts.tsv")
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380 sessionOut <- makeOut("session_info.txt")
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381
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382 # Initialise data for html links and images, table with the link label and
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383 # link address
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384 linkData <- data.frame(Label=character(), Link=character(),
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385 stringsAsFactors=FALSE)
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386 imageData <- data.frame(Label=character(), Link=character(),
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387 stringsAsFactors=FALSE)
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388
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389 # Initialise vectors for storage of up/down/neutral regulated counts
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390 upCount <- numeric()
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391 downCount <- numeric()
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|
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392 flatCount <- numeric()
|
|
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393
|
|
|
394 ################################################################################
|
|
|
395 ### Data Processing
|
|
|
396 ################################################################################
|
|
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397
|
|
|
398 # Transform gene selection from string into index values for mroast
|
|
|
399 if (workMode == "glm") {
|
|
|
400 if (selectOpt == "rank") {
|
|
|
401 selectVals <- gsub(" ", "", selectVals, fixed=TRUE)
|
|
|
402 selectVals <- unlist(strsplit(selectVals, ","))
|
|
|
403
|
|
|
404 for (i in 1:length(selectVals)) {
|
|
|
405 if (grepl(":", selectVals[i], fixed=TRUE)) {
|
|
|
406 temp <- unlist(strsplit(selectVals[i], ":"))
|
|
|
407 selectVals <- selectVals[-i]
|
|
|
408 a <- as.numeric(temp[1])
|
|
|
409 b <- as.numeric(temp[2])
|
|
|
410 selectVals <- c(selectVals, a:b)
|
|
|
411 }
|
|
|
412 }
|
|
|
413 selectVals <- as.numeric(unique(selectVals))
|
|
|
414 } else {
|
|
|
415 selectVals <- gsub(" ", "", selectVals, fixed=TRUE)
|
|
|
416 selectVals <- unlist(strsplit(selectVals, ","))
|
|
|
417 }
|
|
|
418 }
|
|
|
419
|
|
|
420 if (inputType == "fastq" || inputType == "pairedFastq") {
|
|
|
421 # Use EdgeR hairpin process and capture outputs
|
|
|
422
|
|
|
423 hpReadout <- capture.output(
|
|
|
424 data <- processAmplicons(readfile=fastqPath, readfile2=fastqPathRev,
|
|
|
425 barcodefile=samplePath,
|
|
|
426 hairpinfile=annoPath,
|
|
|
427 barcodeStart=barStart, barcodeEnd=barEnd,
|
|
|
428 barcodeStartRev=barStartRev,
|
|
|
429 barcodeEndRev=barEndRev,
|
|
|
430 hairpinStart=hpStart, hairpinEnd=hpEnd,
|
|
|
431 verbose=TRUE)
|
|
|
432 )
|
|
|
433
|
|
|
434 # Remove function output entries that show processing data or is empty
|
|
|
435 hpReadout <- hpReadout[hpReadout!=""]
|
|
|
436 hpReadout <- hpReadout[!grepl("Processing", hpReadout)]
|
|
|
437 hpReadout <- hpReadout[!grepl("in file", hpReadout)]
|
|
|
438 hpReadout <- gsub(" -- ", "", hpReadout, fixed=TRUE)
|
|
|
439
|
|
|
440 # Make the names of groups syntactically valid (replace spaces with periods)
|
|
|
441 data$samples$group <- make.names(data$samples$group)
|
|
|
442 if (secFactName != "none") {
|
|
|
443 data$samples[[secFactName]] <- make.names(data$samples[[secFactName]])
|
|
|
444 }
|
|
|
445 } else if (inputType == "counts") {
|
|
|
446 # Process counts information, set ID column to be row names
|
|
|
447 rownames(counts) <- counts$ID
|
|
|
448 counts <- counts[ , !(colnames(counts) == "ID")]
|
|
|
449 countsRows <- nrow(counts)
|
|
|
450
|
|
|
451 # Process group information
|
|
|
452 sampleNames <- colnames(counts)
|
|
|
453 matchedIndex <- match(sampleNames, samples$ID)
|
|
|
454 factors <- samples$group[matchedIndex]
|
|
|
455
|
|
|
456 if (secFactName != "none") {
|
|
|
457 secFactors <- samples[[secFactName]][matchedIndex]
|
|
|
458 }
|
|
|
459
|
|
|
460 annoRows <- nrow(anno)
|
|
|
461 anno <- anno[match(rownames(counts), anno$ID), ]
|
|
|
462 annoMatched <- sum(!is.na(anno$ID))
|
|
|
463
|
|
|
464 if (any(is.na(anno$ID))) {
|
|
|
465 warningStr <- paste("count table contained more hairpins than",
|
|
|
466 "specified in hairpin annotation file")
|
|
|
467 warning(warningStr)
|
|
|
468 }
|
|
|
469
|
|
|
470 # Filter out rows with zero counts
|
|
|
471 sel <- rowSums(counts)!=0
|
|
|
472 counts <- counts[sel, ]
|
|
|
473 anno <- anno[sel, ]
|
|
|
474
|
|
|
475 # Create DGEList
|
|
|
476 data <- DGEList(counts=counts, lib.size=colSums(counts),
|
|
|
477 norm.factors=rep(1,ncol(counts)), genes=anno, group=factors)
|
|
|
478
|
|
|
479 # Make the names of groups syntactically valid (replace spaces with periods)
|
|
|
480 data$samples$group <- make.names(data$samples$group)
|
|
|
481 }
|
|
|
482
|
|
|
483 # Filter out any samples with zero counts
|
|
|
484 if (any(data$samples$lib.size == 0)) {
|
|
|
485 sampleSel <- data$samples$lib.size != 0
|
|
|
486 filteredSamples <- paste(data$samples$ID[!sampleSel], collapse=", ")
|
|
|
487 data$counts <- data$counts[, sampleSel]
|
|
|
488 data$samples <- data$samples[sampleSel, ]
|
|
|
489 }
|
|
|
490
|
|
|
491 # Filter hairpins with low counts
|
|
|
492 preFilterCount <- nrow(data)
|
|
|
493 selRow <- rowSums(cpm(data$counts) > cpmReq) >= sampleReq
|
|
|
494 selCol <- colSums(data$counts) > readReq
|
|
|
495 data <- data[selRow, selCol]
|
|
|
496
|
|
|
497 # Check if any data survived filtering
|
|
|
498 if (length(data$counts) == 0) {
|
|
|
499 stop("no data remains after filtering, consider relaxing filters")
|
|
|
500 }
|
|
|
501
|
|
|
502 # Count number of filtered tags and samples
|
|
|
503 postFilterCount <- nrow(data)
|
|
|
504 filteredCount <- preFilterCount - postFilterCount
|
|
|
505 sampleFilterCount <- sum(!selCol)
|
|
|
506
|
|
|
507 if (secFactName == "none") {
|
|
|
508 # Estimate dispersions
|
|
|
509 data <- estimateDisp(data)
|
|
|
510 commonBCV <- round(sqrt(data$common.dispersion), 4)
|
|
|
511 } else {
|
|
|
512 # Construct design
|
|
|
513 if (inputType == "counts") {
|
|
|
514
|
|
|
515 sampleNames <- colnames(counts)
|
|
|
516 matchedIndex <- match(sampleNames, samples$ID)
|
|
|
517 factors <- factor(make.names(samples$group[matchedIndex]))
|
|
|
518
|
|
|
519 secFactors <- factor(make.names(samples[[secFactName]][matchedIndex]))
|
|
|
520
|
|
|
521 } else if (inputType == "fastq" || inputType == "pairedFastq") {
|
|
|
522
|
|
|
523 factors <- factor(data$sample$group)
|
|
|
524 secFactors <- factor(data$sample[[secFactName]])
|
|
|
525
|
|
|
526 }
|
|
|
527
|
|
|
528 design <- model.matrix(~0 + factors + secFactors)
|
|
|
529
|
|
|
530 # Estimate dispersions
|
|
|
531 data <- estimateDisp(data, design=design)
|
|
|
532 commonBCV <- round(sqrt(data$common.dispersion), 4)
|
|
|
533 }
|
|
|
534
|
|
|
535
|
|
|
536 ################################################################################
|
|
|
537 ### Output Processing
|
|
|
538 ################################################################################
|
|
|
539
|
|
|
540 # Plot number of hairpins that could be matched per sample
|
|
|
541 png(barIndexPng, width=600, height=600)
|
|
|
542 barplot(height<-colSums(data$counts), las=2, main="Counts per index",
|
|
|
543 cex.names=1.0, cex.axis=0.8, ylim=c(0, max(height)*1.2))
|
|
|
544 imageData[1, ] <- c("Counts per Index", "barIndex.png")
|
|
|
545 invisible(dev.off())
|
|
|
546
|
|
|
547 pdf(barIndexPdf)
|
|
|
548 barplot(height<-colSums(data$counts), las=2, main="Counts per index",
|
|
|
549 cex.names=1.0, cex.axis=0.8, ylim=c(0, max(height)*1.2))
|
|
|
550 linkData[1, ] <- c("Counts per Index Barplot (.pdf)", "barIndex.pdf")
|
|
|
551 invisible(dev.off())
|
|
|
552
|
|
|
553 # Plot per hairpin totals across all samples
|
|
|
554 png(barHairpinPng, width=600, height=600)
|
|
|
555 if (nrow(data$counts)<50) {
|
|
|
556 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin",
|
|
|
557 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2))
|
|
|
558 } else {
|
|
|
559 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin",
|
|
|
560 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2),
|
|
|
561 names.arg=FALSE)
|
|
|
562 }
|
|
|
563 imageData <- rbind(imageData, c("Counts per Hairpin", "barHairpin.png"))
|
|
|
564 invisible(dev.off())
|
|
|
565
|
|
|
566 pdf(barHairpinPdf)
|
|
|
567 if (nrow(data$counts)<50) {
|
|
|
568 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin",
|
|
|
569 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2))
|
|
|
570 } else {
|
|
|
571 barplot(height<-rowSums(data$counts), las=2, main="Counts per hairpin",
|
|
|
572 cex.names=0.8, cex.axis=0.8, ylim=c(0, max(height)*1.2),
|
|
|
573 names.arg=FALSE)
|
|
|
574 }
|
|
|
575 newEntry <- c("Counts per Hairpin Barplot (.pdf)", "barHairpin.pdf")
|
|
|
576 linkData <- rbind(linkData, newEntry)
|
|
|
577 invisible(dev.off())
|
|
|
578
|
|
|
579 # Make an MDS plot to visualise relationships between replicate samples
|
|
|
580 png(mdsPng, width=600, height=600)
|
|
|
581 plotMDS(data, labels=data$samples$group, col=as.numeric(data$samples$group), main="MDS Plot")
|
|
|
582 imageData <- rbind(imageData, c("MDS Plot", "mds.png"))
|
|
|
583 invisible(dev.off())
|
|
|
584
|
|
|
585 pdf(mdsPdf)
|
|
|
586 plotMDS(data, labels=data$samples$group, col=as.numeric(data$samples$group),main="MDS Plot")
|
|
|
587 newEntry <- c("MDS Plot (.pdf)", "mds.pdf")
|
|
|
588 linkData <- rbind(linkData, newEntry)
|
|
|
589 invisible(dev.off())
|
|
|
590
|
|
|
591 # BCV Plot
|
|
|
592 png(bcvPng, width=600, height=600)
|
|
|
593 plotBCV(data, main="BCV Plot")
|
|
|
594 imageData <- rbind(imageData, c("BCV Plot", "bcv.png"))
|
|
|
595 invisible(dev.off())
|
|
|
596
|
|
|
597 pdf(bcvPdf)
|
|
|
598 plotBCV(data, main="BCV Plot")
|
|
|
599 newEntry <- c("BCV Plot (.pdf)", "bcv.pdf")
|
|
|
600 linkData <- rbind(linkData, newEntry)
|
|
|
601 invisible(dev.off())
|
|
|
602
|
|
|
603 if (workMode == "classic") {
|
|
|
604 # Assess differential representation using classic exact testing methodology
|
|
|
605 # in edgeR
|
|
|
606 testData <- exactTest(data, pair=pairData)
|
|
|
607
|
|
|
608 top <- topTags(testData, n=Inf)
|
|
|
609
|
|
|
610 if (selectDirection == "all") {
|
|
|
611 topIDs <- top$table[(top$table$FDR < fdrThresh) &
|
|
|
612 (abs(top$table$logFC) > lfcThresh), 1]
|
|
|
613 } else if (selectDirection == "up") {
|
|
|
614 topIDs <- top$table[(top$table$FDR < fdrThresh) &
|
|
|
615 (top$table$logFC > lfcThresh), 1]
|
|
|
616 } else if (selectDirection == "down") {
|
|
|
617 topIDs <- top$table[(top$table$FDR < fdrThresh) &
|
|
|
618 (top$table$logFC < -lfcThresh), 1]
|
|
|
619 }
|
|
|
620
|
|
|
621 write.table(top, file=topOut, row.names=FALSE, sep="\t")
|
|
|
622
|
|
|
623 linkName <- paste0("Top Tags Table(", pairData[2], "-", pairData[1],
|
|
|
624 ") (.tsv)")
|
|
|
625 linkAddr <- paste0("toptag(", pairData[2], "-", pairData[1], ").tsv")
|
|
|
626 linkData <- rbind(linkData, c(linkName, linkAddr))
|
|
|
627
|
|
|
628 upCount[1] <- sum(top$table$FDR < fdrThresh & top$table$logFC > lfcThresh)
|
|
|
629
|
|
|
630 downCount[1] <- sum(top$table$FDR < fdrThresh &
|
|
|
631 top$table$logFC < -lfcThresh)
|
|
|
632
|
|
|
633 flatCount[1] <- sum(top$table$FDR > fdrThresh |
|
|
|
634 abs(top$table$logFC) < lfcThresh)
|
|
|
635
|
|
|
636
|
|
|
637
|
|
|
638 # Select hairpins with FDR < 0.05 to highlight on plot
|
|
|
639 png(smearPng, width=600, height=600)
|
|
|
640 plotTitle <- gsub(".", " ",
|
|
|
641 paste0("Smear Plot: ", pairData[2], "-", pairData[1]),
|
|
|
642 fixed=TRUE)
|
|
|
643 plotSmear(testData, de.tags=topIDs,
|
|
|
644 pch=20, cex=1.0, main=plotTitle)
|
|
|
645 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2)
|
|
|
646 imgName <- paste0("Smear Plot(", pairData[2], "-", pairData[1], ")")
|
|
|
647 imgAddr <- paste0("smear(", pairData[2], "-", pairData[1],").png")
|
|
|
648 imageData <- rbind(imageData, c(imgName, imgAddr))
|
|
|
649 invisible(dev.off())
|
|
|
650
|
|
|
651 pdf(smearPdf)
|
|
|
652 plotTitle <- gsub(".", " ",
|
|
|
653 paste0("Smear Plot: ", pairData[2], "-", pairData[1]),
|
|
|
654 fixed=TRUE)
|
|
|
655 plotSmear(testData, de.tags=topIDs,
|
|
|
656 pch=20, cex=1.0, main=plotTitle)
|
|
|
657 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2)
|
|
|
658 imgName <- paste0("Smear Plot(", pairData[2], "-", pairData[1], ") (.pdf)")
|
|
|
659 imgAddr <- paste0("smear(", pairData[2], "-", pairData[1], ").pdf")
|
|
|
660 linkData <- rbind(linkData, c(imgName, imgAddr))
|
|
|
661 invisible(dev.off())
|
|
|
662
|
|
|
663 } else if (workMode == "glm") {
|
|
|
664 # Generating design information
|
|
|
665 if (secFactName == "none") {
|
|
|
666
|
|
|
667 factors <- factor(data$sample$group)
|
|
|
668 design <- model.matrix(~0 + factors)
|
|
|
669
|
|
|
670 colnames(design) <- gsub("factors", "", colnames(design), fixed=TRUE)
|
|
|
671
|
|
|
672 } else {
|
|
|
673
|
|
|
674 factors <- factor(data$sample$group)
|
|
|
675
|
|
|
676 if (inputType == "counts") {
|
|
|
677
|
|
|
678 sampleNames <- colnames(counts)
|
|
|
679 matchedIndex <- match(sampleNames, samples$ID)
|
|
|
680 factors <- factor(samples$group[matchedIndex])
|
|
|
681
|
|
|
682 secFactors <- factor(samples[[secFactName]][matchedIndex])
|
|
|
683
|
|
|
684 } else if (inputType == "fastq" || inputType == "pairedFastq") {
|
|
|
685
|
|
|
686 secFactors <- factor(data$sample[[secFactName]])
|
|
|
687
|
|
|
688 }
|
|
|
689
|
|
|
690 design <- model.matrix(~0 + factors + secFactors)
|
|
|
691
|
|
|
692 colnames(design) <- gsub("factors", "", colnames(design), fixed=TRUE)
|
|
|
693 colnames(design) <- gsub("secFactors", secFactName, colnames(design),
|
|
|
694 fixed=TRUE)
|
|
|
695 }
|
|
|
696
|
|
|
697
|
|
|
698 # Split up contrasts seperated by comma into a vector
|
|
|
699 contrastData <- unlist(strsplit(contrastData, split=","))
|
|
|
700
|
|
|
701 for (i in 1:length(contrastData)) {
|
|
|
702 # Generate contrasts information
|
|
|
703 contrasts <- makeContrasts(contrasts=contrastData[i], levels=design)
|
|
|
704
|
|
|
705 # Fit negative bionomial GLM
|
|
|
706 fit <- glmFit(data, design)
|
|
|
707 # Carry out Likelihood ratio test
|
|
|
708 testData <- glmLRT(fit, contrast=contrasts)
|
|
|
709
|
|
|
710 # Select hairpins with FDR < 0.05 to highlight on plot
|
|
|
711 top <- topTags(testData, n=Inf)
|
|
|
712
|
|
|
713 if (selectDirection == "all") {
|
|
|
714 topIDs <- top$table[(top$table$FDR < fdrThresh) &
|
|
|
715 (abs(top$table$logFC) > lfcThresh), 1]
|
|
|
716 } else if (selectDirection == "up") {
|
|
|
717 topIDs <- top$table[(top$table$FDR < fdrThresh) &
|
|
|
718 (top$table$logFC > lfcThresh), 1]
|
|
|
719 } else if (selectDirection == "down") {
|
|
|
720 topIDs <- top$table[(top$table$FDR < fdrThresh) &
|
|
|
721 (top$table$logFC < -lfcThresh), 1]
|
|
|
722 }
|
|
|
723
|
|
|
724 write.table(top, file=topOut[i], row.names=FALSE, sep="\t")
|
|
|
725
|
|
|
726 linkName <- paste0("Top Tags Table(", contrastData[i], ") (.tsv)")
|
|
|
727 linkAddr <- paste0("toptag(", contrastData[i], ").tsv")
|
|
|
728 linkData <- rbind(linkData, c(linkName, linkAddr))
|
|
|
729
|
|
|
730 # Collect counts for differential representation
|
|
|
731 upCount[i] <- sum(top$table$FDR < fdrThresh & top$table$logFC > lfcThresh)
|
|
|
732 downCount[i] <- sum(top$table$FDR < fdrThresh &
|
|
|
733 top$table$logFC < -lfcThresh)
|
|
|
734 flatCount[i] <- sum(top$table$FDR > fdrThresh |
|
|
|
735 abs(top$table$logFC) < lfcThresh)
|
|
|
736
|
|
|
737 # Make a plot of logFC versus logCPM
|
|
|
738 png(smearPng[i], height=600, width=600)
|
|
|
739 plotTitle <- paste("Smear Plot:", gsub(".", " ", contrastData[i],
|
|
|
740 fixed=TRUE))
|
|
|
741 plotSmear(testData, de.tags=topIDs, pch=20, cex=0.8, main=plotTitle)
|
|
|
742 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2)
|
|
|
743
|
|
|
744 imgName <- paste0("Smear Plot(", contrastData[i], ")")
|
|
|
745 imgAddr <- paste0("smear(", contrastData[i], ").png")
|
|
|
746 imageData <- rbind(imageData, c(imgName, imgAddr))
|
|
|
747 invisible(dev.off())
|
|
|
748
|
|
|
749 pdf(smearPdf[i])
|
|
|
750 plotTitle <- paste("Smear Plot:", gsub(".", " ", contrastData[i],
|
|
|
751 fixed=TRUE))
|
|
|
752 plotSmear(testData, de.tags=topIDs, pch=20, cex=0.8, main=plotTitle)
|
|
|
753 abline(h=c(-1, 0, 1), col=c("dodgerblue", "yellow", "dodgerblue"), lty=2)
|
|
|
754
|
|
|
755 linkName <- paste0("Smear Plot(", contrastData[i], ") (.pdf)")
|
|
|
756 linkAddr <- paste0("smear(", contrastData[i], ").pdf")
|
|
|
757 linkData <- rbind(linkData, c(linkName, linkAddr))
|
|
|
758 invisible(dev.off())
|
|
|
759
|
|
|
760 genes <- as.character(data$genes$Gene)
|
|
|
761 unq <- unique(genes)
|
|
|
762 unq <- unq[!is.na(unq)]
|
|
|
763 geneList <- list()
|
|
|
764 for (gene in unq) {
|
|
|
765 if (length(which(genes == gene)) >= hairpinReq) {
|
|
|
766 geneList[[gene]] <- which(genes == gene)
|
|
|
767 }
|
|
|
768 }
|
|
|
769
|
|
|
770 if (wantRoast) {
|
|
|
771 # Input preparaton for roast
|
|
|
772 nrot <- 9999
|
|
|
773 set.seed(602214129)
|
|
|
774 roastData <- mroast(data, index=geneList, design=design,
|
|
|
775 contrast=contrasts, nrot=nrot)
|
|
|
776 roastData <- cbind(GeneID=rownames(roastData), roastData)
|
|
|
777 write.table(roastData, file=roastOut[i], row.names=FALSE, sep="\t")
|
|
|
778 linkName <- paste0("Gene Level Analysis Table(", contrastData[i],
|
|
|
779 ") (.tsv)")
|
|
|
780 linkAddr <- paste0("gene_level(", contrastData[i], ").tsv")
|
|
|
781 linkData <- rbind(linkData, c(linkName, linkAddr))
|
|
|
782 if (selectOpt == "rank") {
|
|
|
783 selectedGenes <- rownames(roastData)[selectVals]
|
|
|
784 } else {
|
|
|
785 selectedGenes <- selectVals
|
|
|
786 }
|
|
|
787
|
|
|
788 if (packageVersion("limma")<"3.19.19") {
|
|
|
789 png(barcodePng[i], width=600, height=length(selectedGenes)*150)
|
|
|
790 } else {
|
|
|
791 png(barcodePng[i], width=600, height=length(selectedGenes)*300)
|
|
|
792 }
|
|
|
793 par(mfrow=c(length(selectedGenes), 1))
|
|
|
794 for (gene in selectedGenes) {
|
|
|
795 barcodeplot(testData$table$logFC, index=geneList[[gene]],
|
|
|
796 main=paste("Barcode Plot for", gene, "(logFCs)",
|
|
|
797 gsub(".", " ", contrastData[i])),
|
|
|
798 labels=c("Positive logFC", "Negative logFC"))
|
|
|
799 }
|
|
|
800 imgName <- paste0("Barcode Plot(", contrastData[i], ")")
|
|
|
801 imgAddr <- paste0("barcode(", contrastData[i], ").png")
|
|
|
802 imageData <- rbind(imageData, c(imgName, imgAddr))
|
|
|
803 dev.off()
|
|
|
804 if (packageVersion("limma")<"3.19.19") {
|
|
|
805 pdf(barcodePdf[i], width=8, height=2)
|
|
|
806 } else {
|
|
|
807 pdf(barcodePdf[i], width=8, height=4)
|
|
|
808 }
|
|
|
809 for (gene in selectedGenes) {
|
|
|
810 barcodeplot(testData$table$logFC, index=geneList[[gene]],
|
|
|
811 main=paste("Barcode Plot for", gene, "(logFCs)",
|
|
|
812 gsub(".", " ", contrastData[i])),
|
|
|
813 labels=c("Positive logFC", "Negative logFC"))
|
|
|
814 }
|
|
|
815 linkName <- paste0("Barcode Plot(", contrastData[i], ") (.pdf)")
|
|
|
816 linkAddr <- paste0("barcode(", contrastData[i], ").pdf")
|
|
|
817 linkData <- rbind(linkData, c(linkName, linkAddr))
|
|
|
818 dev.off()
|
|
|
819 }
|
|
|
820 }
|
|
|
821 }
|
|
|
822
|
|
|
823 # Generate data frame of the significant differences
|
|
|
824 sigDiff <- data.frame(Up=upCount, Flat=flatCount, Down=downCount)
|
|
|
825 if (workMode == "glm") {
|
|
|
826
|
|
|
827 row.names(sigDiff) <- contrastData
|
|
|
828
|
|
|
829 } else if (workMode == "classic") {
|
|
|
830
|
|
|
831 row.names(sigDiff) <- paste0(pairData[2], "-", pairData[1])
|
|
|
832
|
|
|
833 }
|
|
|
834
|
|
|
835 # Output table of summarised counts
|
|
|
836 ID <- rownames(data$counts)
|
|
|
837 outputCounts <- cbind(ID, data$counts)
|
|
|
838 write.table(outputCounts, file=countsOut, row.names=FALSE, sep="\t",
|
|
|
839 quote=FALSE)
|
|
|
840 linkName <- "Counts table (.tsv)"
|
|
|
841 linkAddr <- "counts.tsv"
|
|
|
842 linkData <- rbind(linkData, c(linkName, linkAddr))
|
|
|
843
|
|
|
844 # Record session info
|
|
|
845 writeLines(capture.output(sessionInfo()), sessionOut)
|
|
|
846 linkData <- rbind(linkData, c("Session Info", "session_info.txt"))
|
|
|
847
|
|
|
848 # Record ending time and calculate total run time
|
|
|
849 timeEnd <- as.character(Sys.time())
|
|
|
850 timeTaken <- capture.output(round(difftime(timeEnd,timeStart), digits=3))
|
|
|
851 timeTaken <- gsub("Time difference of ", "", timeTaken, fixed=TRUE)
|
|
|
852 ################################################################################
|
|
|
853 ### HTML Generation
|
|
|
854 ################################################################################
|
|
|
855 # Clear file
|
|
|
856 cat("", file=htmlPath)
|
|
|
857
|
|
|
858 cata("<html>\n")
|
|
|
859 HtmlHead("EdgeR Output")
|
|
|
860
|
|
|
861 cata("<body>\n")
|
|
|
862 cata("<h3>EdgeR Analysis Output:</h3>\n")
|
|
|
863 cata("<h4>Input Summary:</h4>\n")
|
|
|
864 if (inputType == "fastq" || inputType == "pairedFastq") {
|
|
|
865
|
|
|
866 cata("<ul>\n")
|
|
|
867 ListItem(hpReadout[1])
|
|
|
868 ListItem(hpReadout[2])
|
|
|
869 cata("</ul>\n")
|
|
|
870 cata(hpReadout[3], "<br />\n")
|
|
|
871 cata("<ul>\n")
|
|
|
872 ListItem(hpReadout[4])
|
|
|
873 ListItem(hpReadout[7])
|
|
|
874 cata("</ul>\n")
|
|
|
875 cata(hpReadout[8:11], sep="<br />\n")
|
|
|
876 cata("<br />\n")
|
|
|
877 cata("<b>Please check that read percentages are consistent with ")
|
|
|
878 cata("expectations.</b><br >\n")
|
|
|
879
|
|
|
880 } else if (inputType == "counts") {
|
|
|
881
|
|
|
882 cata("<ul>\n")
|
|
|
883 ListItem("Number of Samples: ", ncol(data$counts))
|
|
|
884 ListItem("Number of Hairpins: ", countsRows)
|
|
|
885 ListItem("Number of annotations provided: ", annoRows)
|
|
|
886 ListItem("Number of annotations matched to hairpin: ", annoMatched)
|
|
|
887 cata("</ul>\n")
|
|
|
888
|
|
|
889 }
|
|
|
890
|
|
|
891 cata("The estimated common biological coefficient of variation (BCV) is: ",
|
|
|
892 commonBCV, "<br />\n")
|
|
|
893
|
|
|
894 if (secFactName == "none") {
|
|
|
895
|
|
|
896 cata("No secondary factor specified.<br />\n")
|
|
|
897
|
|
|
898 } else {
|
|
|
899
|
|
|
900 cata("Secondary factor specified as: ", secFactName, "<br />\n")
|
|
|
901
|
|
|
902 }
|
|
|
903
|
|
|
904 cata("<h4>Output:</h4>\n")
|
|
|
905 cata("PDF copies of JPEGS available in 'Plots' section.<br />\n")
|
|
|
906 for (i in 1:nrow(imageData)) {
|
|
|
907 if (grepl("barcode", imageData$Link[i])) {
|
|
|
908
|
|
|
909 if (packageVersion("limma")<"3.19.19") {
|
|
|
910
|
|
|
911 HtmlImage(imageData$Link[i], imageData$Label[i],
|
|
|
912 height=length(selectedGenes)*150)
|
|
|
913
|
|
|
914 } else {
|
|
|
915
|
|
|
916 HtmlImage(imageData$Link[i], imageData$Label[i],
|
|
|
917 height=length(selectedGenes)*300)
|
|
|
918
|
|
|
919 }
|
|
|
920 } else {
|
|
|
921
|
|
|
922 HtmlImage(imageData$Link[i], imageData$Label[i])
|
|
|
923
|
|
|
924 }
|
|
|
925 }
|
|
|
926 cata("<br />\n")
|
|
|
927
|
|
|
928 cata("<h4>Differential Representation Counts:</h4>\n")
|
|
|
929
|
|
|
930 cata("<table border=\"1\" cellpadding=\"4\">\n")
|
|
|
931 cata("<tr>\n")
|
|
|
932 TableItem()
|
|
|
933 for (i in colnames(sigDiff)) {
|
|
|
934 TableHeadItem(i)
|
|
|
935 }
|
|
|
936 cata("</tr>\n")
|
|
|
937 for (i in 1:nrow(sigDiff)) {
|
|
|
938 cata("<tr>\n")
|
|
|
939 TableHeadItem(unmake.names(row.names(sigDiff)[i]))
|
|
|
940 for (j in 1:ncol(sigDiff)) {
|
|
|
941 TableItem(as.character(sigDiff[i, j]))
|
|
|
942 }
|
|
|
943 cata("</tr>\n")
|
|
|
944 }
|
|
|
945 cata("</table>")
|
|
|
946
|
|
|
947 cata("<h4>Plots:</h4>\n")
|
|
|
948 for (i in 1:nrow(linkData)) {
|
|
|
949 if (grepl(".pdf", linkData$Link[i])) {
|
|
|
950 HtmlLink(linkData$Link[i], linkData$Label[i])
|
|
|
951 }
|
|
|
952 }
|
|
|
953
|
|
|
954 cata("<h4>Tables:</h4>\n")
|
|
|
955 for (i in 1:nrow(linkData)) {
|
|
|
956 if (grepl(".tsv", linkData$Link[i])) {
|
|
|
957 HtmlLink(linkData$Link[i], linkData$Label[i])
|
|
|
958 }
|
|
|
959 }
|
|
|
960
|
|
|
961 cata("<p>Alt-click links to download file.</p>\n")
|
|
|
962 cata("<p>Click floppy disc icon on associated history item to download ")
|
|
|
963 cata("all files.</p>\n")
|
|
|
964 cata("<p>.tsv files can be viewed in Excel or any spreadsheet program.</p>\n")
|
|
|
965
|
|
|
966 cata("<h4>Additional Information:</h4>\n")
|
|
|
967
|
|
|
968 if (inputType == "fastq") {
|
|
|
969
|
|
|
970 ListItem("Data was gathered from fastq raw read file(s).")
|
|
|
971
|
|
|
972 } else if (inputType == "counts") {
|
|
|
973
|
|
|
974 ListItem("Data was gathered from a table of counts.")
|
|
|
975
|
|
|
976 }
|
|
|
977
|
|
|
978 if (cpmReq != 0 && sampleReq != 0) {
|
|
|
979 tempStr <- paste("Target sequences without more than", cpmReq,
|
|
|
980 "CPM in at least", sampleReq, "samples are insignificant",
|
|
|
981 "and filtered out.")
|
|
|
982 ListItem(tempStr)
|
|
|
983
|
|
|
984 filterProp <- round(filteredCount/preFilterCount*100, digits=2)
|
|
|
985 tempStr <- paste0(filteredCount, " of ", preFilterCount," (", filterProp,
|
|
|
986 "%) target sequences were filtered out for low ",
|
|
|
987 "count-per-million.")
|
|
|
988 ListItem(tempStr)
|
|
|
989 }
|
|
|
990
|
|
|
991 if (readReq != 0) {
|
|
|
992 tempStr <- paste("Samples that did not produce more than", readReq,
|
|
|
993 "counts were filtered out.")
|
|
|
994 ListItem(tempStr)
|
|
|
995
|
|
|
996 tempStr <- paste0(sampleFilterCount, " samples were filtered out for low ",
|
|
|
997 "counts.")
|
|
|
998 ListItem(tempStr)
|
|
|
999 }
|
|
|
1000
|
|
|
1001 if (exists("filteredSamples")) {
|
|
|
1002 tempStr <- paste("The following samples were filtered out for having zero",
|
|
|
1003 "library size: ", filteredSamples)
|
|
|
1004 ListItem(tempStr)
|
|
|
1005 }
|
|
|
1006
|
|
|
1007 if (workMode == "classic") {
|
|
|
1008 ListItem("An exact test was performed on each target sequence.")
|
|
|
1009 } else if (workMode == "glm") {
|
|
|
1010 ListItem("A generalised linear model was fitted to each target sequence.")
|
|
|
1011 }
|
|
|
1012
|
|
|
1013 cit <- character()
|
|
|
1014 link <-character()
|
|
|
1015 link[1] <- paste0("<a href=\"",
|
|
|
1016 "http://www.bioconductor.org/packages/release/bioc/",
|
|
|
1017 "vignettes/limma/inst/doc/usersguide.pdf",
|
|
|
1018 "\">", "limma User's Guide", "</a>.")
|
|
|
1019 link[2] <- paste0("<a href=\"",
|
|
|
1020 "http://www.bioconductor.org/packages/release/bioc/",
|
|
|
1021 "vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf",
|
|
|
1022 "\">", "edgeR User's Guide", "</a>")
|
|
|
1023
|
|
|
1024 cit[1] <- paste("Robinson MD, McCarthy DJ and Smyth GK (2010).",
|
|
|
1025 "edgeR: a Bioconductor package for differential",
|
|
|
1026 "expression analysis of digital gene expression",
|
|
|
1027 "data. Bioinformatics 26, 139-140")
|
|
|
1028 cit[2] <- paste("Robinson MD and Smyth GK (2007). Moderated statistical tests",
|
|
|
1029 "for assessing differences in tag abundance. Bioinformatics",
|
|
|
1030 "23, 2881-2887")
|
|
|
1031 cit[3] <- paste("Robinson MD and Smyth GK (2008). Small-sample estimation of",
|
|
|
1032 "negative binomial dispersion, with applications to SAGE data.",
|
|
|
1033 "Biostatistics, 9, 321-332")
|
|
|
1034
|
|
|
1035 cit[4] <- paste("McCarthy DJ, Chen Y and Smyth GK (2012). Differential",
|
|
|
1036 "expression analysis of multifactor RNA-Seq experiments with",
|
|
|
1037 "respect to biological variation. Nucleic Acids Research 40,",
|
|
|
1038 "4288-4297")
|
|
|
1039
|
|
|
1040 cata("<h4>Citations</h4>")
|
|
|
1041 cata("<ol>\n")
|
|
|
1042 ListItem(cit[1])
|
|
|
1043 ListItem(cit[2])
|
|
|
1044 ListItem(cit[3])
|
|
|
1045 ListItem(cit[4])
|
|
|
1046 cata("</ol>\n")
|
|
|
1047
|
|
|
1048 cata("<p>Report problems to: su.s@wehi.edu.au</p>\n")
|
|
|
1049
|
|
|
1050 for (i in 1:nrow(linkData)) {
|
|
|
1051 if (grepl("session_info", linkData$Link[i])) {
|
|
|
1052 HtmlLink(linkData$Link[i], linkData$Label[i])
|
|
|
1053 }
|
|
|
1054 }
|
|
|
1055
|
|
|
1056 cata("<table border=\"0\">\n")
|
|
|
1057 cata("<tr>\n")
|
|
|
1058 TableItem("Task started at:"); TableItem(timeStart)
|
|
|
1059 cata("</tr>\n")
|
|
|
1060 cata("<tr>\n")
|
|
|
1061 TableItem("Task ended at:"); TableItem(timeEnd)
|
|
|
1062 cata("</tr>\n")
|
|
|
1063 cata("<tr>\n")
|
|
|
1064 TableItem("Task run time:"); TableItem(timeTaken)
|
|
|
1065 cata("<tr>\n")
|
|
|
1066 cata("</table>\n")
|
|
|
1067
|
|
|
1068 cata("</body>\n")
|
|
|
1069 cata("</html>")
|
