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1 # This tool takes in a matrix of feature counts as well as gene annotations and
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2 # outputs a table of top expressions as well as various plots for differential
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3 # expression analysis
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4 #
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5 # ARGS: 1.countPath -Path to RData input containing counts
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6 # 2.annoPath -Path to RData input containing gene annotations
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7 # 3.htmlPath -Path to html file linking to other outputs
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8 # 4.outPath -Path to folder to write all output to
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9 # 5.rdaOpt -String specifying if RData should be saved
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10 # 6.normOpt -String specifying type of normalisation used
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11 # 7.weightOpt -String specifying usage of weights
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12 # 8.contrastData -String containing contrasts of interest
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13 # 9.cpmReq -Float specifying cpm requirement
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14 # 10.sampleReq -Integer specifying cpm requirement
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15 # 11.pAdjOpt -String specifying the p-value adjustment method
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16 # 12.pValReq -Float specifying the p-value requirement
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17 # 13.lfcReq -Float specifying the log-fold-change requirement
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18 # 14.factorData -String containing factor names and values
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19 #
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20 # OUT: Voom Plot
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21 # BCV Plot
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22 # MA Plot
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23 # Top Expression Table
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24 # HTML file linking to the ouputs
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25 #
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26 # Author: Shian Su - registertonysu@gmail.com - Jan 2014
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27
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28 # Record starting time
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29 timeStart <- as.character(Sys.time())
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30
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31 # Load all required libraries
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32 library(methods, quietly=TRUE, warn.conflicts=FALSE)
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33 library(statmod, quietly=TRUE, warn.conflicts=FALSE)
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34 library(splines, quietly=TRUE, warn.conflicts=FALSE)
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35 library(edgeR, quietly=TRUE, warn.conflicts=FALSE)
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36 library(limma, quietly=TRUE, warn.conflicts=FALSE)
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37 library(scales, quietly=TRUE, warn.conflicts=FALSE)
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38
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39 if (packageVersion("limma") < "3.20.1") {
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40 stop("Please update 'limma' to version >= 3.20.1 to run this tool")
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41 }
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42
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43 ################################################################################
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44 ### Function Delcaration
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45 ################################################################################
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46 # Function to sanitise contrast equations so there are no whitespaces
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47 # surrounding the arithmetic operators, leading or trailing whitespace
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48 sanitiseEquation <- function(equation) {
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49 equation <- gsub(" *[+] *", "+", equation)
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50 equation <- gsub(" *[-] *", "-", equation)
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51 equation <- gsub(" *[/] *", "/", equation)
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52 equation <- gsub(" *[*] *", "*", equation)
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53 equation <- gsub("^\\s+|\\s+$", "", equation)
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54 return(equation)
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55 }
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56
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57 # Function to sanitise group information
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58 sanitiseGroups <- function(string) {
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59 string <- gsub(" *[,] *", ",", string)
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60 string <- gsub("^\\s+|\\s+$", "", string)
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61 return(string)
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62 }
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63
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64 # Function to change periods to whitespace in a string
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65 unmake.names <- function(string) {
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66 string <- gsub(".", " ", string, fixed=TRUE)
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67 return(string)
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68 }
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69
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70 # Generate output folder and paths
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71 makeOut <- function(filename) {
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72 return(paste0(outPath, "/", filename))
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73 }
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74
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75 # Generating design information
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76 pasteListName <- function(string) {
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77 return(paste0("factors$", string))
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78 }
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79
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80 # Create cata function: default path set, default seperator empty and appending
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81 # true by default (Ripped straight from the cat function with altered argument
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82 # defaults)
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83 cata <- function(..., file = htmlPath, sep = "", fill = FALSE, labels = NULL,
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84 append = TRUE) {
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85 if (is.character(file))
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86 if (file == "")
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87 file <- stdout()
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88 else if (substring(file, 1L, 1L) == "|") {
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89 file <- pipe(substring(file, 2L), "w")
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90 on.exit(close(file))
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91 }
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92 else {
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93 file <- file(file, ifelse(append, "a", "w"))
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94 on.exit(close(file))
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95 }
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96 .Internal(cat(list(...), file, sep, fill, labels, append))
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97 }
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98
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99 # Function to write code for html head and title
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100 HtmlHead <- function(title) {
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101 cata("<head>\n")
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102 cata("<title>", title, "</title>\n")
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103 cata("</head>\n")
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104 }
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105
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106 # Function to write code for html links
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107 HtmlLink <- function(address, label=address) {
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108 cata("<a href=\"", address, "\" target=\"_blank\">", label, "</a><br />\n")
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109 }
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110
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111 # Function to write code for html images
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112 HtmlImage <- function(source, label=source, height=600, width=600) {
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113 cata("<img src=\"", source, "\" alt=\"", label, "\" height=\"", height)
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114 cata("\" width=\"", width, "\"/>\n")
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115 }
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116
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117 # Function to write code for html list items
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118 ListItem <- function(...) {
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119 cata("<li>", ..., "</li>\n")
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120 }
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121
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122 TableItem <- function(...) {
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123 cata("<td>", ..., "</td>\n")
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124 }
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125
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126 TableHeadItem <- function(...) {
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127 cata("<th>", ..., "</th>\n")
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128 }
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129
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130 ################################################################################
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131 ### Input Processing
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132 ################################################################################
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133
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134 # Collects arguments from command line
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135 argv <- commandArgs(TRUE)
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136
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137 # Grab arguments
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138 countPath <- as.character(argv[1])
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139 annoPath <- as.character(argv[2])
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140 htmlPath <- as.character(argv[3])
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141 outPath <- as.character(argv[4])
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142 rdaOpt <- as.character(argv[5])
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143 normOpt <- as.character(argv[6])
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144 weightOpt <- as.character(argv[7])
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145 contrastData <- as.character(argv[8])
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146 cpmReq <- as.numeric(argv[9])
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147 sampleReq <- as.numeric(argv[10])
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148 pAdjOpt <- as.character(argv[11])
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149 pValReq <- as.numeric(argv[12])
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150 lfcReq <- as.numeric(argv[13])
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151 factorData <- list()
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152 for (i in 14:length(argv)) {
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153 newFact <- unlist(strsplit(as.character(argv[i]), split="::"))
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154 factorData <- rbind(factorData, newFact)
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155 } # Factors have the form: FACT_NAME::LEVEL,LEVEL,LEVEL,LEVEL,...
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156
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157 # Process arguments
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158 if (weightOpt=="yes") {
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159 wantWeight <- TRUE
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160 } else {
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161 wantWeight <- FALSE
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162 }
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163
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164 if (rdaOpt=="yes") {
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165 wantRda <- TRUE
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166 } else {
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167 wantRda <- FALSE
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168 }
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169
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170 if (annoPath=="None") {
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171 haveAnno <- FALSE
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172 } else {
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173 haveAnno <- TRUE
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174 }
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175
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176 # Set the row names to be the name of the factor and delete first row
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177 row.names(factorData) <- factorData[, 1]
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178 factorData <- factorData[, -1]
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179 factorData <- sapply(factorData, sanitiseGroups)
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180 factorData <- sapply(factorData, strsplit, split=",")
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181 factorData <- sapply(factorData, make.names)
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182
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183 # Transform factor data into data frame of R factor objects
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184 factors <- data.frame(factorData)
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185
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186 #Create output directory
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187 dir.create(outPath, showWarnings=FALSE)
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188
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189 # Split up contrasts seperated by comma into a vector then sanitise
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190 contrastData <- unlist(strsplit(contrastData, split=","))
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191 contrastData <- sanitiseEquation(contrastData)
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192 contrastData <- gsub(" ", ".", contrastData, fixed=TRUE)
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193
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194 bcvOutPdf <- makeOut("bcvplot.pdf")
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195 bcvOutPng <- makeOut("bcvplot.png")
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196 mdsOutPdf <- makeOut("mdsplot.pdf")
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197 mdsOutPng <- makeOut("mdsplot.png")
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198 voomOutPdf <- makeOut("voomplot.pdf")
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199 voomOutPng <- makeOut("voomplot.png")
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200 maOutPdf <- character() # Initialise character vector
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201 maOutPng <- character()
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202 topOut <- character()
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203 for (i in 1:length(contrastData)) {
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204 maOutPdf[i] <- makeOut(paste0("maplot(", contrastData[i], ").pdf"))
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205 maOutPng[i] <- makeOut(paste0("maplot(", contrastData[i], ").png"))
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206 topOut[i] <- makeOut(paste0("toptab(", contrastData[i], ").tsv"))
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207 } # Save output paths for each contrast as vectors
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208 rdaOut <- makeOut("RData.rda")
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209 sessionOut <- makeOut("session_info.txt")
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210
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211 # Initialise data for html links and images, data frame with columns Label and
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212 # Link
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213 linkData <- data.frame(Label=character(), Link=character(),
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214 stringsAsFactors=FALSE)
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215 imageData <- data.frame(Label=character(), Link=character(),
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216 stringsAsFactors=FALSE)
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217
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218 # Initialise vectors for storage of up/down/neutral regulated counts
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219 upCount <- numeric()
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220 downCount <- numeric()
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221 flatCount <- numeric()
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222
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223 # Read in counts and geneanno data
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224 counts <- read.table(countPath, header=TRUE, sep="\t")
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225 row.names(counts) <- counts$GeneID
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226 counts <- counts[ , !(colnames(counts)=="GeneID")]
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227 countsRows <- nrow(counts)
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228 if (haveAnno) {
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229 geneanno <- read.table(annoPath, header=TRUE, sep="\t")
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230 }
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231
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232 ################################################################################
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233 ### Data Processing
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234 ################################################################################
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235
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236 # Extract counts and annotation data
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237 data <- list()
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238 data$counts <- counts
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239 if (haveAnno) {
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240 data$genes <- geneanno
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241 } else {
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242 data$genes <- data.frame(GeneID=row.names(counts))
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243 }
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244
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245 # Filter out genes that do not have a required cpm in a required number of
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246 # samples
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247 preFilterCount <- nrow(data$counts)
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248 sel <- rowSums(cpm(data$counts) > cpmReq) >= sampleReq
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249 data$counts <- data$counts[sel, ]
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250 data$genes <- data$genes[sel, ]
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251 postFilterCount <- nrow(data$counts)
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252 filteredCount <- preFilterCount-postFilterCount
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253
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254 # Creating naming data
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255 samplenames <- colnames(data$counts)
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256 sampleanno <- data.frame("sampleID"=samplenames, factors)
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257
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258 # Generating the DGEList object "data"
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259 data$samples <- sampleanno
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260 data$samples$lib.size <- colSums(data$counts)
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261 data$samples$norm.factors <- 1
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262 row.names(data$samples) <- colnames(data$counts)
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263 data <- new("DGEList", data)
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264
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265 factorList <- sapply(names(factors), pasteListName)
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266 formula <- "~0"
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267 for (i in 1:length(factorList)) {
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268 formula <- paste(formula, factorList[i], sep="+")
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269 }
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270 formula <- formula(formula)
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271 design <- model.matrix(formula)
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272 for (i in 1:length(factorList)) {
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273 colnames(design) <- gsub(factorList[i], "", colnames(design), fixed=TRUE)
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274 }
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275
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276 # Calculating normalising factor, estimating dispersion
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277 data <- calcNormFactors(data, method=normOpt)
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278 #data <- estimateDisp(data, design=design, robust=TRUE)
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279
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280 # Generate contrasts information
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281 contrasts <- makeContrasts(contrasts=contrastData, levels=design)
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282
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283 # Name rows of factors according to their sample
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284 row.names(factors) <- names(data$counts)
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285
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286 ################################################################################
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287 ### Data Output
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288 ################################################################################
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289
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290 # BCV Plot
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291 #png(bcvOutPng, width=600, height=600)
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292 #plotBCV(data, main="BCV Plot")
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293 #imageData[1, ] <- c("BCV Plot", "bcvplot.png")
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294 #invisible(dev.off())
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295
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296 #pdf(bcvOutPdf)
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297 #plotBCV(data, main="BCV Plot")
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298 #invisible(dev.off())
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299
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300 if (wantWeight) {
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301 # Creating voom data object and plot
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302 png(voomOutPng, width=1000, height=600)
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303 vData <- voomWithQualityWeights(data, design=design, plot=TRUE)
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304 imageData[1, ] <- c("Voom Plot", "voomplot.png")
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305 invisible(dev.off())
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306
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307 pdf(voomOutPdf, width=14)
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308 vData <- voomWithQualityWeights(data, design=design, plot=TRUE)
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309 linkData[1, ] <- c("Voom Plot (.pdf)", "voomplot.pdf")
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310 invisible(dev.off())
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311
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312 # Generating fit data and top table with weights
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313 wts <- vData$weights
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314 voomFit <- lmFit(vData, design, weights=wts)
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315
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316 } else {
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317 # Creating voom data object and plot
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318 png(voomOutPng, width=600, height=600)
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319 vData <- voom(data, design=design, plot=TRUE)
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320 imageData[1, ] <- c("Voom Plot", "voomplot.png")
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321 invisible(dev.off())
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322
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323 pdf(voomOutPdf)
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324 vData <- voom(data, design=design, plot=TRUE)
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325 linkData[1, ] <- c("Voom Plot (.pdf)", "voomplot.pdf")
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326 invisible(dev.off())
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327
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328 # Generate voom fit
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329 voomFit <- lmFit(vData, design)
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330
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331 }
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332
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333 # Fit linear model and estimate dispersion with eBayes
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334 voomFit <- contrasts.fit(voomFit, contrasts)
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335 voomFit <- eBayes(voomFit)
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336
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337 # Plot MDS
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338 labels <- names(counts)
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339 png(mdsOutPng, width=600, height=600)
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340 # Currently only using a single factor
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341 plotMDS(vData, labels=labels, col=as.numeric(factors[, 1]), cex=0.8)
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342 imgName <- "Voom Plot"
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343 imgAddr <- "mdsplot.png"
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344 imageData <- rbind(imageData, c(imgName, imgAddr))
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345 invisible(dev.off())
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346
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347 pdf(mdsOutPdf)
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348 plotMDS(vData, labels=labels, cex=0.5)
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349 linkName <- paste0("MDS Plot (.pdf)")
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350 linkAddr <- paste0("mdsplot.pdf")
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351 linkData <- rbind(linkData, c(linkName, linkAddr))
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352 invisible(dev.off())
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353
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354
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355 for (i in 1:length(contrastData)) {
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356
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357 status = decideTests(voomFit[, i], adjust.method=pAdjOpt, p.value=pValReq,
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358 lfc=lfcReq)
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359
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360 sumStatus <- summary(status)
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361
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362 # Collect counts for differential expression
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363 upCount[i] <- sumStatus["1",]
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364 downCount[i] <- sumStatus["-1",]
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365 flatCount[i] <- sumStatus["0",]
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366
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367 # Write top expressions table
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368 top <- topTable(voomFit, coef=i, number=Inf, sort.by="P")
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369 write.table(top, file=topOut[i], row.names=FALSE, sep="\t")
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370
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371 linkName <- paste0("Top Differential Expressions(", contrastData[i],
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372 ") (.tsv)")
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373 linkAddr <- paste0("toptab(", contrastData[i], ").tsv")
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374 linkData <- rbind(linkData, c(linkName, linkAddr))
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375
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376 # Plot MA (log ratios vs mean average) using limma package on weighted data
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377 pdf(maOutPdf[i])
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378 limma::plotMA(voomFit, status=status, coef=i,
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379 main=paste("MA Plot:", unmake.names(contrastData[i])),
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380 col=alpha(c("firebrick", "blue"), 0.4), values=c("1", "-1"),
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381 xlab="Average Expression", ylab="logFC")
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382
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383 abline(h=0, col="grey", lty=2)
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384
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385 linkName <- paste0("MA Plot(", contrastData[i], ")", " (.pdf)")
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386 linkAddr <- paste0("maplot(", contrastData[i], ").pdf")
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387 linkData <- rbind(linkData, c(linkName, linkAddr))
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388 invisible(dev.off())
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389
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390 png(maOutPng[i], height=600, width=600)
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391 limma::plotMA(voomFit, status=status, coef=i,
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392 main=paste("MA Plot:", unmake.names(contrastData[i])),
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393 col=alpha(c("firebrick", "blue"), 0.4), values=c("1", "-1"),
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394 xlab="Average Expression", ylab="logFC")
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395
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396 abline(h=0, col="grey", lty=2)
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397
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398 imgName <- paste0("MA Plot(", contrastData[i], ")")
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399 imgAddr <- paste0("maplot(", contrastData[i], ").png")
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400 imageData <- rbind(imageData, c(imgName, imgAddr))
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401 invisible(dev.off())
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402 }
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403 sigDiff <- data.frame(Up=upCount, Flat=flatCount, Down=downCount)
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404 row.names(sigDiff) <- contrastData
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405
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406 # Save relevant items as rda object
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407 if (wantRda) {
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408 if (wantWeight) {
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409 save(data, status, vData, labels, factors, wts, voomFit, top, contrasts,
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410 design,
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411 file=rdaOut, ascii=TRUE)
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412 } else {
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413 save(data, status, vData, labels, factors, voomFit, top, contrasts, design,
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414 file=rdaOut, ascii=TRUE)
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415 }
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416 linkData <- rbind(linkData, c("RData (.rda)", "RData.rda"))
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417 }
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418
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419 # Record session info
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420 writeLines(capture.output(sessionInfo()), sessionOut)
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421 linkData <- rbind(linkData, c("Session Info", "session_info.txt"))
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422
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423 # Record ending time and calculate total run time
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424 timeEnd <- as.character(Sys.time())
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425 timeTaken <- capture.output(round(difftime(timeEnd,timeStart), digits=3))
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426 timeTaken <- gsub("Time difference of ", "", timeTaken, fixed=TRUE)
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427 ################################################################################
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428 ### HTML Generation
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429 ################################################################################
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430
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431 # Clear file
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432 cat("", file=htmlPath)
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433
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434 cata("<html>\n")
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435
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436 cata("<body>\n")
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437 cata("<h3>Limma Voom Analysis Output:</h3>\n")
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|
438 cata("PDF copies of JPEGS available in 'Plots' section.<br />\n")
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|
439 if (wantWeight) {
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|
440 HtmlImage(imageData$Link[1], imageData$Label[1], width=1000)
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441 } else {
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|
442 HtmlImage(imageData$Link[1], imageData$Label[1])
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443 }
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444
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|
|
445 for (i in 2:nrow(imageData)) {
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446 HtmlImage(imageData$Link[i], imageData$Label[i])
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|
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447 }
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|
448
|
|
|
449 cata("<h4>Differential Expression Counts:</h4>\n")
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|
|
450
|
|
|
451 cata("<table border=\"1\" cellpadding=\"4\">\n")
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|
452 cata("<tr>\n")
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|
453 TableItem()
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|
|
454 for (i in colnames(sigDiff)) {
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|
455 TableHeadItem(i)
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|
|
456 }
|
|
|
457 cata("</tr>\n")
|
|
|
458 for (i in 1:nrow(sigDiff)) {
|
|
|
459 cata("<tr>\n")
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|
|
460 TableHeadItem(unmake.names(row.names(sigDiff)[i]))
|
|
|
461 for (j in 1:ncol(sigDiff)) {
|
|
|
462 TableItem(as.character(sigDiff[i, j]))
|
|
|
463 }
|
|
|
464 cata("</tr>\n")
|
|
|
465 }
|
|
|
466 cata("</table>")
|
|
|
467
|
|
|
468 cata("<h4>Plots:</h4>\n")
|
|
|
469 for (i in 1:nrow(linkData)) {
|
|
|
470 if (grepl(".pdf", linkData$Link[i])) {
|
|
|
471 HtmlLink(linkData$Link[i], linkData$Label[i])
|
|
|
472 }
|
|
|
473 }
|
|
|
474
|
|
|
475 cata("<h4>Tables:</h4>\n")
|
|
|
476 for (i in 1:nrow(linkData)) {
|
|
|
477 if (grepl(".tsv", linkData$Link[i])) {
|
|
|
478 HtmlLink(linkData$Link[i], linkData$Label[i])
|
|
|
479 }
|
|
|
480 }
|
|
|
481
|
|
|
482 if (wantRda) {
|
|
|
483 cata("<h4>R Data Object:</h4>\n")
|
|
|
484 for (i in 1:nrow(linkData)) {
|
|
|
485 if (grepl(".rda", linkData$Link[i])) {
|
|
|
486 HtmlLink(linkData$Link[i], linkData$Label[i])
|
|
|
487 }
|
|
|
488 }
|
|
|
489 }
|
|
|
490
|
|
|
491 cata("<p>Alt-click links to download file.</p>\n")
|
|
|
492 cata("<p>Click floppy disc icon associated history item to download ")
|
|
|
493 cata("all files.</p>\n")
|
|
|
494 cata("<p>.tsv files can be viewed in Excel or any spreadsheet program.</p>\n")
|
|
|
495
|
|
|
496 cata("<h4>Additional Information</h4>\n")
|
|
|
497 cata("<ul>\n")
|
|
|
498 if (cpmReq!=0 && sampleReq!=0) {
|
|
|
499 tempStr <- paste("Genes without more than", cpmReq,
|
|
|
500 "CPM in at least", sampleReq, "samples are insignificant",
|
|
|
501 "and filtered out.")
|
|
|
502 ListItem(tempStr)
|
|
|
503 filterProp <- round(filteredCount/preFilterCount*100, digits=2)
|
|
|
504 tempStr <- paste0(filteredCount, " of ", preFilterCount," (", filterProp,
|
|
|
505 "%) genes were filtered out for low expression.")
|
|
|
506 ListItem(tempStr)
|
|
|
507 }
|
|
|
508 ListItem(normOpt, " was the method used to normalise library sizes.")
|
|
|
509 if (wantWeight) {
|
|
|
510 ListItem("Weights were applied to samples.")
|
|
|
511 } else {
|
|
|
512 ListItem("Weights were not applied to samples.")
|
|
|
513 }
|
|
|
514 if (pAdjOpt!="none") {
|
|
|
515 if (pAdjOpt=="BH" || pAdjOpt=="BY") {
|
|
|
516 tempStr <- paste0("MA-Plot highlighted genes are significant at FDR ",
|
|
|
517 "of ", pValReq," and exhibit log2-fold-change of at ",
|
|
|
518 "least ", lfcReq, ".")
|
|
|
519 ListItem(tempStr)
|
|
|
520 } else if (pAdjOpt=="holm") {
|
|
|
521 tempStr <- paste0("MA-Plot highlighted genes are significant at adjusted ",
|
|
|
522 "p-value of ", pValReq," by the Holm(1979) ",
|
|
|
523 "method, and exhibit log2-fold-change of at least ",
|
|
|
524 lfcReq, ".")
|
|
|
525 ListItem(tempStr)
|
|
|
526 }
|
|
|
527 } else {
|
|
|
528 tempStr <- paste0("MA-Plot highlighted genes are significant at p-value ",
|
|
|
529 "of ", pValReq," and exhibit log2-fold-change of at ",
|
|
|
530 "least ", lfcReq, ".")
|
|
|
531 ListItem(tempStr)
|
|
|
532 }
|
|
|
533 cata("</ul>\n")
|
|
|
534
|
|
|
535 cata("<h4>Summary of experimental data:</h4>\n")
|
|
|
536
|
|
|
537 cata("<p>*CHECK THAT SAMPLES ARE ASSOCIATED WITH CORRECT GROUP*</p>\n")
|
|
|
538
|
|
|
539 cata("<table border=\"1\" cellpadding=\"3\">\n")
|
|
|
540 cata("<tr>\n")
|
|
|
541 TableItem()
|
|
|
542 for (i in names(factors)) {
|
|
|
543 TableHeadItem(i)
|
|
|
544 }
|
|
|
545 cata("</tr>\n")
|
|
|
546
|
|
|
547 for (i in 1:nrow(factors)) {
|
|
|
548 cata("<tr>\n")
|
|
|
549 TableHeadItem(row.names(factors)[i])
|
|
|
550 for (j in ncol(factors)) {
|
|
|
551 TableItem(as.character(unmake.names(factors[i, j])))
|
|
|
552 }
|
|
|
553 cata("</tr>\n")
|
|
|
554 }
|
|
|
555 cata("</table>")
|
|
|
556
|
|
|
557 cit <- character()
|
|
|
558 link <- character()
|
|
|
559 link[1] <- paste0("<a href=\"",
|
|
|
560 "http://www.bioconductor.org/packages/release/bioc/",
|
|
|
561 "vignettes/limma/inst/doc/usersguide.pdf",
|
|
|
562 "\">", "limma User's Guide", "</a>.")
|
|
|
563
|
|
|
564 link[2] <- paste0("<a href=\"",
|
|
|
565 "http://www.bioconductor.org/packages/release/bioc/",
|
|
|
566 "vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf",
|
|
|
567 "\">", "edgeR User's Guide", "</a>")
|
|
|
568
|
|
|
569 cit[1] <- paste("Please cite the paper below for the limma software itself.",
|
|
|
570 "Please also try to cite the appropriate methodology articles",
|
|
|
571 "that describe the statistical methods implemented in limma,",
|
|
|
572 "depending on which limma functions you are using. The",
|
|
|
573 "methodology articles are listed in Section 2.1 of the",
|
|
|
574 link[1],
|
|
|
575 "Cite no. 3 only if sample weights were used.")
|
|
|
576 cit[2] <- paste("Smyth, GK (2005). Limma: linear models for microarray data.",
|
|
|
577 "In: 'Bioinformatics and Computational Biology Solutions using",
|
|
|
578 "R and Bioconductor'. R. Gentleman, V. Carey, S. doit,.",
|
|
|
579 "Irizarry, W. Huber (eds), Springer, New York, pages 397-420.")
|
|
|
580 cit[3] <- paste("Please cite the first paper for the software itself and the",
|
|
|
581 "other papers for the various original statistical methods",
|
|
|
582 "implemented in edgeR. See Section 1.2 in the", link[2],
|
|
|
583 "for more detail.")
|
|
|
584 cit[4] <- paste("Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a",
|
|
|
585 "Bioconductor package for differential expression analysis",
|
|
|
586 "of digital gene expression data. Bioinformatics 26, 139-140")
|
|
|
587 cit[5] <- paste("Robinson MD and Smyth GK (2007). Moderated statistical tests",
|
|
|
588 "for assessing differences in tag abundance. Bioinformatics",
|
|
|
589 "23, 2881-2887")
|
|
|
590 cit[6] <- paste("Robinson MD and Smyth GK (2008). Small-sample estimation of",
|
|
|
591 "negative binomial dispersion, with applications to SAGE data.",
|
|
|
592 "Biostatistics, 9, 321-332")
|
|
|
593 cit[7] <- paste("McCarthy DJ, Chen Y and Smyth GK (2012). Differential",
|
|
|
594 "expression analysis of multifactor RNA-Seq experiments with",
|
|
|
595 "respect to biological variation. Nucleic Acids Research 40,",
|
|
|
596 "4288-4297")
|
|
|
597 cit[8] <- paste("Law, CW, Chen, Y, Shi, W, and Smyth, GK (2014). Voom:",
|
|
|
598 "precision weights unlock linear model analysis tools for",
|
|
|
599 "RNA-seq read counts. Genome Biology 15, R29.")
|
|
|
600 cit[9] <- paste("Ritchie, M. E., Diyagama, D., Neilson, J., van Laar,",
|
|
|
601 "R., Dobrovic, A., Holloway, A., and Smyth, G. K. (2006).",
|
|
|
602 "Empirical array quality weights for microarray data.",
|
|
|
603 "BMC Bioinformatics 7, Article 261.")
|
|
|
604 cata("<h3>Citations</h3>\n")
|
|
|
605
|
|
|
606 cata("<h4>limma</h4>\n")
|
|
|
607 cata(cit[1], "\n")
|
|
|
608 cata("<ol>\n")
|
|
|
609 ListItem(cit[2])
|
|
|
610 ListItem(cit[8])
|
|
|
611 ListItem(cit[9])
|
|
|
612 cata("</ol>\n")
|
|
|
613
|
|
|
614 cata("<h4>edgeR</h4>\n")
|
|
|
615 cata(cit[3], "\n")
|
|
|
616 cata("<ol>\n")
|
|
|
617 ListItem(cit[4])
|
|
|
618 ListItem(cit[5])
|
|
|
619 ListItem(cit[6])
|
|
|
620 ListItem(cit[7])
|
|
|
621 cata("</ol>\n")
|
|
|
622
|
|
1
|
623 cata("<p>Please report problems or suggestions to: su.s@wehi.edu.au</p>\n")
|
|
0
|
624
|
|
|
625 for (i in 1:nrow(linkData)) {
|
|
|
626 if (grepl("session_info", linkData$Link[i])) {
|
|
|
627 HtmlLink(linkData$Link[i], linkData$Label[i])
|
|
|
628 }
|
|
|
629 }
|
|
|
630
|
|
|
631 cata("<table border=\"0\">\n")
|
|
|
632 cata("<tr>\n")
|
|
|
633 TableItem("Task started at:"); TableItem(timeStart)
|
|
|
634 cata("</tr>\n")
|
|
|
635 cata("<tr>\n")
|
|
|
636 TableItem("Task ended at:"); TableItem(timeEnd)
|
|
|
637 cata("</tr>\n")
|
|
|
638 cata("<tr>\n")
|
|
|
639 TableItem("Task run time:"); TableItem(timeTaken)
|
|
|
640 cata("<tr>\n")
|
|
|
641 cata("</table>\n")
|
|
|
642
|
|
|
643 cata("</body>\n")
|
|
1
|
644 cata("</html>")
|
