Mercurial > repos > proteore > proteore_go_terms_profiles_comparison

annotate GO_prof_comp.R @ 3:e337dcfb84e4 draft

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planemo upload commit 51fc514a85c1055cab5bb6e76c90f3da7e648101-dirty
author proteore
date Thu, 07 Mar 2019 09:20:58 -0500
parents 67a4f68f1c1c
children
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1 options(warn=-1) #TURN OFF WARNINGS !!!!!!
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2 suppressMessages(library(clusterProfiler,quietly = TRUE))
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3 suppressMessages(library(plyr, quietly = TRUE))
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4 suppressMessages(library(ggplot2, quietly = TRUE))
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5 suppressMessages(library(DOSE, quietly = TRUE))
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7 #return the number of character from the longest description found (from the 10 first)
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8 max_str_length_10_first <- function(vector){
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9 vector <- as.vector(vector)
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10 nb_description = length(vector)
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11 if (nb_description >= 10){nb_description=10}
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12 return(max(nchar(vector[1:nb_description])))
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13 }
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14
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15 str2bool <- function(x){
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16 if (any(is.element(c("t","true"),tolower(x)))){
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17 return (TRUE)
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18 }else if (any(is.element(c("f","false"),tolower(x)))){
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19 return (FALSE)
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20 }else{
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21 return(NULL)
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22 }
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23 }
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25 get_args <- function(){
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26
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27 ## Collect arguments
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28 args <- commandArgs(TRUE)
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29
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30 ## Default setting when no arguments passed
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31 if(length(args) < 1) {
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32 args <- c("--help")
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33 }
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34
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35 ## Help section
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36 if("--help" %in% args) {
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37 cat("Selection and Annotation HPA
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38 Arguments:
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39 --inputtype1: type of input (list of id or filename)
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40 --inputtype2: type of input (list of id or filename)
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41 --input1: input1
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42 --input2: input2
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43 --column1: the column number which you would like to apply...
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44 --column2: the column number which you would like to apply...
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45 --header1: true/false if your file contains a header
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46 --header2: true/false if your file contains a header
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47 --ont: ontology to use
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48 --lev: ontology level
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49 --org: organism db package
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50 --list_name1: name of the first list
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51 --list_name2: name of the second list \n")
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52
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53 q(save="no")
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54 }
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55
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56 parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
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57 argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
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58 args <- as.list(as.character(argsDF$V2))
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59 names(args) <- argsDF$V1
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60
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61 return(args)
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62 }
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63
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64 get_ids=function(inputtype, input, ncol, header) {
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65
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66 if (inputtype == "text") {
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67 ids = strsplit(input, "[ \t\n]+")[[1]]
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68 } else if (inputtype == "file") {
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69 header=str2bool(header)
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70 ncol=get_cols(ncol)
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71 csv = read.csv(input,header=header, sep="\t", as.is=T)
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72 ids=csv[,ncol]
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73 }
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74
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75 ids = unlist(strsplit(as.character(ids),";"))
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76 ids = ids[which(!is.na(ids))]
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77
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78 return(ids)
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79 }
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80
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81 str2bool <- function(x){
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82 if (any(is.element(c("t","true"),tolower(x)))){
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83 return (TRUE)
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84 }else if (any(is.element(c("f","false"),tolower(x)))){
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85 return (FALSE)
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86 }else{
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87 return(NULL)
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88 }
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89 }
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90
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91 check_ids <- function(vector,type) {
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92 uniprot_pattern = "^([OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2})$"
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93 entrez_id = "^([0-9]+|[A-Z]{1,2}_[0-9]+|[A-Z]{1,2}_[A-Z]{1,4}[0-9]+)$"
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94 if (type == "entrez")
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95 return(grepl(entrez_id,vector))
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96 else if (type == "uniprot") {
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97 return(grepl(uniprot_pattern,vector))
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98 }
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99 }
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100
3
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101 #res.cmp@compareClusterResult$Description <- sapply(as.vector(res.cmp@compareClusterResult$Description), function(x) {ifelse(nchar(x)>50, substr(x,1,50),x)},USE.NAMES = FALSE)
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102 fortify.compareClusterResult <- function(res.cmp, showCategory=30, by="geneRatio", split=NULL, includeAll=TRUE) {
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103 clProf.df <- as.data.frame(res.cmp)
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104 .split <- split
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105 ## get top 5 (default) categories of each gene cluster.
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106 if (is.null(showCategory)) {
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107 result <- clProf.df
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108 } else {
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109 Cluster <- NULL # to satisfy codetools
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110 topN <- function(res, showCategory) {
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111 ddply(.data = res, .variables = .(Cluster), .fun = function(df, N) {
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112 if (length(df$Count) > N) {
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113 if (any(colnames(df) == "pvalue")) {
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114 idx <- order(df$pvalue, decreasing=FALSE)[1:N]
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115 } else {
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116 ## for groupGO
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117 idx <- order(df$Count, decreasing=T)[1:N]
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118 }
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119 return(df[idx,])
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120 } else {
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121 return(df)
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122 }
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123 },
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124 N=showCategory
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125 )
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126 }
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127 if (!is.null(.split) && .split %in% colnames(clProf.df)) {
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128 lres <- split(clProf.df, as.character(clProf.df[, .split]))
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129 lres <- lapply(lres, topN, showCategory = showCategory)
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130 result <- do.call('rbind', lres)
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131 } else {
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132 result <- topN(clProf.df, showCategory)
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133 }
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134 }
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135 ID <- NULL
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136 if (includeAll == TRUE) {
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137 result = subset(clProf.df, ID %in% result$ID)
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138 }
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139 ## remove zero count
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140 result$Description <- as.character(result$Description) ## un-factor
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141 GOlevel <- result[,c("ID", "Description")] ## GO ID and Term
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142 GOlevel <- unique(GOlevel)
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143 result <- result[result$Count != 0, ]
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144 result$Description <- factor(result$Description,levels=rev(GOlevel[,2]))
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145 if (by=="rowPercentage") {
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146 Description <- Count <- NULL # to satisfy codetools
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147 result <- ddply(result,.(Description),transform,Percentage = Count/sum(Count),Total = sum(Count))
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148 ## label GO Description with gene counts.
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149 x <- mdply(result[, c("Description", "Total")], paste, sep=" (")
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150 y <- sapply(x[,3], paste, ")", sep="")
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151 result$Description <- y
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152
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153 ## restore the original order of GO Description
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154 xx <- result[,c(2,3)]
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155 xx <- unique(xx)
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156 rownames(xx) <- xx[,1]
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157 Termlevel <- xx[as.character(GOlevel[,1]),2]
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158
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159 ##drop the *Total* column
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160 result <- result[, colnames(result) != "Total"]
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161 result$Description <- factor(result$Description, levels=rev(Termlevel))
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162
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163 } else if (by == "count") {
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164 ## nothing
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165 } else if (by == "geneRatio") { ##default
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166 gsize <- as.numeric(sub("/\\d+$", "", as.character(result$GeneRatio)))
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167 gcsize <- as.numeric(sub("^\\d+/", "", as.character(result$GeneRatio)))
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168 result$GeneRatio = gsize/gcsize
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169 cluster <- paste(as.character(result$Cluster),"\n", "(", gcsize, ")", sep="")
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170 lv <- unique(cluster)[order(as.numeric(unique(result$Cluster)))]
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171 result$Cluster <- factor(cluster, levels = lv)
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172 } else {
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173 ## nothing
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174 }
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175 return(result)
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176 }
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177
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178 ##function plotting.clusteProfile from clusterProfiler pkg
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179 plotting.clusterProfile <- function(clProf.reshape.df,x = ~Cluster,type = "dot", colorBy = "p.adjust",by = "geneRatio",title="",font.size=12) {
0
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180
3
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181 Description <- Percentage <- Count <- Cluster <- GeneRatio <- p.adjust <- pvalue <- NULL # to
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182 if (type == "dot") {
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183 if (by == "rowPercentage") {
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184 p <- ggplot(clProf.reshape.df,
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185 aes_(x = x, y = ~Description, size = ~Percentage))
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186 } else if (by == "count") {
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187 p <- ggplot(clProf.reshape.df,
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188 aes_(x = x, y = ~Description, size = ~Count))
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189 } else if (by == "geneRatio") { ##DEFAULT
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190 p <- ggplot(clProf.reshape.df,
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191 aes_(x = x, y = ~Description, size = ~GeneRatio))
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192 } else {
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193 ## nothing here
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194 }
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195 if (any(colnames(clProf.reshape.df) == colorBy)) {
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196 p <- p +
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197 geom_point() +
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198 aes_string(color=colorBy) +
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199 scale_color_continuous(low="red", high="blue", guide=guide_colorbar(reverse=TRUE))
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200 ## scale_color_gradientn(guide=guide_colorbar(reverse=TRUE), colors = enrichplot:::sig_palette)
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201 } else {
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202 p <- p + geom_point(colour="steelblue")
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203 }
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204 }
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205
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206 p <- p + xlab("") + ylab("") + ggtitle(title) +
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207 theme_dose(font.size)
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208
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209 ## theme(axis.text.x = element_text(colour="black", size=font.size, vjust = 1)) +
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210 ## theme(axis.text.y = element_text(colour="black",
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211 ## size=font.size, hjust = 1)) +
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212 ## ggtitle(title)+theme_bw()
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213 ## p <- p + theme(axis.text.x = element_text(angle=angle.axis.x,
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214 ## hjust=hjust.axis.x,
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215 ## vjust=vjust.axis.x))
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216
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217 return(p)
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218 }
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219
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220 make_dotplot<-function(res.cmp,ontology) {
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221
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222 dfok<-fortify.compareClusterResult(res.cmp)
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223 dfok$Description <- sapply(as.vector(dfok$Description), function(x) {ifelse(nchar(x)>50, substr(x,1,50),x)},USE.NAMES = FALSE)
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224 p<-plotting.clusterProfile(dfok, title="")
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225
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226 #plot(p, type="dot") #
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227 output_path= paste("GO_profiles_comp_",ontology,".png",sep="")
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228 png(output_path,height = 720, width = 600)
3
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229 pl <- plot(p, type="dot")
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230 print(pl)
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231 dev.off()
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232 }
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233
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234 get_cols <-function(input_cols) {
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235 input_cols <- gsub("c","",gsub("C","",gsub(" ","",input_cols)))
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236 if (grepl(":",input_cols)) {
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237 first_col=unlist(strsplit(input_cols,":"))[1]
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238 last_col=unlist(strsplit(input_cols,":"))[2]
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239 cols=first_col:last_col
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240 } else {
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241 cols = as.integer(unlist(strsplit(input_cols,",")))
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242 }
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243 return(cols)
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244 }
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245
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246 #to check
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247 cmp.GO <- function(l,fun="groupGO",orgdb, ontology, level=3, readable=TRUE) {
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248 cmpGO<-compareCluster(geneClusters = l,
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249 fun=fun,
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250 OrgDb = orgdb,
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251 ont=ontology,
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252 level=level,
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253 readable=TRUE)
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254
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255 return(cmpGO)
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256 }
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257
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258 check_ids <- function(vector,type) {
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259 uniprot_pattern = "^([OPQ][0-9][A-Z0-9]{3}[0-9]|[A-NR-Z][0-9]([A-Z][A-Z0-9]{2}[0-9]){1,2})$"
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260 entrez_id = "^([0-9]+|[A-Z]{1,2}_[0-9]+|[A-Z]{1,2}_[A-Z]{1,4}[0-9]+)$"
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261 if (type == "entrez")
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262 return(grepl(entrez_id,vector))
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263 else if (type == "uniprot") {
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264 return(grepl(uniprot_pattern,vector))
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265 }
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266 }
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267
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268 main = function() {
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269
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270 #to get the args of the command line
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271 args=get_args()
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272
3
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273
0
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274 ids1<-get_ids(args$inputtype1, args$input1, args$column1, args$header1)
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275 ids2<-get_ids(args$inputtype2, args$input2, args$column2, args$header2)
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276 ont = strsplit(args$ont, ",")[[1]]
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277 lev=as.integer(args$lev)
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278 org=args$org
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279
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280 #load annot package
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281 suppressMessages(library(args$org, character.only = TRUE, quietly = TRUE))
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282
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283 # Extract OrgDb
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284 if (args$org=="org.Hs.eg.db") {
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285 orgdb<-org.Hs.eg.db
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286 } else if (args$org=="org.Mm.eg.db") {
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287 orgdb<-org.Mm.eg.db
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288 } else if (args$org=="org.Rn.eg.db") {
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289 orgdb<-org.Rn.eg.db
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290 }
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291
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292 for(ontology in ont) {
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293 liste = list("l1"=ids1,"l2"=ids2)
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294 names(liste) = c(args$list_name1,args$list_name2)
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295 res.cmp<-cmp.GO(l=liste,fun="groupGO",orgdb, ontology, level=lev, readable=TRUE)
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296 make_dotplot(res.cmp,ontology)
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297 output_path = paste("GO_profiles_comp_",ontology,".tsv",sep="")
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298 write.table(res.cmp@compareClusterResult, output_path, sep="\t", row.names=F, quote=F)
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299 }
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300
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301 } #end main
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302
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303 main()
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304