Mercurial > repos > proteore > proteore_clusterprofiler

annotate GO-enrich.R @ 27:54f8e6f5c59d draft

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planemo upload commit d884b75cfc292105807c42c58f864d536dc9e431-dirty
author proteore
date Wed, 21 Nov 2018 08:53:49 -0500
parents c8bff18c102b
children eafb37258a6f
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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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4 # Read file and return file content as data.frame
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5 read_file <- function(path,header){
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6 file <- try(read.csv(path,header=header, sep="\t",stringsAsFactors = FALSE, quote="",check.names = F),silent=TRUE)
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7 if (inherits(file,"try-error")){
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8 stop("File not found !")
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9 }else{
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10 file <- file[!apply(is.na(file) | file == "", 1, all), , drop=FALSE]
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11 return(file)
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12 }
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13 }
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15 #return the number of character from the longest description found (from the 10 first)
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16 max_str_length_10_first <- function(vector){
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17 vector <- as.vector(vector)
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18 nb_description = length(vector)
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19 if (nb_description >= 10){nb_description=10}
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20 return(max(nchar(vector[1:nb_description])))
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21 }
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22
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23 str2bool <- function(x){
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24 if (any(is.element(c("t","true"),tolower(x)))){
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25 return (TRUE)
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26 }else if (any(is.element(c("f","false"),tolower(x)))){
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27 return (FALSE)
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28 }else{
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29 return(NULL)
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30 }
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31 }
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32
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33 #used before the limit was set to 50 characters
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34 width_by_max_char <- function (nb_max_char) {
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35 if (nb_max_char < 50 ){
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36 width=600
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37 } else if (nb_max_char < 75) {
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38 width=800
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39 } else if (nb_max_char < 100) {
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40 width=900
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41 } else {
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42 width=1000
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43 }
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44 return (width)
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45 }
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46
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47 repartition.GO <- function(geneid, orgdb, ontology, level=3, readable=TRUE) {
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48 ggo<-groupGO(gene=geneid,
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49 OrgDb = orgdb,
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50 ont=ontology,
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51 level=level,
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52 readable=TRUE)
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53
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54 if (length(ggo@result$ID) > 0 ) {
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55 ggo@result$Description <- sapply(as.vector(ggo@result$Description), function(x) {ifelse(nchar(x)>50, substr(x,1,50),x)},USE.NAMES = FALSE)
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56 #nb_max_char = max_str_length_10_first(ggo$Description)
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57 #width = width_by_max_char(nb_max_char)
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58 name <- paste("GGO_", ontology, "_bar-plot", sep = "")
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59 png(name,height = 720, width = 600)
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60 p <- barplot(ggo, showCategory=10)
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61 print(p)
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62 dev.off()
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63 ggo <- as.data.frame(ggo)
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64 return(ggo)
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65 }
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66 }
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67
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68 # GO over-representation test
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69 enrich.GO <- function(geneid, universe, orgdb, ontology, pval_cutoff, qval_cutoff,plot) {
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70 ego<-enrichGO(gene=geneid,
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71 universe=universe,
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72 OrgDb=orgdb,
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73 ont=ontology,
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74 pAdjustMethod="BH",
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75 pvalueCutoff=pval_cutoff,
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76 qvalueCutoff=qval_cutoff,
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77 readable=TRUE)
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78
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79 # Plot bar & dot plots
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80 #if there are enriched GopTerms
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81 if (length(ego$ID)>0){
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82
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83 ego@result$Description <- sapply(ego@result$Description, function(x) {ifelse(nchar(x)>50, substr(x,1,50),x)},USE.NAMES = FALSE)
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84 #nb_max_char = max_str_length_10_first(ego$Description)
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85 #width = width_by_max_char(nb_max_char)
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86
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87 if ("dotplot" %in% plot ){
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88 dot_name <- paste("EGO_", ontology, "_dot-plot", sep = "")
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89 png(dot_name,height = 720, width = 600)
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90 p <- dotplot(ego, showCategory=10)
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91 print(p)
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92 dev.off()
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93 }
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94
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95 if ("barplot" %in% plot ){
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96 bar_name <- paste("EGO_", ontology, "_bar-plot", sep = "")
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97 png(bar_name,height = 720, width = 600)
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98 p <- barplot(ego, showCategory=10)
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99 print(p)
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100 dev.off()
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101
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102 }
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103 ego <- as.data.frame(ego)
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104 return(ego)
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105 } else {
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106 warning(paste("No Go terms enriched (EGO) found for ",ontology,"ontology"),immediate. = TRUE,noBreaks. = TRUE,call. = FALSE)
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107 }
0
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108 }
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109
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110 check_ids <- function(vector,type) {
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111 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})$"
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112 entrez_id = "^([0-9]+|[A-Z]{1,2}_[0-9]+|[A-Z]{1,2}_[A-Z]{1,4}[0-9]+)$"
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113 if (type == "entrez")
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114 return(grepl(entrez_id,vector))
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115 else if (type == "uniprot") {
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116 return(grepl(uniprot_pattern,vector))
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117 }
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118 }
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119
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120 clusterProfiler = function() {
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121 args <- commandArgs(TRUE)
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122 if(length(args)<1) {
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123 args <- c("--help")
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124 }
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125
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126 # Help section
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127 if("--help" %in% args) {
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128 cat("clusterProfiler Enrichment Analysis
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129 Arguments:
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130 --input_type: type of input (list of id or filename)
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131 --input: input
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132 --ncol: the column number which contains list of input IDs
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133 --header: true/false if your file contains a header
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134 --id_type: the type of input IDs (UniProt/EntrezID)
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135 --universe_type: list or filename
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136 --universe: background IDs list
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137 --uncol: the column number which contains background IDs list
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138 --uheader: true/false if the background IDs file contains header
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139 --universe_id_type: the type of universe IDs (UniProt/EntrezID)
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140 --species
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141 --onto_opt: ontology options
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142 --go_function: groupGO/enrichGO
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143 --level: 1-3
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144 --pval_cutoff
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145 --qval_cutoff
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146 --text_output: text output filename
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147 --plot : type of visualization, dotplot or/and barplot \n")
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148 q(save="no")
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149 }
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150 # Parse arguments
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151 parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
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152 argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
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153 args <- as.list(as.character(argsDF$V2))
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154 names(args) <- argsDF$V1
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155 plot = unlist(strsplit(args$plot,","))
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156 #print(args)
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157
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158 #save(args,file="/home/dchristiany/proteore_project/ProteoRE/tools/cluster_profiler/args.Rda")
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159 #load("/home/dchristiany/proteore_project/ProteoRE/tools/cluster_profiler/args.Rda")
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160
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161 suppressMessages(library(args$species, character.only = TRUE, quietly = TRUE))
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162
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163 # Extract OrgDb
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164 if (args$species=="org.Hs.eg.db") {
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165 orgdb<-org.Hs.eg.db
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166 } else if (args$species=="org.Mm.eg.db") {
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167 orgdb<-org.Mm.eg.db
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168 } else if (args$species=="org.Rn.eg.db") {
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169 orgdb<-org.Rn.eg.db
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170 }
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171
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172 # Extract input IDs
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173 input_type = args$input_type
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174 id_type = args$id_type
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175
0
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176 if (input_type == "text") {
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177 input = strsplit(args$input, "[ \t\n]+")[[1]]
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178 } else if (input_type == "file") {
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179 filename = args$input
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180 ncol = args$ncol
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181 # Check ncol
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182 if (! as.numeric(gsub("c", "", ncol)) %% 1 == 0) {
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183 stop("Please enter the right format for column number: c[number]")
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184 } else {
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185 ncol = as.numeric(gsub("c", "", ncol))
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186 }
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187 header = str2bool(args$header) # Get file content
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188 file = read_file(filename, header) # Extract Protein IDs list
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189 input = unlist(sapply(as.character(file[,ncol]),function(x) rapply(strsplit(x,";"),c),USE.NAMES = FALSE))
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190 }
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191
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192
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193 ## Get input gene list from input IDs
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194 #ID format Conversion
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195 #This case : from UNIPROT (protein id) to ENTREZ (gene id)
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196 #bitr = conversion function from clusterProfiler
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197 if (id_type=="Uniprot" & any(check_ids(input,"uniprot"))) {
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198 any(check_ids(input,"uniprot"))
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199 idFrom<-"UNIPROT"
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200 idTo<-"ENTREZID"
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201 suppressMessages(gene<-bitr(input, fromType=idFrom, toType=idTo, OrgDb=orgdb))
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202 gene<-unique(gene$ENTREZID)
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203 } else if (id_type=="Entrez" & any(check_ids(input,"entrez"))) {
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204 gene<-unique(input)
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205 } else {
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206 stop(paste(id_type,"not found in your ids list, please check your IDs in input or the selected column of your input file"))
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207 }
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208
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209 ontology <- strsplit(args$onto_opt, ",")[[1]]
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210
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211 ## Extract GGO/EGO arguments
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212 if (args$go_represent == "true") {
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213 go_represent <- args$go_represent
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214 level <- as.numeric(args$level)
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215 }
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216
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217 if (args$go_enrich == "true") {
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218 go_enrich <- args$go_enrich
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219 pval_cutoff <- as.numeric(args$pval_cutoff)
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220 qval_cutoff <- as.numeric(args$qval_cutoff)
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221 # Extract universe background genes (same as input file)
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222 if (!is.null(args$universe_type)) {
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223 universe_type = args$universe_type
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224 if (universe_type == "text") {
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225 universe = strsplit(args$universe, "[ \t\n]+")[[1]]
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226 } else if (universe_type == "file") {
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227 universe_filename = args$universe
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228 universe_ncol = args$uncol
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229 # Check ncol
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230 if (! as.numeric(gsub("c", "", universe_ncol)) %% 1 == 0) {
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231 stop("Please enter the right format for column number: c[number]")
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232 } else {
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233 universe_ncol = as.numeric(gsub("c", "", universe_ncol))
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234 }
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235 universe_header = str2bool(args$uheader)
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236 # Get file content
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237 universe_file = read_file(universe_filename, universe_header)
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238 # Extract Protein IDs list
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239 universe <- sapply(universe_file[,universe_ncol], function(x) rapply(strsplit(x,";"),c),USE.NAMES = FALSE)
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240 }
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241 universe_id_type = args$universe_id_type
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242 ##to initialize
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243 if (universe_id_type=="Uniprot" & any(check_ids(universe,"uniprot"))) {
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244 idFrom<-"UNIPROT"
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245 idTo<-"ENTREZID"
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246 suppressMessages(universe_gene<-bitr(universe, fromType=idFrom, toType=idTo, OrgDb=orgdb))
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247 universe_gene<-unique(universe_gene$ENTREZID)
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248 } else if (universe_id_type=="Entrez" & any(check_ids(universe,"entrez"))) {
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249 universe_gene<-unique(unlist(universe))
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250 } else {
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251 if (universe_type=="text"){
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252 print(paste(universe_id_type,"not found in your background IDs list",sep=" "))
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253 } else {
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254 print(paste(universe_id_type,"not found in the column",universe_ncol,"of your background IDs file",sep=" "))
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255 }
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256 universe_gene = NULL
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257 }
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258 } else {
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259 universe_gene = NULL
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260 }
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261 } else {
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262 universe_gene = NULL
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263 }
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264
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265 ##enrichGO : GO over-representation test
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266 for (onto in ontology) {
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267 if (args$go_represent == "true") {
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268 ggo<-repartition.GO(gene, orgdb, onto, level, readable=TRUE)
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269 ggo <- as.data.frame(apply(ggo, c(1,2), function(x) gsub("^$|^ $", NA, x))) #convert "" and " " to NA
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270 output_path = paste("cluster_profiler_GGO_",onto,".tsv",sep="")
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271 write.table(ggo, output_path, sep="\t", row.names = FALSE, quote = FALSE )
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272 }
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273
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274 if (args$go_enrich == "true") {
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275 ego<-enrich.GO(gene, universe_gene, orgdb, onto, pval_cutoff, qval_cutoff,plot)
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276 ego <- as.data.frame(apply(ego, c(1,2), function(x) gsub("^$|^ $", NA, x))) #convert "" and " " to NA
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277 output_path = paste("cluster_profiler_EGO_",onto,".tsv",sep="")
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278 write.table(ego, output_path, sep="\t", row.names = FALSE, quote = FALSE )
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279 }
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280 }
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281 }
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282
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283 clusterProfiler()