Mercurial > repos > proteore > proteore_clusterprofiler

annotate GO-enrich.R @ 0:076349b72690 draft

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planemo upload commit 2e441b4969ae7cf9aeb227a1d47c43ef7268a5e6-dirty
author proteore
date Wed, 22 Aug 2018 10:44:46 -0400
parents
children 91b9b48d07b3
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0
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1 suppressMessages(library(clusterProfiler))
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2
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3 #library(org.Sc.sgd.db)
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4 suppressMessages(library(org.Hs.eg.db))
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5 suppressMessages(library(org.Mm.eg.db))
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6
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7 # Read file and return file content as data.frame
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8 readfile = function(filename, header) {
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9 if (header == "true") {
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10 # Read only first line of the file as header:
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11 headers <- read.table(filename, nrows = 1, header = FALSE, sep = "\t", stringsAsFactors = FALSE, fill = TRUE, na.strings=c("", "NA"), blank.lines.skip = TRUE, quote = "")
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12 #Read the data of the files (skipping the first row)
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13 file <- read.table(filename, skip = 1, header = FALSE, sep = "\t", stringsAsFactors = FALSE, fill = TRUE, na.strings=c("", "NA"), blank.lines.skip = TRUE, quote = "")
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14 # Remove empty rows
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15 file <- file[!apply(is.na(file) | file == "", 1, all), , drop=FALSE]
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16 #And assign the header to the data
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17 names(file) <- headers
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18 }
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19 else {
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20 file <- read.table(filename, header = FALSE, sep = "\t", stringsAsFactors = FALSE, fill = TRUE, na.strings=c("", "NA"), blank.lines.skip = TRUE, quote = "")
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21 # Remove empty rows
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22 file <- file[!apply(is.na(file) | file == "", 1, all), , drop=FALSE]
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23 }
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24 return(file)
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25 }
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26
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27 repartition.GO <- function(geneid, orgdb, ontology, level=3, readable=TRUE) {
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28 ggo<-groupGO(gene=geneid,
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29 OrgDb = orgdb,
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30 ont=ontology,
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31 level=level,
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32 readable=TRUE)
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33 name <- paste("GGO.", ontology, ".png", sep = "")
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34 png(name)
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35 p <- barplot(ggo, showCategory=10)
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36 print(p)
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37 dev.off()
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38 return(ggo)
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39 }
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40
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41 # GO over-representation test
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42 enrich.GO <- function(geneid, universe, orgdb, ontology, pval_cutoff, qval_cutoff) {
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43 ego<-enrichGO(gene=geneid,
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44 universe=universe,
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45 OrgDb=orgdb,
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46 keytype="ENTREZID",
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47 ont=ontology,
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48 pAdjustMethod="BH",
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49 pvalueCutoff=pval_cutoff,
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50 qvalueCutoff=qval_cutoff,
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51 readable=TRUE)
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52 # Plot bar & dot plots
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53 bar_name <- paste("EGO.", ontology, ".bar.png", sep = "")
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54 png(bar_name)
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55 p <- barplot(ego)
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56 print(p)
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57 dev.off()
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58 dot_name <- paste("EGO.", ontology, ".dot.png", sep = "")
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59 png(dot_name)
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60 p <- dotplot(ego, showCategory=10)
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61 print(p)
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62 dev.off()
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63 return(ego)
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64 }
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65
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66 check_ids <- function(vector,type) {
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67 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})$"
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68 entrez_id = "^'[0-9]+|[A-Z]{1,2}_[0-9]+|[A-Z]{1,2}_[A-Z]{1,4}[0-9]+)$"
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69 if (type == "entrez")
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70 return(grepl(entrez_id,vector))
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71 else if (type == "uniprot") {
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72 return(grepl(uniprot_pattern,vector))
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73 }
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74 }
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75
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76 clusterProfiler = function() {
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77 args <- commandArgs(TRUE)
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78 if(length(args)<1) {
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79 args <- c("--help")
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80 }
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81
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82 # Help section
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83 if("--help" %in% args) {
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84 cat("clusterProfiler Enrichment Analysis
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85 Arguments:
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86 --input_type: type of input (list of id or filename)
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87 --input: input
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88 --ncol: the column number which contains list of input IDs
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89 --header: true/false if your file contains a header
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90 --id_type: the type of input IDs (UniProt/EntrezID)
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91 --universe_type: list or filename
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92 --universe: background IDs list
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93 --uncol: the column number which contains background IDs list
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94 --uheader: true/false if the background IDs file contains header
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95 --universe_id_type: the type of universe IDs (UniProt/EntrezID)
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96 --species
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97 --onto_opt: ontology options
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98 --go_function: groupGO/enrichGO
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99 --level: 1-3
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100 --pval_cutoff
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101 --qval_cutoff
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102 --text_output: text output filename \n")
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103 q(save="no")
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104 }
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105 # Parse arguments
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106 parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
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107 argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
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108 args <- as.list(as.character(argsDF$V2))
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109 names(args) <- argsDF$V1
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110 #print(args)
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111
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112 #save(args,file="args.Rda")
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113 load("/home/dchristiany/proteore_project/ProteoRE/tools/cluster_profiler/args.Rda")
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114
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115 # Extract OrgDb
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116 if (args$species=="human") {
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117 orgdb<-org.Hs.eg.db
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118 } else if (args$species=="mouse") {
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119 orgdb<-org.Mm.eg.db
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120 } else if (args$species=="rat") {
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121 orgdb<-org.Rn.eg.db
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122 }
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123
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124 # Extract input IDs
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125 input_type = args$input_type
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126 if (input_type == "text") {
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127 input = strsplit(args$input, "[ \t\n]+")[[1]]
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128 } else if (input_type == "file") {
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129 filename = args$input
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130 ncol = args$ncol
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131 # Check ncol
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132 if (! as.numeric(gsub("c", "", ncol)) %% 1 == 0) {
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133 stop("Please enter the right format for column number: c[number]")
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134 } else {
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135 ncol = as.numeric(gsub("c", "", ncol))
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136 }
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137 header = args$header
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138 # Get file content
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139 file = readfile(filename, header)
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140 # Extract Protein IDs list
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141 input = sapply(as.character(file[,ncol]),function(x) rapply(strsplit(x,";"),c),USE.NAMES = FALSE)
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142 }
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143 id_type = args$id_type
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144 ## Get input gene list from input IDs
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145 #ID format Conversion
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146 #This case : from UNIPROT (protein id) to ENTREZ (gene id)
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147 #bitr = conversion function from clusterProfiler
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148 if (id_type=="Uniprot" & any(check_ids(input,"uniprot"))) {
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149 any(check_ids(input,"uniprot"))
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150 idFrom<-"UNIPROT"
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151 idTo<-"ENTREZID"
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152 gene<-bitr(input, fromType=idFrom, toType=idTo, OrgDb=orgdb)
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153 gene<-unique(gene$ENTREZID)
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154 } else if (id_type=="Entrez" & any(check_ids(input,"entrez"))) {
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155 gene<-unique(input)
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156 } else {
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157 print(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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158 stop()
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159 }
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160
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161 ontology <- strsplit(args$onto_opt, ",")[[1]]
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162 ## Extract GGO/EGO arguments
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163 if (args$go_represent == "true") {
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164 go_represent <- args$go_represent
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165 level <- as.numeric(args$level)
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166 }
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167 if (args$go_enrich == "true") {
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168 go_enrich <- args$go_enrich
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169 pval_cutoff <- as.numeric(args$pval_cutoff)
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170 qval_cutoff <- as.numeric(args$qval_cutoff)
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171 # Extract universe background genes (same as input file)
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172 if (!is.null(args$universe_type)) {
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173 universe_type = args$universe_type
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174 if (universe_type == "text") {
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175 universe = strsplit(args$universe, "[ \t\n]+")[[1]]
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176 } else if (universe_type == "file") {
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177 universe_filename = args$universe
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178 universe_ncol = args$uncol
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179 # Check ncol
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180 if (! as.numeric(gsub("c", "", universe_ncol)) %% 1 == 0) {
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181 stop("Please enter the right format for column number: c[number]")
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182 } else {
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183 universe_ncol = as.numeric(gsub("c", "", universe_ncol))
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184 }
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185 universe_header = args$uheader
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186 # Get file content
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187 universe_file = readfile(universe_filename, universe_header)
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188 # Extract Protein IDs list
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189 universe <- sapply(universe_file[,universe_ncol], function(x) rapply(strsplit(x,";"),c),USE.NAMES = FALSE)
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190 }
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191 universe_id_type = args$universe_id_type
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192 ##to initialize
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193 if (universe_id_type=="Uniprot" & any(check_ids(universe,"uniprot"))) {
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194 idFrom<-"UNIPROT"
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195 idTo<-"ENTREZID"
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196 universe_gene<-bitr(universe, fromType=idFrom, toType=idTo, OrgDb=orgdb)
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197 universe_gene<-unique(universe_gene$ENTREZID)
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198 } else if (universe_id_type=="Entrez" & any(check_ids(universe,"entrez"))) {
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199 universe_gene<-unique(universe)
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200 } else {
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201 if (universe_type=="text"){
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202 print(paste(universe_id_type,"not found in your background IDs list",sep=" "))
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203 } else {
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204 print(paste(universe_id_type,"not found in the column",universe_ncol,"of your background IDs file",sep=" "))
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205 }
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206 universe_gene = NULL
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207 }
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208 } else {
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209 universe_gene = NULL
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210 }
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211 }
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212
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213 ##enrichGO : GO over-representation test
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214 for (onto in ontology) {
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215 if (args$go_represent == "true") {
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216 ggo<-repartition.GO(gene, orgdb, onto, level, readable=TRUE)
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217 write.table(ggo, args$text_output, append = TRUE, sep="\t", row.names = FALSE, quote=FALSE)
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218 }
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219 if (args$go_enrich == "true" & !is.null(universe_gene)) {
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220 ego<-enrich.GO(gene, universe_gene, orgdb, onto, pval_cutoff, qval_cutoff)
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221 write.table(ego, args$text_output, append = TRUE, sep="\t", row.names = FALSE, quote=FALSE)
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222 }
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223 }
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224 }
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225
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226 clusterProfiler()