Mercurial > repos > proteore > proteore_clusterprofiler

annotate GO-enrich.R @ 6:5e16cec55146 draft

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planemo upload commit 2da0aec067fd35a8ec102ce27ec4bac8f54b1c30-dirty
author proteore
date Thu, 29 Mar 2018 11:43:28 -0400
parents 8a91f58782df
children 4609346d8108
Ignore whitespace changes - Everywhere: Within whitespace: At end of lines:
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1 library(clusterProfiler)
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2
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3 #library(org.Sc.sgd.db)
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4 library(org.Hs.eg.db)
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5 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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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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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 clusterProfiler = function() {
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67 args <- commandArgs(TRUE)
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68 if(length(args)<1) {
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69 args <- c("--help")
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70 }
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71
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72 # Help section
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73 if("--help" %in% args) {
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74 cat("clusterProfiler Enrichment Analysis
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75 Arguments:
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76 --input_type: type of input (list of id or filename)
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77 --input: input
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78 --ncol: the column number which contains list of input IDs
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79 --header: true/false if your file contains a header
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80 --id_type: the type of input IDs (UniProt/EntrezID)
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81 --universe_type: list or filename
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82 --universe: background IDs list
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83 --uncol: the column number which contains background IDs list
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84 --uheader: true/false if the background IDs file contains header
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85 --universe_id_type: the type of universe IDs (UniProt/EntrezID)
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86 --species
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87 --onto_opt: ontology options
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88 --go_function: groupGO/enrichGO
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89 --level: 1-3
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90 --pval_cutoff
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91 --qval_cutoff
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92 --text_output: text output filename \n")
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93 q(save="no")
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94 }
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95 # Parse arguments
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96 parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
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97 argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
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98 args <- as.list(as.character(argsDF$V2))
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99 names(args) <- argsDF$V1
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100 #print(args)
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101
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102 # Extract OrgDb
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103 if (args$species=="human") {
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104 orgdb<-org.Hs.eg.db
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105 }
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106 else if (args$species=="mouse") {
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107 orgdb<-org.Mm.eg.db
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108 }
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109 else if (args$species=="rat") {
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110 orgdb<-org.Rn.eg.db
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111 }
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112
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113 # Extract input IDs
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114 input_type = args$input_type
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115 if (input_type == "text") {
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116 input = strsplit(args$input, "[ \t\n]+")[[1]]
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117 }
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118 else if (input_type == "file") {
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119 filename = args$input
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120 ncol = args$ncol
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121 # Check ncol
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122 if (! as.numeric(gsub("c", "", ncol)) %% 1 == 0) {
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123 stop("Please enter the right format for column number: c[number]")
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124 }
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125 else {
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126 ncol = as.numeric(gsub("c", "", ncol))
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127 }
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128 header = args$header
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129 # Get file content
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130 file = readfile(filename, header)
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131 # Extract Protein IDs list
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132 input = c()
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133 for (row in as.character(file[,ncol])) {
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134 input = c(input, strsplit(row, ";")[[1]][1])
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135 }
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136 }
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137 id_type = args$id_type
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138 ## Get input gene list from input IDs
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139 #ID format Conversion
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140 #This case : from UNIPROT (protein id) to ENTREZ (gene id)
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141 #bitr = conversion function from clusterProfiler
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142 if (id_type=="Uniprot") {
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143 idFrom<-"UNIPROT"
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144 idTo<-"ENTREZID"
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145 gene<-bitr(input, fromType=idFrom, toType=idTo, OrgDb=orgdb)
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146 gene<-unique(gene$ENTREZID)
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147 }
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148 else if (id_type=="Entrez") {
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149 gene<-unique(input)
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150 }
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151
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152 ontology <- strsplit(args$onto_opt, ",")[[1]]
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153 ## Extract GGO/EGO arguments
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154 if (args$go_represent == "true") {
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155 go_represent <- args$go_represent
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156 level <- as.numeric(args$level)
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157 }
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158 if (args$go_enrich == "true") {
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159 go_enrich <- args$go_enrich
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160 pval_cutoff <- as.numeric(args$pval_cutoff)
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161 qval_cutoff <- as.numeric(args$qval_cutoff)
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162 # Extract universe background genes (same as input file)
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163 if (!is.null(args$universe_type)) {
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164 universe_type = args$universe_type
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165 if (universe_type == "text") {
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166 universe = strsplit(args$universe, "[ \t\n]+")[[1]]
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167 }
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168 else if (universe_type == "file") {
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169 universe_filename = args$universe
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170 universe_ncol = args$uncol
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171 # Check ncol
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172 if (! as.numeric(gsub("c", "", universe_ncol)) %% 1 == 0) {
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173 stop("Please enter the right format for column number: c[number]")
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174 }
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175 else {
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176 universe_ncol = as.numeric(gsub("c", "", universe_ncol))
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177 }
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178 universe_header = args$uheader
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179 # Get file content
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180 universe_file = readfile(universe_filename, universe_header)
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181 # Extract Protein IDs list
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182 universe = c()
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183 for (row in as.character(universe_file[,universe_ncol])) {
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184 universe = c(universe, strsplit(row, ";")[[1]][1])
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185 }
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186 }
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187 universe_id_type = args$universe_id_type
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188 ##to initialize
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189 if (universe_id_type=="Uniprot") {
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190 idFrom<-"UNIPROT"
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191 idTo<-"ENTREZID"
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192 universe_gene<-bitr(universe, fromType=idFrom, toType=idTo, OrgDb=orgdb)
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193 universe_gene<-unique(universe_gene$ENTREZID)
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194 }
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195 else if (universe_id_type=="Entrez") {
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196 universe_gene<-unique(universe)
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197 }
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198 }
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199 else {
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200 universe_gene = NULL
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201 }
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202 }
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203
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204 ##enrichGO : GO over-representation test
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205 for (onto in ontology) {
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206 if (args$go_represent == "true") {
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207 ggo<-repartition.GO(gene, orgdb, onto, level, readable=TRUE)
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208 write.table(ggo, args$text_output, append = TRUE, sep="\t", row.names = FALSE, quote=FALSE)
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209 }
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210 if (args$go_enrich == "true") {
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211 ego<-enrich.GO(gene, universe_gene, orgdb, onto, pval_cutoff, qval_cutoff)
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212 write.table(ego, args$text_output, append = TRUE, sep="\t", row.names = FALSE, quote=FALSE)
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213 }
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214 }
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215 }
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216
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217 clusterProfiler()