Mercurial > repos > proteore > proteore_clusterprofiler

annotate GO-enrich.R @ 10:6604d9aa7cf3 draft

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planemo upload commit 688c456ca57914a63c20eba942ec5fe81e896099-dirty
author proteore
date Tue, 18 Sep 2018 11:12:47 -0400
parents 60a3897346e0
children 1d6095094e5f
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1 suppressMessages(library(clusterProfiler,quietly = TRUE))
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2
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3 # Read file and return file content as data.frame
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4 readfile = function(filename, header) {
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5 if (header == "true") {
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6 # Read only first line of the file as header:
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7 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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8 #Read the data of the files (skipping the first row)
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9 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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10 # Remove empty rows
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11 file <- file[!apply(is.na(file) | file == "", 1, all), , drop=FALSE]
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12 #And assign the header to the data
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13 names(file) <- headers
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14 }
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15 else {
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16 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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17 # Remove empty rows
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18 file <- file[!apply(is.na(file) | file == "", 1, all), , drop=FALSE]
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19 }
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20 return(file)
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21 }
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22
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23 #return the number of character from the longest description found (from the 10 first)
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24 max_str_length_10_first <- function(vector){
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25 vector <- as.vector(vector)
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26 nb_description = length(vector)
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27 if (nb_description >= 10){nb_description=10}
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28 return(max(nchar(vector[1:nb_description])))
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29 }
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30
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31 #used before the limit was set to 50 characters
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32 width_by_max_char <- function (nb_max_char) {
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33 if (nb_max_char < 50 ){
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34 width=600
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35 } else if (nb_max_char < 75) {
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36 width=800
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37 } else if (nb_max_char < 100) {
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38 width=900
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39 } else {
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40 width=1000
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41 }
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42 return (width)
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43 }
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44
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45 repartition.GO <- function(geneid, orgdb, ontology, level=3, readable=TRUE) {
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46 ggo<-groupGO(gene=geneid,
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47 OrgDb = orgdb,
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48 ont=ontology,
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49 level=level,
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50 readable=TRUE)
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51
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52 if (length(ggo@result$ID) > 0 ) {
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53 ggo@result$Description <- sapply(as.vector(ggo@result$Description), function(x) {ifelse(nchar(x)>50, substr(x,1,50),x)},USE.NAMES = FALSE)
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54 #nb_max_char = max_str_length_10_first(ggo$Description)
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55 #width = width_by_max_char(nb_max_char)
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56 name <- paste("GGO_", ontology, "_bar-plot", sep = "")
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57 png(name,height = 720, width = 600)
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58 p <- barplot(ggo, showCategory=10)
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59 print(p)
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60 dev.off()
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61 return(ggo)
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62 }
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63 }
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64
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65 # GO over-representation test
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66 enrich.GO <- function(geneid, universe, orgdb, ontology, pval_cutoff, qval_cutoff,plot) {
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67 ego<-enrichGO(gene=geneid,
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68 universe=universe,
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69 OrgDb=orgdb,
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70 ont=ontology,
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71 keytype="ENTREZID",
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72 pAdjustMethod="BH",
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73 pvalueCutoff=pval_cutoff,
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74 qvalueCutoff=qval_cutoff,
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75 readable=TRUE)
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76
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77 # Plot bar & dot plots
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78 #if there are enriched GopTerms
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79 if (length(ego$ID)>0){
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80
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81 ego@result$Description <- sapply(ego@result$Description, function(x) {ifelse(nchar(x)>50, substr(x,1,50),x)},USE.NAMES = FALSE)
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82 #nb_max_char = max_str_length_10_first(ego$Description)
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83 #width = width_by_max_char(nb_max_char)
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84
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85 if ("dotplot" %in% plot ){
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86 dot_name <- paste("EGO_", ontology, "_dot-plot", sep = "")
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87 png(dot_name,height = 720, width = 600)
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88 p <- dotplot(ego, showCategory=10)
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89 print(p)
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90 dev.off()
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91 }
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92
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93 if ("barplot" %in% plot ){
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94 bar_name <- paste("EGO_", ontology, "_bar-plot", sep = "")
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95 png(bar_name,height = 720, width = 600)
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96 p <- barplot(ego, showCategory=10)
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97 print(p)
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98 dev.off()
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99
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100 }
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101 return(ego)
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102 } else {
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103 warning(paste("No Go terms enriched (EGO) found for ",ontology,"ontology"),immediate. = TRUE,noBreaks. = TRUE,call. = FALSE)
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104 }
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105 }
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106
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107 check_ids <- function(vector,type) {
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108 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})$"
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109 entrez_id = "^([0-9]+|[A-Z]{1,2}_[0-9]+|[A-Z]{1,2}_[A-Z]{1,4}[0-9]+)$"
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110 if (type == "entrez")
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111 return(grepl(entrez_id,vector))
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112 else if (type == "uniprot") {
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113 return(grepl(uniprot_pattern,vector))
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114 }
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115 }
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116
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117 clusterProfiler = function() {
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118 args <- commandArgs(TRUE)
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119 if(length(args)<1) {
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120 args <- c("--help")
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121 }
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122
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123 # Help section
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124 if("--help" %in% args) {
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125 cat("clusterProfiler Enrichment Analysis
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126 Arguments:
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127 --input_type: type of input (list of id or filename)
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128 --input: input
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129 --ncol: the column number which contains list of input IDs
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130 --header: true/false if your file contains a header
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131 --id_type: the type of input IDs (UniProt/EntrezID)
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132 --universe_type: list or filename
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133 --universe: background IDs list
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134 --uncol: the column number which contains background IDs list
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135 --uheader: true/false if the background IDs file contains header
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136 --universe_id_type: the type of universe IDs (UniProt/EntrezID)
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137 --species
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138 --onto_opt: ontology options
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139 --go_function: groupGO/enrichGO
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140 --level: 1-3
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141 --pval_cutoff
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142 --qval_cutoff
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143 --text_output: text output filename
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144 --plot : type of visualization, dotplot or/and barplot \n")
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145 q(save="no")
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146 }
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147 # Parse arguments
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148 parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
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149 argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
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150 args <- as.list(as.character(argsDF$V2))
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151 names(args) <- argsDF$V1
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152 plot = unlist(strsplit(args$plot,","))
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153 #print(args)
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154
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155 #save(args,file="/home/dchristiany/proteore_project/ProteoRE/tools/cluster_profiler/args.Rda")
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156 #load("/home/dchristiany/proteore_project/ProteoRE/tools/cluster_profiler/args.Rda")
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157
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158 suppressMessages(library(args$species, character.only = TRUE, quietly = TRUE))
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159
0
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160 # Extract OrgDb
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161 if (args$species=="org.Hs.eg.db") {
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162 orgdb<-org.Hs.eg.db
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163 } else if (args$species=="org.Mm.eg.db") {
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164 orgdb<-org.Mm.eg.db
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165 } else if (args$species=="org.Rn.eg.db") {
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166 orgdb<-org.Rn.eg.db
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167 }
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168
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169 # Extract input IDs
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170 input_type = args$input_type
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171 if (input_type == "text") {
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172 input = strsplit(args$input, "[ \t\n]+")[[1]]
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173 } else if (input_type == "file") {
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174 filename = args$input
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175 ncol = args$ncol
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176 # Check ncol
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177 if (! as.numeric(gsub("c", "", ncol)) %% 1 == 0) {
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178 stop("Please enter the right format for column number: c[number]")
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179 } else {
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180 ncol = as.numeric(gsub("c", "", ncol))
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181 }
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182 header = args$header
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183 # Get file content
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184 file = readfile(filename, header)
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185 # Extract Protein IDs list
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186 input = sapply(as.character(file[,ncol]),function(x) rapply(strsplit(x,";"),c),USE.NAMES = FALSE)
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187 }
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188 id_type = args$id_type
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189 ## Get input gene list from input IDs
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190 #ID format Conversion
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191 #This case : from UNIPROT (protein id) to ENTREZ (gene id)
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192 #bitr = conversion function from clusterProfiler
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193 if (id_type=="Uniprot" & any(check_ids(input,"uniprot"))) {
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194 any(check_ids(input,"uniprot"))
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195 idFrom<-"UNIPROT"
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196 idTo<-"ENTREZID"
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197 suppressMessages(gene<-bitr(input, fromType=idFrom, toType=idTo, OrgDb=orgdb))
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198 gene<-unique(gene$ENTREZID)
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199 } else if (id_type=="Entrez" & any(check_ids(input,"entrez"))) {
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200 gene<-unique(input)
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201 } else {
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202 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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203 stop()
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204 }
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205
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206 ontology <- strsplit(args$onto_opt, ",")[[1]]
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207 ## Extract GGO/EGO arguments
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208 if (args$go_represent == "true") {
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209 go_represent <- args$go_represent
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210 level <- as.numeric(args$level)
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211 }
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212 if (args$go_enrich == "true") {
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213 go_enrich <- args$go_enrich
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214 pval_cutoff <- as.numeric(args$pval_cutoff)
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215 qval_cutoff <- as.numeric(args$qval_cutoff)
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216 # Extract universe background genes (same as input file)
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217 if (!is.null(args$universe_type)) {
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218 universe_type = args$universe_type
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219 if (universe_type == "text") {
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220 universe = strsplit(args$universe, "[ \t\n]+")[[1]]
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221 } else if (universe_type == "file") {
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222 universe_filename = args$universe
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223 universe_ncol = args$uncol
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224 # Check ncol
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225 if (! as.numeric(gsub("c", "", universe_ncol)) %% 1 == 0) {
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226 stop("Please enter the right format for column number: c[number]")
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227 } else {
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228 universe_ncol = as.numeric(gsub("c", "", universe_ncol))
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229 }
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230 universe_header = args$uheader
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231 # Get file content
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232 universe_file = readfile(universe_filename, universe_header)
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233 # Extract Protein IDs list
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234 universe <- sapply(universe_file[,universe_ncol], function(x) rapply(strsplit(x,";"),c),USE.NAMES = FALSE)
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235 }
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236 universe_id_type = args$universe_id_type
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237 ##to initialize
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238 if (universe_id_type=="Uniprot" & any(check_ids(universe,"uniprot"))) {
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239 idFrom<-"UNIPROT"
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240 idTo<-"ENTREZID"
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241 suppressMessages(universe_gene<-bitr(universe, fromType=idFrom, toType=idTo, OrgDb=orgdb))
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242 universe_gene<-unique(universe_gene$ENTREZID)
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243 } else if (universe_id_type=="Entrez" & any(check_ids(universe,"entrez"))) {
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244 universe_gene<-unique(universe)
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245 } else {
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246 if (universe_type=="text"){
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247 print(paste(universe_id_type,"not found in your background IDs list",sep=" "))
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248 } else {
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249 print(paste(universe_id_type,"not found in the column",universe_ncol,"of your background IDs file",sep=" "))
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250 }
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251 universe_gene = NULL
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252 }
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253 } else {
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254 universe_gene = NULL
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255 }
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256 } else {
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257 universe_gene = NULL
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258 }
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259
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260 ##enrichGO : GO over-representation test
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261 for (onto in ontology) {
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262 if (args$go_represent == "true") {
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263 ggo<-repartition.GO(gene, orgdb, onto, level, readable=TRUE)
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264 output_path = paste("cluster_profiler_GGO_",onto,".csv",sep="")
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265 write.table(ggo, output_path, sep="\t", row.names = FALSE, quote=FALSE)
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266 }
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267
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268 if (args$go_enrich == "true") {
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269 ego<-enrich.GO(gene, universe_gene, orgdb, onto, pval_cutoff, qval_cutoff,plot)
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270 output_path = paste("cluster_profiler_EGO_",onto,".csv",sep="")
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271 write.table(ego, output_path, sep="\t", row.names = FALSE, quote=FALSE)
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272 }
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273 }
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274 }
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275
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276 clusterProfiler()