Mercurial > repos > proteore > proteore_clusterprofiler

annotate GO-enrich.R @ 1:09ba28df72ad draft

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author proteore
date Thu, 01 Mar 2018 10:41:32 -0500
parents bd052861852b
children 710414ebb6db
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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 the first line of the files as data (without headers):
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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)
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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)
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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 headers of step two 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)
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21 file <- file[!apply(is.na(file) | file == "", 1, all), , drop=FALSE]
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22 }
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23 return(file)
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24 }
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25
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26 repartition.GO <- function(geneid, orgdb, ontology, level=3, readable=TRUE) {
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27 ggo<-groupGO(gene=geneid,
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28 OrgDb = orgdb,
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29 ont=ontology,
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30 level=level,
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31 readable=TRUE)
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32 name <- paste("GGO.", ontology, ".png", sep = "")
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33 png(name)
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34 p <- barplot(ggo)
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35 print(p)
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36 dev.off()
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37 return(ggo)
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38 }
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39
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40 # GO over-representation test
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41 enrich.GO <- function(geneid, orgdb, ontology, pval_cutoff, qval_cutoff) {
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42 ego<-enrichGO(gene=geneid,
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43 OrgDb=orgdb,
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44 keytype="ENTREZID",
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45 ont=ontology,
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46 pAdjustMethod="BH",
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47 pvalueCutoff=pval_cutoff,
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48 qvalueCutoff=qval_cutoff,
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49 readable=TRUE)
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50 bar_name <- paste("EGO.", ontology, ".bar.png", sep = "")
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51 png(bar_name)
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52 p <- barplot(ego)
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53 print(p)
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54 dev.off()
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55 dot_name <- paste("EGO.", ontology, ".dot.png", sep = "")
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56 png(dot_name)
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57 p <- dotplot(ego)
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58 print(p)
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59 dev.off()
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60 return(ego)
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61 }
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62
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63 clusterProfiler = function() {
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64 args <- commandArgs(TRUE)
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65 if(length(args)<1) {
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66 args <- c("--help")
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67 }
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68
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69 # Help section
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70 if("--help" %in% args) {
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71 cat("clusterProfiler Enrichment Analysis
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72 Arguments:
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73 --input_type: type of input (list of id or filename)
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74 --input: input
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75 --ncol: the column number which you would like to apply...
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76 --header: true/false if your file contains a header
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77 --id_type: the type of input IDs (UniProt/EntrezID)
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78 --species
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79 --onto_opt: ontology options
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80 --go_function: groupGO/enrichGO
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81 --level: 1-3
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82 --pval_cutoff
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83 --qval_cutoff
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84 --text_output: text output filename \n")
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85 q(save="no")
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86 }
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87 # Parse arguments
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88 parseArgs <- function(x) strsplit(sub("^--", "", x), "=")
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89 argsDF <- as.data.frame(do.call("rbind", parseArgs(args)))
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90 args <- as.list(as.character(argsDF$V2))
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91 names(args) <- argsDF$V1
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92
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93 input_type = args$input_type
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94 if (input_type == "text") {
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95 input = args$input
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96 }
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97 else if (input_type == "file") {
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98 filename = args$input
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99 ncol = args$ncol
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100 # Check ncol
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101 if (! as.numeric(gsub("c", "", ncol)) %% 1 == 0) {
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102 stop("Please enter an integer for level")
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103 }
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104 else {
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105 ncol = as.numeric(gsub("c", "", ncol))
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106 }
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107 header = args$header
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108 # Get file content
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109 file = readfile(filename, header)
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110 # Extract Protein IDs list
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111 input = c()
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112 for (row in as.character(file[,ncol])) {
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113 input = c(input, strsplit(row, ";")[[1]][1])
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114 }
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115 }
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116 id_type = args$id_type
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117
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118
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119 #ID format Conversion
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120 #This case : from UNIPROT (protein id) to ENTREZ (gene id)
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121 #bitr = conversion function from clusterProfiler
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122
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123 if (args$species=="human") {
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124 orgdb<-org.Hs.eg.db
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125 }
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126 else if (args$species=="mouse") {
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127 orgdb<-org.Mm.eg.db
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128 }
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129 else if (args$species=="rat") {
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130 orgdb<-org.Rn.eg.db
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131 }
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132
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133 ##to initialize
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134 if (id_type=="Uniprot") {
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135 idFrom<-"UNIPROT"
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136 idTo<-"ENTREZID"
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137 gene<-bitr(input, fromType=idFrom, toType=idTo, OrgDb=orgdb)
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138 }
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139 else if (id_type=="Entrez") {
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140 gene<-input
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141 }
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142
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143 ontology <- strsplit(args$onto_opt, ",")[[1]]
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144 if (args$go_represent == "true") {
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145 go_represent <- args$go_represent
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146 level <- as.numeric(args$level)
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147 }
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148 if (args$go_enrich == "true") {
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149 go_enrich <- args$go_enrich
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150 pval_cutoff <- as.numeric(args$pval_cutoff)
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151 qval_cutoff <- as.numeric(args$qval_cutoff)
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152 }
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153
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154 ##enrichGO : GO over-representation test
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155 for (onto in ontology) {
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156 if (args$go_represent == "true") {
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157 ggo<-repartition.GO(gene$ENTREZID, orgdb, onto, level, readable=TRUE)
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158 write.table(ggo, args$text_output, append = TRUE, sep="\t", row.names = FALSE, quote=FALSE)
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159 }
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160 if (args$go_enrich == "true") {
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161 ego<-enrich.GO(gene$ENTREZID, orgdb, onto, pval_cutoff, qval_cutoff)
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162 write.table(ego, args$text_output, append = TRUE, sep="\t", row.names = FALSE, quote=FALSE)
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163 }
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164 }
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165 }
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166
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167 clusterProfiler()