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annotate enrichment_v3.R @ 9:70c0c8757f5f draft
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author | proteore |
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date | Fri, 21 Sep 2018 05:32:38 -0400 |
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1 # enrichment_v3.R |
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2 # Usage : Rscript --vanilla enrichment_v3.R --inputtype tabfile (or |
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3 # copypaste) --input file.txt --ontology "BP/CC/MF" --option option (e.g |
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4 # : classic/elim...) --threshold threshold --correction correction --textoutput |
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5 # text --barplotoutput barplot |
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6 # --dotplotoutput dotplot --column column --geneuniver human |
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7 # e.g : Rscript --vanilla enrichment_v3.R --inputtype tabfile --input file.txt |
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8 # --ontology BP --option classic --threshold 1e-15 --correction holm |
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9 # --textoutput TRUE |
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10 # --barplotoutput TRUE --dotplotoutput TRUE --column c1 --geneuniverse |
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11 # org.Hs.eg.db |
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12 # INPUT : |
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13 # - type of input. Can be ids separated by a blank space (copypast), or a text |
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14 # file (tabfile) |
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15 # - file with at least one column of ensembl ids |
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16 # - gene ontology category : Biological Process (BP), Cellular Component (CC), Molecular Function (MF) |
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17 # - test option (relative to topGO algorithms) : elim, weight01, parentchild, or no option (classic) |
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18 # - threshold for enriched GO term pvalues (e.g : 1e-15) |
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19 # - correction for multiple testing (see p.adjust options : holm, hochberg, hommel, bonferroni, BH, BY,fdr,none |
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20 # - outputs wanted in this order text, barplot, dotplot with boolean value (e.g |
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21 # : TRUE TRUE TRUE ). |
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22 # Declare the output not wanted as none |
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23 # - column containing the ensembl ids if the input file is a tabfile |
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24 # - gene universe reference for the user chosen specie |
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25 # - header : if the input is a text file, does this text file have a header |
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26 # (TRUE/FALSE) |
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27 # |
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28 # OUTPUT : |
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29 # - outputs commanded by the user named respectively result.tsv for the text |
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30 # results file, barplot.png for the barplot image file and dotplot.png for the |
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31 # dotplot image file |
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32 |
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33 options(warn=-1) #TURN OFF WARNINGS !!!!!! |
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34 |
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35 # loading topGO library |
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36 suppressMessages(library(topGO)) |
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37 |
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38 # Read file and return file content as data.frame |
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39 readfile = function(filename, header) { |
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40 if (header == "true") { |
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41 # Read only first line of the file as header: |
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42 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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43 #Read the data of the files (skipping the first row) |
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44 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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45 # Remove empty rows |
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46 file <- file[!apply(is.na(file) | file == "", 1, all), , drop=FALSE] |
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47 #And assign the header to the data |
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48 names(file) <- headers |
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49 } |
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50 else { |
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51 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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52 # Remove empty rows |
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53 file <- file[!apply(is.na(file) | file == "", 1, all), , drop=FALSE] |
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54 } |
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55 return(file) |
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56 } |
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57 |
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58 check_ens_ids <- function(vector) { |
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59 ens_pattern = "^(ENS[A-Z]+[0-9]{11}|[A-Z]{3}[0-9]{3}[A-Za-z](-[A-Za-z])?|CG[0-9]+|[A-Z0-9]+\\.[0-9]+|YM[A-Z][0-9]{3}[a-z][0-9])$" |
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60 return(grepl(ens_pattern,vector)) |
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61 } |
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62 |
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63 '%!in%' <- function(x,y)!('%in%'(x,y)) |
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64 |
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65 |
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66 # Parse command line arguments |
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67 |
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68 args = commandArgs(trailingOnly = TRUE) |
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69 |
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70 # create a list of the arguments from the command line, separated by a blank space |
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71 hh <- paste(unlist(args),collapse=' ') |
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72 |
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73 # delete the first element of the list which is always a blank space |
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74 listoptions <- unlist(strsplit(hh,'--'))[-1] |
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75 |
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76 # for each input, split the arguments with blank space as separator, unlist, |
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77 # and delete the first element which is the input name (e.g --inputtype) |
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78 options.args <- sapply(listoptions,function(x){ |
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79 unlist(strsplit(x, '[ \t\n]+'))[-1] |
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80 }) |
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81 # same as the step above, except that only the names are kept |
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82 options.names <- sapply(listoptions,function(x){ |
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83 option <- unlist(strsplit(x, '[ \t\n]+'))[1] |
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84 }) |
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85 names(options.args) <- unlist(options.names) |
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86 |
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87 |
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88 if (length(options.args) != 12) { |
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89 stop("Not enough/Too many arguments", call. = FALSE) |
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90 } |
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91 |
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92 #save(options.args,file="/home/dchristiany/proteore_project/ProteoRE/tools/topGO/args.Rda") |
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93 #load("/home/dchristiany/proteore_project/ProteoRE/tools/topGO/args.Rda") |
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94 |
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95 |
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96 typeinput = options.args[1] |
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97 listfile = options.args[2] |
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98 onto = as.character(options.args[3]) |
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99 option = as.character(options.args[4]) |
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100 correction = as.character(options.args[6]) |
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101 threshold = as.numeric(options.args[5]) |
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102 text = as.character(options.args[7]) |
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103 barplot = as.character(options.args[8]) |
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104 dotplot = as.character(options.args[9]) |
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105 column = as.numeric(gsub("c","",options.args[10])) |
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106 geneuniverse = as.character(options.args[11]) |
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107 header = as.character(options.args[12]) |
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108 |
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109 if (typeinput=="copypaste"){ |
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110 sample = as.data.frame(unlist(listfile)) |
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111 sample = sample[,column] |
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112 } |
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113 if (typeinput=="tabfile"){ |
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114 |
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115 if (header=="TRUE"){ |
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116 sample = readfile(listfile, "true") |
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117 }else{ |
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118 sample = readfile(listfile, "false") |
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119 } |
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120 sample = sample[,column] |
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121 } |
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122 |
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123 #check of ENS ids |
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124 if (! any(check_ens_ids(sample))){ |
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125 print("no ensembl gene ids found in your ids list, please check your IDs in input or the selected column of your input file") |
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126 stop() |
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127 } |
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128 |
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129 # Launch enrichment analysis and return result data from the analysis or the null |
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130 # object if the enrichment could not be done. |
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131 goEnrichment = function(geneuniverse,sample,onto){ |
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132 |
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133 # get all the GO terms of the corresponding ontology (BP/CC/MF) and all their |
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134 # associated ensembl ids according to the org package |
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135 xx = annFUN.org(onto,mapping=geneuniverse,ID="ensembl") |
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136 allGenes = unique(unlist(xx)) |
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137 # check if the genes given by the user can be found in the org package (gene |
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138 # universe), that is in |
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139 # allGenes |
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140 if (length(intersect(sample,allGenes))==0){ |
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141 |
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142 print("None of the input ids can be found in the org package data, enrichment analysis cannot be realized. \n The inputs ids probably have no associated GO terms.") |
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143 return(c(NULL,NULL)) |
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144 |
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145 } |
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146 |
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147 geneList = factor(as.integer(allGenes %in% sample)) |
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148 names(geneList) <- allGenes |
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149 |
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150 |
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151 #topGO enrichment |
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152 |
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153 |
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154 # Creation of a topGOdata object |
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155 # It will contain : the list of genes of interest, the GO annotations and the GO hierarchy |
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156 # Parameters : |
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157 # ontology : character string specifying the ontology of interest (BP, CC, MF) |
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158 # allGenes : named vector of type numeric or factor |
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159 # annot : tells topGO how to map genes to GO annotations. |
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160 # argument not used here : nodeSize : at which minimal number of GO annotations |
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161 # do we consider a gene |
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162 |
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163 myGOdata = new("topGOdata", description="SEA with TopGO", ontology=onto, allGenes=geneList, annot = annFUN.org, mapping=geneuniverse,ID="ensembl") |
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164 |
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165 |
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166 # Performing enrichment tests |
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167 result <- runTest(myGOdata, algorithm=option, statistic="fisher") |
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168 return(c(result,myGOdata)) |
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169 } |
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170 |
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171 # Some libraries such as GOsummaries won't be able to treat the values such as |
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172 # "< 1e-30" produced by topGO. As such it is important to delete the < char |
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173 # with the deleteInfChar function. Nevertheless the user will have access to the original results in the text output. |
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174 deleteInfChar = function(values){ |
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175 |
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176 lines = grep("<",values) |
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177 if (length(lines)!=0){ |
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178 for (line in lines){ |
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179 values[line]=gsub("<","",values[line]) |
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180 } |
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181 } |
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182 return(values) |
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183 } |
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184 |
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185 corrMultipleTesting = function(result, myGOdata,correction,threshold){ |
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186 |
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187 # adjust for multiple testing |
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188 if (correction!="none"){ |
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189 # GenTable : transforms the result object into a list. Filters can be applied |
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190 # (e.g : with the topNodes argument, to get for instance only the n first |
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191 # GO terms with the lowest pvalues), but as we want to apply a correction we |
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192 # take all the GO terms, no matter their pvalues |
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193 allRes <- GenTable(myGOdata, test = result, orderBy = "result", ranksOf = "result",topNodes=length(attributes(result)$score)) |
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194 # Some pvalues given by topGO are not numeric (e.g : "<1e-30). As such, these |
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195 # values are converted to 1e-30 to be able to correct the pvalues |
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196 pvaluestmp = deleteInfChar(allRes$test) |
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197 |
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198 # the correction is done from the modified pvalues |
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199 allRes$qvalues = p.adjust(pvaluestmp, method = as.character(correction), n = length(pvaluestmp)) |
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200 allRes = as.data.frame(allRes) |
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201 |
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202 # Rename the test column by pvalues, so that is more explicit |
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203 nb = which(names(allRes) %in% c("test")) |
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204 |
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205 names(allRes)[nb] = "pvalues" |
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206 |
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207 allRes = allRes[which(as.numeric(allRes$pvalues) <= threshold),] |
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208 if (length(allRes$pvalues)==0){ |
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209 print("Threshold was too stringent, no GO term found with pvalue equal or lesser than the threshold value") |
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210 return(NULL) |
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211 } |
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212 allRes = allRes[order(allRes$qvalues),] |
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213 } |
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214 |
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215 if (correction=="none"){ |
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216 # get all the go terms under user threshold |
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217 mysummary <- summary(attributes(result)$score <= threshold) |
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218 numsignif <- as.integer(mysummary[[3]]) |
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219 # get all significant nodes |
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220 allRes <- GenTable(myGOdata, test = result, orderBy = "result", ranksOf = "result",topNodes=numsignif) |
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221 |
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222 |
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223 allRes = as.data.frame(allRes) |
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224 # Rename the test column by pvalues, so that is more explicit |
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225 nb = which(names(allRes) %in% c("test")) |
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226 names(allRes)[nb] = "pvalues" |
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227 if (numsignif==0){ |
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228 |
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229 print("Threshold was too stringent, no GO term found with pvalue equal or lesser than the threshold value") |
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230 return(NULL) |
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231 } |
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232 |
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233 allRes = allRes[order(allRes$pvalues),] |
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234 } |
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235 |
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236 return(allRes) |
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237 } |
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238 |
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239 # roundValues will simplify the results by rounding down the values. For instance 1.1e-17 becomes 1e-17 |
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240 roundValues = function(values){ |
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241 for (line in 1:length(values)){ |
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242 values[line]=as.numeric(gsub(".*e","1e",as.character(values[line]))) |
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243 } |
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244 return(values) |
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245 } |
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246 |
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247 createDotPlot = function(data, onto){ |
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248 |
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249 values = deleteInfChar(data$pvalues) |
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250 values = roundValues(values) |
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251 values = as.numeric(values) |
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252 |
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253 geneRatio = data$Significant/data$Annotated |
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254 goTerms = data$Term |
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255 count = data$Significant |
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256 |
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257 labely = paste("GO terms",onto,sep=" ") |
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258 ggplot(data,aes(x=geneRatio,y=goTerms, color=values,size=count)) +geom_point( ) + scale_colour_gradientn(colours=c("red","violet","blue")) + xlab("Gene Ratio") + ylab(labely) + labs(color="p-values\n" ) |
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259 ggsave("dotplot.png", device = "png", dpi = 320, limitsize = TRUE, width = 15, height = 15, units="cm") |
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260 } |
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261 |
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262 createBarPlot = function(data, onto){ |
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263 |
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264 |
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265 values = deleteInfChar(data$pvalues) |
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266 values = roundValues(values) |
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267 |
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268 values = as.numeric(values) |
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269 goTerms = data$Term |
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270 count = data$Significant |
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271 |
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272 labely = paste("GO terms",onto,sep=" ") |
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273 ggplot(data, aes(x=goTerms, y=count,fill=values,scale(scale = 0.5))) + ylab("Gene count") + xlab(labely) +geom_bar(stat="identity") + scale_fill_gradientn(colours=c("red","violet","blue")) + coord_flip() + labs(fill="p-values\n") |
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274 ggsave("barplot.png", device = "png", dpi = 320, limitsize = TRUE, width = 15, height = 15, units="cm") |
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275 } |
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276 |
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277 |
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278 # Produce the different outputs |
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279 createOutputs = function(result, cut_result,text, barplot, dotplot, onto){ |
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280 |
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281 |
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282 if (is.null(result)){ |
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283 |
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284 if (text=="TRUE"){ |
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285 |
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286 err_msg = "None of the input ids can be found in the org package data, enrichment analysis cannot be realized. \n The inputs ids probably either have no associated GO terms or are not ENSG identifiers (e.g : ENSG00000012048)." |
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287 write.table(err_msg, file='result.csv', quote=FALSE, sep='\t', col.names = T, row.names = F) |
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288 |
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289 } |
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290 |
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291 if (barplot=="TRUE"){ |
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292 |
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293 png(filename="barplot.png") |
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294 plot.new() |
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295 #text(0,0,err_msg) |
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296 dev.off() |
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297 } |
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298 |
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299 if (dotplot=="TRUE"){ |
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300 |
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301 png(filename="dotplot.png") |
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302 plot.new() |
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303 #text(0,0,err_msg) |
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304 dev.off() |
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305 |
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306 } |
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307 return(TRUE) |
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308 } |
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311 if (is.null(cut_result)){ |
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312 |
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313 |
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314 if (text=="TRUE"){ |
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315 |
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316 err_msg = "Threshold was too stringent, no GO term found with pvalue equal or lesser than the threshold value." |
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317 write.table(err_msg, file='result.csv', quote=FALSE, sep='\t', col.names = T, row.names = F) |
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321 if (barplot=="TRUE"){ |
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322 |
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323 png(filename="barplot.png") |
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324 plot.new() |
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325 text(0,0,err_msg) |
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326 dev.off() |
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327 } |
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328 |
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329 if (dotplot=="TRUE"){ |
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330 |
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331 png(filename="dotplot.png") |
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332 plot.new() |
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333 text(0,0,err_msg) |
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334 dev.off() |
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335 |
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336 } |
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337 return(TRUE) |
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338 |
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339 |
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340 |
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341 } |
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342 |
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343 if (text=="TRUE"){ |
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344 write.table(cut_result, file='result.csv', quote=FALSE, sep='\t', col.names = T, row.names = F) |
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345 } |
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346 |
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347 if (barplot=="TRUE"){ |
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348 |
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349 createBarPlot(cut_result, onto) |
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350 } |
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351 |
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352 if (dotplot=="TRUE"){ |
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353 |
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354 createDotPlot(cut_result, onto) |
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355 } |
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356 } |
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357 |
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358 |
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359 |
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360 # Load R library ggplot2 to plot graphs |
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361 suppressMessages(library(ggplot2)) |
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362 |
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363 # Launch enrichment analysis |
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364 allresult = suppressMessages(goEnrichment(geneuniverse,sample,onto)) |
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365 result = allresult[1][[1]] |
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366 myGOdata = allresult[2][[1]] |
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367 if (!is.null(result)){ |
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368 |
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369 # Adjust the result with a multiple testing correction or not and with the user |
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370 # p-value cutoff |
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371 cut_result = corrMultipleTesting(result,myGOdata, correction,threshold) |
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372 }else{ |
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373 |
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374 cut_result=NULL |
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375 |
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376 } |
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377 |
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378 |
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379 createOutputs(result, cut_result,text, barplot, dotplot, onto) |
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380 |