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1 # #clusterfinder_Auto.R makes a file containig the coordenates of the identified cluster/-s by Silhouette criterion
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2 #
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3 #!/usr/bin/env Rscript
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4 args = commandArgs(trailingOnly=TRUE)
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5
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6 if(length(args) < 1){
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7 stop("USE: Rscript clusterfinder_Auto.R <session_id>")
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8 }
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9
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10 #Arguments definition
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11
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12 session_id <- args[1]
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13
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14 print(session_id)
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15
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16 setwd(session_id)
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17
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18 options(error=traceback)
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19 options(warn=-1)
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20
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21 #Load required library
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22 library(cluster)
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23
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24 #Load required libraries
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25 library(clValid)
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26
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27 #Loading required package: cluster
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28 library(fpc)
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29
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30 #Install NbClust package
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31 #install.packages("NbClust",dependencies = TRUE)
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32
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33 #Loading required package: NbClust
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34 library(NbClust)
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35
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36
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37 #Read the dataset
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38 data_centers <- read.table("fillouts1file.txt", header=TRUE, sep=";", na.strings="")
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39 #data_centers <- read.table("fillouts1file.txt", header=TRUE, sep=";", na.strings="")
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40
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41
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42 #Run the XXX for selection of the number of clusters
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43
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44 clustersdata <- NbClust(data_centers, diss=NULL, distance = "euclidean", min.nc=2, max.nc=15, method = "kmeans", index = "all", alphaBeale = 0.1)
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45
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46 #capture.output(clustersdata$Best.nc[1,], file = "bestnumberclusters.txt")
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47
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48 #capture.output(NbClust(data_centers, diss=NULL, distance = "euclidean", min.nc=2, max.nc=15, method = "kmeans", index = "all", alphaBeale = 0.1), file = "proofclusters.txt")
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49
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50 #capture.output(NbClust(data_centers, diss=NULL, distance = "euclidean", min.nc=2, max.nc=15, method = "kmeans", index = "all", alphaBeale = 0.1), file = "numclusters.txt")
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51
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52 #best_num_cluster <- scan("proofclusters.txt", what = character(), quiet = TRUE)
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53
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54 k.max <- as.integer(rownames(table(clustersdata$Best.nc[1,]))[which.max(apply(table(clustersdata$Best.nc[1,]),MARGIN=1,max))])
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55
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56 km.res <- kmeans(as.matrix(data_centers), centers = k.max, nstart = 25)
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57
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58 best_position <- as.integer(rownames(table(clustersdata$Best.nc[1,]))[which.max(apply(table(clustersdata$Best.nc[1,]),MARGIN=1,max))])
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59
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60 # Make clustercoordenates table
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61 #X Coordenate
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62 start_x <- best_position + 1
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63 end_x <- start_x + (best_position - 1)
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64
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65 cluster_pos_x <- 1
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66 x_pos <- c()
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67 for (c in start_x:end_x){
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68 x_pos[cluster_pos_x] <- km.res$centers[c]
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69 cluster_pos_x <- cluster_pos_x + 1
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70 }
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71 #print(x_pos)
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72
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73 #Y Coordenate
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74 start_y <- end_x + 1
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75 end_y <- start_y + (best_position - 1)
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76
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77 cluster_pos_y <- 1
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78 y_pos <- c()
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79 for(cc in start_y:end_y){
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80 y_pos[cluster_pos_y] <- km.res$centers[cc]
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81 cluster_pos_y <- cluster_pos_y + 1
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82 }
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83 #print(y_pos)
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84
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85 #Z Coordenate
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86 start_z <- end_y + 1
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87 end_z <- start_z + (best_position - 1)
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88
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89 cluster_pos_z <- 1
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90 z_pos <- c()
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91 for(ccc in start_z:end_z){
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92 z_pos[cluster_pos_z] <- km.res$centers[ccc]
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93 cluster_pos_z <- cluster_pos_z + 1
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94 }
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95 #print(z_pos)
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96
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97 #Create a file with clusters coordenates. Cluster coordenates are vectors x_pos, y_pos, z_pos
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98 num_filas <- length(x_pos)
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99 clusters_tabla <- matrix(1, nrow = num_filas, ncol = 4) #columns are column 1 number of ccluster, column 2 x-coordenates, colum 3 y-coordenates, column 4 z-coordenates
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100
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101 ##Add number of cluster located in column 1
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102 for(i in 1:num_filas){
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103 clusters_tabla[i,1] <- i
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104 }
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105
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106 ##Add cluster x,y,z-coordenates in clusters_tabla
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107 #x-coordenates
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108 v_pos_x <- 1
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109 for(f in 1:num_filas){
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110 clusters_tabla[f,2] <- x_pos[v_pos_x]
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111 v_pos_x <- v_pos_x + 1
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112 }
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113
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114 #y-coordenates
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115 v_pos_y <- 1
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116 for(ff in 1:num_filas){
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117 clusters_tabla[ff,3] <- y_pos[v_pos_y]
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118 v_pos_y <- v_pos_y + 1
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119 }
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120
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121 #z-coordenates
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122 v_pos_z <- 1
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123 for(fff in 1:num_filas){
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124 clusters_tabla[fff,4] <- z_pos[v_pos_z]
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125 v_pos_z <- v_pos_z + 1
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126 }
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127
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128 #Write the head for the file which contains the number of clusters annd their coordenates
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129 cabecera_clusterscoordenates <- paste("cluster", sep = " ")
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130 cabecera <- c("x","y","z")
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131
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132 for(i in 1:3){
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133 cabecera_clusterscoordenates <- paste(cabecera_clusterscoordenates, cabecera[i], sep=" ")
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134 }
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135 #write(cabecera_clusterscoordenates, file="/home/galaxy/galaxy/tools/proteindocking/scripts/clusterscoordenates.txt", append= TRUE)
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136 write(cabecera_clusterscoordenates, file="clusterscoordenates.txt", append= TRUE)
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137
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138 #Write rows containing number of cluster and coordenates
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139 for(i in 1:nrow(clusters_tabla)){
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140 fila_completa <- paste(clusters_tabla[i,1], sep = " ")
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141 for(j in 2:ncol(clusters_tabla)){
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142 fila_completa <- paste(fila_completa, clusters_tabla[i,j], sep=" ")
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143 }
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144 write(fila_completa, file="clusterscoordenates.txt", append= TRUE)
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145 #write(fila_completa, file="/home/galaxy/galaxy/tools/proteindocking/scripts/clusterscoordenates.txt", append= TRUE)
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146 }
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147
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148
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149 #Table to see Binding Sited finded in Galaxy screen
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150 #BS_screen <- scan("/home/galaxy/galaxy/tools/proteindocking/scripts/clusterscoordenates.txt", what = character(), quiet = TRUE)
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151 BS_screen <- scan("clusterscoordenates.txt", what = character(), quiet = TRUE)
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152
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153 BS_table <- matrix(1, nrow = nrow(clusters_tabla), ncol = ncol(clusters_tabla))
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154
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155 bs_count <- 5
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156 for(i in 1:nrow(clusters_tabla)){
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157 for(j in 1:ncol(clusters_tabla)){
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158 BS_table[i,j] <- BS_screen[bs_count]
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159 bs_count <- bs_count +1
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160 }
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161 }
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162
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163 colnames(BS_table) <- c("Binding Site", "X", "Y", "Z")
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164 print(BS_table)
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165
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