Mercurial > repos > marie-tremblay-metatoul > 2dnmrannotation

annotate annotationRmn2DGlobale.R @ 3:546c7ccd2ed4 draft default tip

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"planemo upload for repository https://github.com/workflow4metabolomics/tools-metabolomics commit 911f4beba3dcb25c1033e8239426f8f763683523"
author workflow4metabolomics
date Fri, 04 Feb 2022 09:01:11 +0000
parents dff7bde22102
children
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1 ###################################################################################################
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2 # ANNOTATION SPECTRE 2D MATRICE COMPLEXE BASEE SUR UNE (OU PLUSIEURS) SEQUENCE(s) #
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3 # template : dataframe contenant la liste des couples de deplacements chimiques de la matrice complexe a annoter #
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4 # cosy : 1 si sequence a utiliser / 0 sinon #
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5 # hmbc : 1 si sequence a utiliser / 0 sinon #
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6 # hsqc : 1 si sequence a utiliser / 0 sinon #
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7 # jres : 1 si sequence a utiliser / 0 sinon #
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8 # tocsy : 1 si sequence a utiliser / 0 sinon #
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9 # tolPpm1 : tolerance autorisee autour de la valeur1 du couple de deplacements chimiques #
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10 # tolPpm2HJRes : tolerance autorisee autour de la valeur2 du couple de deplacements chimiques si H dans dimension 2 #
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11 # tolPpm2C : tolerance autorisee autour de la valeur2 du couple de deplacements chimiques si C dans dimension 2 #
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12 # seuil : valeur du score de presence en dela de laquelle les metabolites annotes ne sont pas retenus #
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13 # unicite : boolean pour ne retenir que les ... #
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14 ###################################################################################################
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15 ## CALCUL MOYENNE SANS VALEUR(S) MANQUANTE(S)
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16 mean.rmNa <- function(x) {
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17 mean(x, na.rm = TRUE)
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18 }
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20 annotationRmn2DGlobale <- function(template, tolPpm1 = 0.01, tolPpm2HJRes = 0.002, tolPpm2C = 0.5, cosy = 1, hmbc = 1, hsqc = 1, jres = 1, tocsy = 1, seuil, unicite = "NO") {
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21 ## Initialisation
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22 options(max.print = 999999999)
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23 annotationCOSY <- data.frame()
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24 annotationHMBC <- data.frame()
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25 annotationHSQC <- data.frame()
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26 annotationJRES <- data.frame()
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27 annotationTOCSY <- data.frame()
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29 dataCOSY <- "NA"
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30 dataHMBC <- "NA"
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31 dataHSQC <- "NA"
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32 dataJRES <- "NA"
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33 dataTOCSY <- "NA"
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35 ## Application seuil seulement si annotation avec 1 seule sequence
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36 seuilPls2D <- seuil
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38 if (cosy == 1) {
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39 matrice.cosy <- read.xlsx(template, sheet = "COSY", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA")
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40 matrice.cosy <- matrice.cosy[matrice.cosy$peak.index != "x", ]
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41 annotationCOSY <- annotationRmn2D(matrice.cosy, BdDReference_COSY, "COSY", ppm1Tol = tolPpm1, ppm2Tol = tolPpm1, seuil = seuilPls2D, unicite = unicite)
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42 dataCOSY <- data.frame(Metabolite = str_to_lower(annotationCOSY$liste_resultat$Metabolite), score.COSY = annotationCOSY$liste_resultat$score)
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43 dataCOSY <- unique.data.frame(dataCOSY)
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44 }
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46 if (hmbc == 1) {
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47 matrice.hmbc <- read.xlsx(template, sheet = "HMBC", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA")
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48 matrice.hmbc <- matrice.hmbc[matrice.hmbc$peak.index != "x", ]
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49 annotationHMBC <- annotationRmn2D(matrice.hmbc, BdDReference_HMBC, "HMBC", ppm1Tol = tolPpm1, ppm2Tol = tolPpm2C, seuil = seuilPls2D, unicite = unicite)
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50 dataHMBC <- data.frame(Metabolite = str_to_lower(annotationHMBC$liste_resultat$Metabolite), score.HMBC = annotationHMBC$liste_resultat$score)
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51 dataHMBC <- unique.data.frame(dataHMBC)
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52 }
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54 if (hsqc == 1) {
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55 matrice.hsqc <- read.xlsx(template, sheet = "HSQC", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA")
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56 matrice.hsqc <- matrice.hsqc[matrice.hsqc$peak.index != "x", ]
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57 annotationHSQC <- annotationRmn2D(matrice.hsqc, BdDReference_HSQC, "HSQC", ppm1Tol = tolPpm1, ppm2Tol = tolPpm2C, seuil = seuilPls2D, unicite = unicite)
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58 dataHSQC <- data.frame(Metabolite = str_to_lower(annotationHSQC$liste_resultat$Metabolite), score.HSQC = annotationHSQC$liste_resultat$score)
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59 dataHSQC <- unique.data.frame(dataHSQC)
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60 }
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62 if (jres == 1) {
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63 matrice.jres <- read.xlsx(template, sheet = "JRES", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA")
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64 matrice.jres <- matrice.jres[matrice.jres$peak.index != "x", ]
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65 annotationJRES <- annotationRmn2D(matrice.jres, BdDReference_JRES, "JRES", ppm1Tol = tolPpm1, ppm2Tol = tolPpm2HJRes, seuil = seuilPls2D, unicite = unicite)
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66 dataJRES <- data.frame(Metabolite = str_to_lower(annotationJRES$liste_resultat$Metabolite), score.JRES = annotationJRES$liste_resultat$score)
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67 dataJRES <- unique.data.frame(dataJRES)
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68 }
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70 if (tocsy == 1) {
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71 matrice.tocsy <- read.xlsx(template, sheet = "TOCSY", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA")
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72 matrice.tocsy <- matrice.tocsy[matrice.tocsy$peak.index != "x", ]
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73 annotationTOCSY <- annotationRmn2D(matrice.tocsy, BdDReference_TOCSY, "TOCSY", ppm1Tol = tolPpm1, ppm2Tol = tolPpm1, seuil = seuilPls2D, unicite = unicite)
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74 dataTOCSY <- data.frame(Metabolite = str_to_lower(annotationTOCSY$liste_resultat$Metabolite), score.TOCSY = annotationTOCSY$liste_resultat$score)
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75 dataTOCSY <- unique.data.frame(dataTOCSY)
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76 }
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78 seqCombiMeanScoreSeuil <- data.frame()
546c7ccd2ed4 "planemo upload for repository https://github.com/workflow4metabolomics/tools-metabolomics commit 911f4beba3dcb25c1033e8239426f8f763683523"
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79 seqCombiMeanScoreSeuilFiltre <- data.frame()
546c7ccd2ed4 "planemo upload for repository https://github.com/workflow4metabolomics/tools-metabolomics commit 911f4beba3dcb25c1033e8239426f8f763683523"
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80
2
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81 ## CONCATENATION RESULTATS DIFFERENTES SEQUENCES
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82 data2D <- list(dataCOSY, dataHMBC, dataHSQC, dataJRES, dataTOCSY)
dff7bde22102 "planemo upload for repository https://github.com/workflow4metabolomics/tools-metabolomics commit b3abcb650e9b38458aa0ac5f7d838811d982ff65"
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83 whichSequenceNaN <- which((data2D != "NA"))
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84 data2D <- data2D[whichSequenceNaN]
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85 sequencesCombination <- data.frame(data2D[1])
3
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86 seqCombiMeanScore <- sequencesCombination
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87
2
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88 ## Si une seule sequence et seuil sur score = filtre applique dans la fonction annotationRmn2D
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89 if (length(data2D) >= 2) {
2
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90 ## CONCATENATION SCORE PAR SEQUENCE
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91 for (l in 2:length(data2D))
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92 sequencesCombination <- merge.data.frame(sequencesCombination, data2D[l], by = "Metabolite", all.x = TRUE, all.y = TRUE)
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93
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94 ## Replacement of NA values due to mis annotation
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95 for (m in seq_len(nrow(sequencesCombination))) {
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96 COSYcompound <- sort(names(BdDReference_COSY))
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97 HMBCcompound <- sort(names(BdDReference_HMBC))
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98 HSQCcompound <- sort(names(BdDReference_HSQC))
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99 JREScompound <- sort(names(BdDReference_JRES))
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100 TOCSYcompound <- sort(names(BdDReference_TOCSY))
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101
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102 if (is.na(sequencesCombination[m, 2])) {
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103 compound <- as.character(sequencesCombination[m, 1])
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104 for (c in seq_len(length(COSYcompound)))
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105 if (str_to_lower(compound) == str_to_lower(COSYcompound[c]))
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106 sequencesCombination[m, 2] <- 0
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107 }
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108
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109 if (is.na(sequencesCombination[m, 3])) {
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110 compound <- as.character(sequencesCombination[m, 1])
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111 for (c in seq_len(length(HMBCcompound)))
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112 if (str_to_lower(compound) == str_to_lower(HMBCcompound[c]))
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113 sequencesCombination[m, 3] <- 0
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114 }
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115
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116 if (is.na(sequencesCombination[m, 4])) {
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117 compound <- as.character(sequencesCombination[m, 1])
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118 for (c in seq_len(length(HSQCcompound)))
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119 if (str_to_lower(compound) == str_to_lower(HSQCcompound[c]))
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120 sequencesCombination[m, 4] <- 0
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121 }
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122
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123 if (is.na(sequencesCombination[m, 5])) {
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124 compound <- as.character(sequencesCombination[m, 1])
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125 for (c in seq_len(length(JREScompound)))
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126 if (str_to_lower(compound) == str_to_lower(JREScompound[c]))
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127 sequencesCombination[m, 5] <- 0
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128 }
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129
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130 if (is.na(sequencesCombination[m, 6])) {
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131 compound <- as.character(sequencesCombination[m, 1])
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132 for (c in seq_len(length(TOCSYcompound)))
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133 if (str_to_lower(compound) == str_to_lower(TOCSYcompound[c]))
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134 sequencesCombination[m, 6] <- 0
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135 }
2
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136 }
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137
3
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138 ## SCORE MOYEN (sans prise en compte valeurs manquantes)
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139 meanScore <- round(apply(sequencesCombination[, -1], 1, FUN = mean.rmNa), 2)
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140 seqCombiMeanScore <- cbind.data.frame(sequencesCombination, averageScore = meanScore)
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141
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142 ## SUPPRESSION METABOLITE AVEC SCORE MOYEN < SEUIL
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143 seqCombiMeanScoreSeuilFiltre <- seqCombiMeanScore[seqCombiMeanScore$averageScore > seuil, ]
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144 }
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145
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146 return(list(COSY = annotationCOSY, HMBC = annotationHMBC, HSQC = annotationHSQC, JRES = annotationJRES, TOCSY = annotationTOCSY, combination = seqCombiMeanScoreSeuilFiltre))
2
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147 }