Mercurial > repos > ethevenot > batchcorrection

annotate batch_correction_all_loess_script.R @ 2:57edfd3943ab draft

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planemo upload for repository https://github.com/workflow4metabolomics/batch_correction.git commit a619f606894efd016387a0b6f6bf0d5addc38bc9-dirty
author ethevenot
date Mon, 01 May 2017 08:06:08 -0400
parents 78cb1efd7606
children
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78cb1efd7606 planemo upload for repository https://github.com/workflow4metabolomics/batch_correction.git commit 400138235741cbb21fd7c7ddeeb572235fa769c2-dirty
ethevenot
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1 loessF <- function(datVn, qcaVi, preVi, spnN) {
78cb1efd7606 planemo upload for repository https://github.com/workflow4metabolomics/batch_correction.git commit 400138235741cbb21fd7c7ddeeb572235fa769c2-dirty
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ethevenot
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3 if(length(qcaVi) < 5) {
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5 return(predict(lm(datVn[qcaVi] ~ qcaVi),
78cb1efd7606 planemo upload for repository https://github.com/workflow4metabolomics/batch_correction.git commit 400138235741cbb21fd7c7ddeeb572235fa769c2-dirty
ethevenot
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6 newdata = data.frame(qcaVi = preVi)))
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8 } else {
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10 return(predict(loess(datVn[qcaVi] ~ qcaVi,
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11 control = loess.control(surface = "direct"),
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12 span = spnN),
78cb1efd7606 planemo upload for repository https://github.com/workflow4metabolomics/batch_correction.git commit 400138235741cbb21fd7c7ddeeb572235fa769c2-dirty
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13 newdata = data.frame(qcaVi = preVi)))
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15 }
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17 ## Note:
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18 ## the surface = 'direct' argument allows extrapolation
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20 } ## loessF
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22 plotBatchF <- function(datMN, samDF.arg, spnN.arg) {
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24 maiC <- switch(gsub("MN", "", deparse(substitute(datMN))),
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25 raw = "Raw",
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26 nrm = "Normalized")
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28 colVc <- c(sample = "green4",
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29 pool = "red",
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30 blank = "black",
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31 other = "yellow")
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33 par(font = 2, font.axis = 2, font.lab = 2, lwd = 2, pch = 18)
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35 layout(matrix(c(1, 1, 2, 3), nrow = 2),
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36 widths = c(0.7, 0.3))
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38 obsNamVc <- rownames(datMN)
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40 obsColVc <- sapply(samDF.arg[, "sampleType"],
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41 function(typC)
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42 ifelse(typC %in% names(colVc), colVc[typC], colVc["other"]))
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44 ## Graphic 1: Sum of intensities for each sample
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46 par(mar = c(3.6, 3.6, 3.1, 0.6))
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48 batTab <- table(samDF.arg[, "batch"])
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50 sumVn <- rowSums(datMN, na.rm = TRUE)
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52 plot(sumVn,
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53 cex = 1.2,
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54 col = obsColVc,
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55 pch = 18,
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56 xaxs = "i",
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57 xlab = "",
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58 ylab = "")
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59
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60 mtext("Injection order",
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61 line = 2.2,
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62 side = 1)
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63 mtext("Sum of variable intensities",
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64 line = 2.2,
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65 side = 2)
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66
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67 mtext(maiC, cex = 1.2, line = 1.5, side = 3)
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68
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69 abline(v = cumsum(batTab) + 0.5,
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70 col = "red")
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71
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72 mtext(names(batTab),
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73 at = batTab / 2 + c(0, cumsum(batTab[-length(batTab)])))
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74
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75 obsColVuc <- obsColVc[sort(unique(names(obsColVc)))]
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76
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77 text(rep(batTab[1], times = length(obsColVuc)),
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78 par("usr")[3] + (0.97 - length(obsColVuc) * 0.03 + 1:length(obsColVuc) * 0.03) * diff(par("usr")[3:4]),
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79 col = obsColVuc,
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80 font = 2,
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81 labels = names(obsColVuc),
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82 pos = 2)
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83
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84 for(batC in names(batTab)) {
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85
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86 batSeqVi <- which(samDF.arg[, "batch"] == batC)
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87 batPooVi <- intersect(batSeqVi,
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88 grep("pool", samDF.arg[, "sampleType"]))
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89 batSamVi <- intersect(batSeqVi,
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90 grep("sample", samDF.arg[, "sampleType"]))
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91 if(length(batPooVi))
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92 lines(batSeqVi,
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93 loessF(sumVn, batPooVi, batSeqVi, spnN=spnN.arg),
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94 col = colVc["pool"])
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95 lines(batSeqVi,
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96 loessF(sumVn, batSamVi, batSeqVi, spnN=spnN.arg),
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97 col = colVc["sample"])
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98
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99 }
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100
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101 ## Graphics 2 and 3 (right): PCA score plots of components 1-4
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102
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103 radVn <- seq(0, 2 * pi, length.out = 100)
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104 epsN <- .Machine[["double.eps"]] ## [1] 2.22e-16
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105
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106 pcaMN <- datMN
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107
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108 if(any(is.na(pcaMN))) {
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109 minN <- min(pcaMN, na.rm = TRUE)
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110 pcaMN[is.na(pcaMN)] <- minN
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111 }
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112
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113 pcaLs <- opls(pcaMN, predI = 4, algoC = "svd", printL = FALSE, plotL = FALSE)
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114 tMN <- getScoreMN(pcaLs)
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115 vRelVn <- pcaLs@modelDF[, "R2X"]
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116
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117 n <- nrow(tMN)
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118 hotN <- 2 * (n - 1) * (n^2 - 1) / (n^2 * (n - 2))
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119
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120 hotFisN <- hotN * qf(0.95, 2, n - 2)
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121
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122 pcsLs <- list(c(1, 2), c(3, 4))
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123
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124 par(mar = c(3.6, 3.6, 0.6, 1.1))
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125
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126 for(pcsN in 1:length(pcsLs)) {
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127
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128 pcsVn <- pcsLs[[pcsN]]
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129
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130 tcsMN <- tMN[, pcsVn]
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131
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132 micMN <- solve(cov(tcsMN))
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133
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134 n <- nrow(tMN)
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135 hotN <- 2 * (n - 1) * (n^2 - 1) / (n^2 * (n - 2))
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136
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137 hotFisN <- hotN * qf(0.95, 2, n - 2)
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138
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139 hotVn <- apply(tcsMN,
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140 1,
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141 function(x) 1 - pf(1 / hotN * t(as.matrix(x)) %*% micMN %*% as.matrix(x), 2, n - 2))
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142
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143 obsHotVi <- which(hotVn < 0.05)
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144
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145 xLabC <- paste("t",
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146 pcsVn[1],
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147 "(",
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148 round(vRelVn[pcsVn[1]] * 100),
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149 "%)",
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150 sep = "")
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151
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152 yLabC <- paste("t",
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153 pcsVn[2],
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154 "(",
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155 round(vRelVn[pcsVn[2]] * 100),
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156 "%)",
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157 sep = "")
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158
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159 xLimVn <- c(-1, 1) * max(sqrt(var(tcsMN[, 1]) * hotFisN), max(abs(tcsMN[, 1])))
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160 yLimVn <- c(-1, 1) * max(sqrt(var(tcsMN[, 2]) * hotFisN), max(abs(tcsMN[, 2])))
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161
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162 plot(tcsMN,
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163 main = "",
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164 type = "n",
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165 xlab = "",
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166 ylab = "",
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167 xlim = xLimVn,
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168 ylim = yLimVn)
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169
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170 mtext(xLabC,
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171 line = 2.2,
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172 side = 1)
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173 mtext(yLabC,
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174 line = 2.2,
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175 side = 2)
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176
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177 par(lwd = 1)
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178
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179 abline(v = axTicks(1),
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180 col = "grey")
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181
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182 abline(h = axTicks(2),
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183 col = "grey")
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184
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185 abline(v = 0)
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186 abline(h = 0)
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187
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188 lines(sqrt(var(tcsMN[, 1]) * hotFisN) * cos(radVn),
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189 sqrt(var(tcsMN[, 2]) * hotFisN) * sin(radVn))
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190
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191 points(tcsMN,
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192 col = obsColVc,
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193 pch = 18)
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194
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195 if(length(obsHotVi))
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196 text(tcsMN[obsHotVi, 1],
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197 tcsMN[obsHotVi, 2],
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198 col = obsColVc[obsHotVi],
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199 labels = obsNamVc[obsHotVi],
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200 pos = 3)
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201
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202 } ## for(pcsN in 1:length(pcsLs)) {
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203
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204 return(invisible(list(sumVn = sumVn,
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205 tcsMN = tcsMN)))
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206
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207 } ## plotBatchF
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208
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209 shiftBatchCorrectF <- function(rawMN.arg,
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210 samDF.arg,
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211 refC.arg,
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212 spnN.arg) {
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213
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214 cat("\nReference observations are: ", refC.arg, "\n")
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215
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216 ## computing median off all pools (or samples) for each variable
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217
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218 refMeaVn <- apply(rawMN.arg[samDF.arg[, "sampleType"] == refC.arg, ],
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219 2,
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220 function(feaRefVn) mean(feaRefVn, na.rm = TRUE))
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221
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222 ## splitting data and sample metadata from each batch
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223
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224 batRawLs <- split(as.data.frame(rawMN.arg),
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225 f = samDF.arg[, "batch"])
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226 batRawLs <- lapply(batRawLs, function(inpDF) as.matrix(inpDF))
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227
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228 batSamLs <- split(as.data.frame(samDF.arg),
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229 f = samDF.arg[, "batch"])
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230
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231 ## checking extrapolation: are there pools at the first and last observations of each batch
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232
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233 if(refC.arg == "pool") {
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234 pooExtML <- matrix(FALSE, nrow = 2, ncol = length(batRawLs),
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235 dimnames = list(c("first", "last"), names(batRawLs)))
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236 for(batC in names(batSamLs)) {
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237 batSamTypVc <- batSamLs[[batC]][, "sampleType"]
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238 pooExtML["first", batC] <- head(batSamTypVc, 1) == "pool"
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239 pooExtML["last", batC] <- tail(batSamTypVc, 1) == "pool"
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240 }
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241 if(!all(c(pooExtML))) {
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242 cat("\nWarning: Pools are missing at the first and/or last position of the following batches:\n")
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243 pooExtBatVi <- which(!apply(pooExtML, 2, all))
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244 for(i in 1:length(pooExtBatVi))
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245 cat(names(pooExtBatVi)[i], ": ",
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246 paste(rownames(pooExtML)[!pooExtML[, pooExtBatVi[i]]], collapse = ", "), "\n", sep = "")
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247 cat("Extrapolating loess fits for these batches may result in inaccurate modeling!\n")
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248 }
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249 }
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250
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251 ## normalizing
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252
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253 nrmMN <- NULL ## normalized data matrix to be computed
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254
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255 cat("\nProcessing batch:")
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256
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257 for(batC in names(batRawLs)) { ## processing each batch individually
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258
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259 cat("\n", batC)
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260
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261 batRawMN <- batRawLs[[batC]]
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262 batSamDF <- batSamLs[[batC]]
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263
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264 batAllVi <- 1:nrow(batRawMN)
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265
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266 batRefVi <- grep(refC.arg, batSamDF[, "sampleType"])
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267
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268 if(length(batRefVi) < 5)
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269 cat("\nWarning: less than 5 '", refC.arg, "'; linear regression will be performed instead of loess regression for this batch\n", sep="")
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270
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271 ## prediction of the loess fit
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272
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273 batLoeMN <- apply(batRawMN,
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274 2,
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275 function(rawVn) loessF(rawVn, batRefVi, batAllVi, spnN=spnN.arg))
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276
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277 ## normalization
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278
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279 batLoeMN[batLoeMN <= 0] <- NA
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280
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281 batNrmMN <- batRawMN / batLoeMN
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282
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283 nrmMN <- rbind(nrmMN,
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284 batNrmMN)
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285
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286 }
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288 cat("\n")
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290 nrmMN <- sweep(nrmMN, MARGIN = 2, STATS = refMeaVn, FUN = "*")
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292 return(nrmMN)
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294 } ## shiftBatchCorrectF