Mercurial > repos > recetox > waveica

annotate waveica_wrapper.R @ 8:bf32ae95a06f draft default tip

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planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/waveica commit 44e9371974b176490222f96d532df2421571cbaa
author recetox
date Tue, 06 Aug 2024 14:27:48 +0000
parents 1a2aeb8137bf
children
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6480c6d5fa36 planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/waveica commit 77bea6c84c5a932fa203723d4d5fea6147f06505
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1 read_file <- function(file, metadata, ft_ext, mt_ext, transpose) {
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2 data <- read_data(file, ft_ext)
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4 if (transpose) {
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5 col_names <- c("sampleName", data[[1]])
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6 data <- tranpose_data(data, col_names)
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7 }
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9 if (!is.na(metadata)) {
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10 mt_data <- read_data(metadata, mt_ext)
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11 data <- merge(mt_data, data, by = "sampleName")
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12 }
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14 return(data)
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15 }
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b77023c41c76 planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/waveica commit d82e7dad96bebe9424ac7bf490e2786d82c3681a
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17 read_data <- function(file, ext) {
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18 if (ext == "csv") {
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19 data <- read.csv(file, header = TRUE)
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20 } else if (ext == "tsv") {
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21 data <- read.csv(file, header = TRUE, sep = "\t")
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22 } else {
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23 data <- arrow::read_parquet(file)
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24 }
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26 return(data)
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27 }
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29 waveica <- function(file,
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30 metadata = NA,
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31 ext,
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32 transpose = FALSE,
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33 wavelet_filter,
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34 wavelet_length,
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35 k,
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36 t,
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37 t2,
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38 alpha,
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39 exclude_blanks) {
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40 # get input from the Galaxy, preprocess data
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41 ext <- strsplit(x = ext, split = "\\,")[[1]]
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43 ft_ext <- ext[1]
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44 mt_ext <- ext[2]
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46 data <- read_file(file, metadata, ft_ext, mt_ext, transpose)
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48 required_columns <- c(
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49 "sampleName", "class", "sampleType",
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50 "injectionOrder", "batch"
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51 )
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52 data <- verify_input_dataframe(data, required_columns)
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54 data <- sort_by_injection_order(data)
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56 # separate data into features, batch and group
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57 feature_columns <- colnames(data)[!colnames(data) %in% required_columns]
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58 features <- data[, feature_columns]
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59 group <- enumerate_groups(as.character(data$sampleType))
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60 batch <- data$batch
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62 # run WaveICA
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63 features <- recetox.waveica::waveica(
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64 data = features,
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65 wf = get_wf(wavelet_filter, wavelet_length),
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66 batch = batch,
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67 group = group,
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68 K = k,
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69 t = t,
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70 t2 = t2,
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71 alpha = alpha
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72 )
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74 data[, feature_columns] <- features
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76 # remove blanks from dataset
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77 if (exclude_blanks) {
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78 data <- exclude_group(data, group)
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79 }
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81 return(data)
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82 }
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84 waveica_singlebatch <- function(file,
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85 metadata = NA,
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86 ext,
2
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87 transpose = FALSE,
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88 wavelet_filter,
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89 wavelet_length,
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90 k,
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91 alpha,
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92 cutoff,
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93 exclude_blanks) {
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94 # get input from the Galaxy, preprocess data
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95 ext <- strsplit(x = ext, split = "\\,")[[1]]
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96
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97 ft_ext <- ext[1]
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98 mt_ext <- ext[2]
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99
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100 data <- read_file(file, metadata, ft_ext, mt_ext, transpose)
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101
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102 required_columns <- c("sampleName", "class", "sampleType", "injectionOrder")
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103 optional_columns <- c("batch")
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104
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105 data <- verify_input_dataframe(data, required_columns)
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106
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107 data <- sort_by_injection_order(data)
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108
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109 feature_columns <- colnames(data)[!colnames(data) %in% c(required_columns, optional_columns)]
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110 features <- data[, feature_columns]
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111 injection_order <- data$injectionOrder
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112
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113 # run WaveICA
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114 features <- recetox.waveica::waveica_nonbatchwise(
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115 data = features,
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116 wf = get_wf(wavelet_filter, wavelet_length),
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117 injection_order = injection_order,
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118 K = k,
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119 alpha = alpha,
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120 cutoff = cutoff
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121 )
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122
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123 data[, feature_columns] <- features
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124 group <- enumerate_groups(as.character(data$sampleType))
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125 # remove blanks from dataset
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126 if (exclude_blanks) {
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127 data <- exclude_group(data, group)
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128 }
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129
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130 return(data)
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131 }
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132
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133 sort_by_injection_order <- function(data) {
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134 if ("batch" %in% colnames(data)) {
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135 data <- data[order(data[, "batch"], data[, "injectionOrder"], decreasing = FALSE), ]
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136 } else {
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137 data <- data[order(data[, "injectionOrder"], decreasing = FALSE), ]
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138 }
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139 return(data)
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140 }
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141
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142 verify_input_dataframe <- function(data, required_columns) {
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143 if (anyNA(data)) {
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144 stop("Error: dataframe cannot contain NULL values!
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145 Make sure that your dataframe does not contain empty cells")
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146 } else if (!all(required_columns %in% colnames(data))) {
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147 stop(
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148 "Error: missing metadata!
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149 Make sure that the following columns are present in your dataframe: ",
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150 paste(required_columns, collapse = ", ")
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151 )
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152 }
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153
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154 data <- verify_column_types(data, required_columns)
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155
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156 return(data)
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157 }
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158
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159 verify_column_types <- function(data, required_columns) {
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160 # Specify the column names and their expected types
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161 column_types <- list(
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162 "sampleName" = c("character", "factor"),
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163 "class" = c("character", "factor", "integer"),
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164 "sampleType" = c("character", "factor"),
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165 "injectionOrder" = "integer",
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166 "batch" = "integer"
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167 )
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168
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169 column_types <- column_types[required_columns]
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170
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171 for (col_name in names(data)) {
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172 actual_type <- class(data[[col_name]])
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173 if (col_name %in% names(column_types)) {
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174 expected_types <- column_types[[col_name]]
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175
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176 if (!actual_type %in% expected_types) {
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177 stop(
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178 "Column ", col_name, " is of type ", actual_type,
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179 " but expected type is ",
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180 paste(expected_types, collapse = " or "), "\n"
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181 )
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182 }
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183 } else {
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184 if (actual_type != "numeric") {
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185 data[[col_name]] <- as.numeric(as.character(data[[col_name]]))
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186 }
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187 }
2
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188 }
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189 return(data)
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190 }
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191
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192 # Match group labels with [blank/sample/qc] and enumerate them
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193 enumerate_groups <- function(group) {
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194 group[grepl("blank", tolower(group))] <- 0
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195 group[grepl("sample", tolower(group))] <- 1
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196 group[grepl("qc", tolower(group))] <- 2
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197 group[grepl("standard", tolower(group))] <- 3
0
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198
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199 return(group)
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200 }
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201
328710890963 "planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/waveica commit 98e541a75678eee749261630610b946c258fd5f3"
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202 # Create appropriate input for R wavelets function
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203 get_wf <- function(wavelet_filter, wavelet_length) {
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204 wf <- paste(wavelet_filter, wavelet_length, sep = "")
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205
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206 # exception to the wavelet function
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207 if (wf == "d2") {
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208 wf <- "haar"
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209 }
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210
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211 return(wf)
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212 }
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213
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214 # Exclude blanks from a dataframe
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215 exclude_group <- function(data, group) {
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216 row_idx_to_exclude <- which(group %in% 0)
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217 if (length(row_idx_to_exclude) > 0) {
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218 data_without_blanks <- data[-c(row_idx_to_exclude), ]
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219 cat("Blank samples have been excluded from the dataframe.\n")
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220 return(data_without_blanks)
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221 } else {
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222 return(data)
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223 }
0
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224 }
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225
7
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226 store_data <- function(data, feature_output, metadata_output, ext, split_output = FALSE) {
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227 if (ext == "parquet") {
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228 if (split_output == TRUE) {
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229 split_df <- split_output(data)
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230 arrow::write_parquet(split_df$metadata, metadata_output)
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231 arrow::write_parquet(split_df$feature_table, feature_output)
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232 } else {
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233 arrow::write_parquet(data, feature_output)
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234 }
6
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235 } else {
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236 if (split_output == TRUE) {
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237 split_df <- split_output(data)
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238 write.table(split_df$metadata,
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239 file = metadata_output, sep = "\t",
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240 row.names = FALSE, quote = FALSE
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241 )
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242 write.table(split_df$feature_table,
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243 file = feature_output, sep = "\t",
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244 row.names = FALSE, quote = FALSE
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245 )
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246 } else {
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247 write.table(data,
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248 file = feature_output, sep = "\t",
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249 row.names = FALSE, quote = FALSE
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250 )
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251 }
6
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252 }
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253 cat("Normalization has been completed.\n")
0
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254 }
7
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255
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256 split_output <- function(df) {
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257 required_columns_set1 <- c("sampleName", "class", "sampleType", "injectionOrder", "batch")
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258 required_columns_set2 <- c("sampleName", "class", "sampleType", "injectionOrder")
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259
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260 if (all(required_columns_set1 %in% colnames(df))) {
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261 metadata_df <- df[, required_columns_set1, drop = FALSE]
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262 df <- df[, -c(2:5)]
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263 } else if (all(required_columns_set2 %in% colnames(df))) {
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264 metadata_df <- df[, required_columns_set2, drop = FALSE]
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265 df <- df[, -c(2:4)]
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266 } else {
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267 stop("Neither set of required columns is present in the dataframe.")
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268 }
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269
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270 # Transpose the feature table
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271 col_names <- c("id", as.vector(df[[1]]))
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272 feature_table <- tranpose_data(df, col_names)
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273
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274 return(list(metadata = metadata_df, feature_table = feature_table))
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275 }
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276
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277 tranpose_data <- function(data, column_names) {
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278 t_data <- data[-1]
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279 t_data <- t(t_data)
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280 tranposed_data <- data.frame(rownames(t_data), t_data)
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281 colnames(tranposed_data) <- column_names
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282
1a2aeb8137bf planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/waveica commit 013d7c85fa9d77b8a27d194b350cd6b2d127a80f
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283 return(tranposed_data)
1a2aeb8137bf planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/waveica commit 013d7c85fa9d77b8a27d194b350cd6b2d127a80f
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284 }