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annotate w4mclassfilter_wrapper.R @ 14:87ec0d3c2266 draft

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"planemo upload for repository https://github.com/HegemanLab/w4mclassfilter_galaxy_wrapper/tree/master commit a9664e9a04e49d436ebbb643ba1755397ab759dc"
author eschen42
date Fri, 03 Jan 2020 11:07:39 -0500
parents c18040b6e8b9
children
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1 #!/usr/bin/env Rscript
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2
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3 library(batch) ## parseCommandArgs
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4
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5 ########
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6 # MAIN #
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7 ########
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8
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9 argVc <- unlist(parseCommandArgs(evaluate=FALSE))
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10
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11 ##------------------------------
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12 ## Initializing
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13 ##------------------------------
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14
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15 ## options
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16 ##--------
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17
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18 strAsFacL <- options()$stringsAsFactors
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19 options(stringsAsFactors = FALSE)
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20
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21 ## libraries
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22 ##----------
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23
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24 suppressMessages(library(w4mclassfilter))
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25
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26 expected_version <- "0.98.18"
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27 actual_version <- packageVersion("w4mclassfilter")
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28 if(packageVersion("w4mclassfilter") < expected_version) {
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29 stop(
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30 sprintf(
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31 "Unrecoverable error: Version %s of the 'w4mclassfilter' R package was loaded instead of expected version %s",
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32 actual_version, expected_version
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33 )
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34 )
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35 }
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36
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37 ## constants
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38 ##----------
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39
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40 modNamC <- "w4mclassfilter" ## module name
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41
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42 topEnvC <- environment()
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43 flgC <- "\n"
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44
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45 ## functions
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46 ##----------
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47
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48 flgF <- function(tesC,
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49 envC = topEnvC,
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50 txtC = NA) { ## management of warning and error messages
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51
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52 tesL <- eval(parse(text = tesC), envir = envC)
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53
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54 if(!tesL) {
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55
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56 #sink(NULL)
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57 stpTxtC <- ifelse(is.na(txtC),
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58 paste0(tesC, " is FALSE"),
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59 txtC)
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60
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61 stop(stpTxtC,
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62 call. = FALSE)
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63
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64 }
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65
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66 } ## flgF
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67
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68
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69 ## log file
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70 ##---------
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71
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72 my_print <- function(x, ...) { cat(c(x, ...))}
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73
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74 my_print("\nStart of the '", modNamC, "' Galaxy module call: ",
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75 format(Sys.time(), "%a %d %b %Y %X"), "\n", sep="")
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76
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77 ## arguments
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78 ##----------
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79
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80 # files
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81
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82 dataMatrix_in <- as.character(argVc["dataMatrix_in"])
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83 dataMatrix_out <- as.character(argVc["dataMatrix_out"])
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84
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85 sampleMetadata_in <- as.character(argVc["sampleMetadata_in"])
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86 sampleMetadata_out <- as.character(argVc["sampleMetadata_out"])
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87
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88 variableMetadata_in <- as.character(argVc["variableMetadata_in"])
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89 variableMetadata_out <- as.character(argVc["variableMetadata_out"])
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90
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91 # other parameters
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92
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93 transformation <- as.character(argVc["transformation"])
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94 my_imputation_label <- as.character(argVc["imputation"])
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95 my_imputation_function <- if (my_imputation_label == "zero") {
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96 w4m_filter_zero_imputation
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97 } else if (my_imputation_label == "center") {
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98 w4m_filter_median_imputation
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99 } else if (my_imputation_label == "none") {
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100 w4m_filter_no_imputation
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101 } else {
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102 stop(sprintf("Unknown value %s supplied for 'imputation' parameter. Expected one of {zero,center,none}."))
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103 }
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104 wildcards <- as.logical(argVc["wildcards"])
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105 sampleclassNames <- as.character(argVc["sampleclassNames"])
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106 sampleclassNames <- strsplit(x = sampleclassNames, split = ",", fixed = TRUE)[[1]]
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107 if (wildcards) {
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108 sampleclassNames <- gsub("[.]", "[.]", sampleclassNames)
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109 sampleclassNames <- utils::glob2rx(sampleclassNames, trim.tail = FALSE)
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110 }
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111 inclusive <- as.logical(argVc["inclusive"])
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112 classnameColumn <- as.character(argVc["classnameColumn"])
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113 samplenameColumn <- as.character(argVc["samplenameColumn"])
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114
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115 order_vrbl <- as.character(argVc["order_vrbl"])
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116 centering <- as.character(argVc["centering"])
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117 order_smpl <-
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118 if (centering == 'centroid' || centering == 'median') {
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119 "sampleMetadata"
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120 } else {
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121 as.character(argVc["order_smpl"])
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122 }
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123
6
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124 variable_range_filter <- as.character(argVc["variable_range_filter"])
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125 variable_range_filter <- strsplit(x = variable_range_filter, split = ",", fixed = TRUE)[[1]]
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126
8
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127 ## -----------------------------
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128 ## Transformation and imputation
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129 ## -----------------------------
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130 my_transformation_and_imputation <- if (transformation == "log10") {
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131 function(m) {
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132 # convert negative intensities to missing values
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133 m[m < 0] <- NA
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134 if (!is.matrix(m))
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135 stop("Cannot transform and impute data - the supplied data is not in matrix form")
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136 if (nrow(m) == 0)
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137 stop("Cannot transform and impute data - data matrix has no rows")
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138 if (ncol(m) == 0)
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139 stop("Cannot transform and impute data - data matrix has no columns")
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140 suppressWarnings({
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141 # suppress warnings here since non-positive values will produce NaN's that will be fixed in the next step
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142 m <- log10(m)
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143 m[is.na(m)] <- NA
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144 })
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145 return ( my_imputation_function(m) )
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146 }
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147 } else if (transformation == "log2") {
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148 function(m) {
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149 # convert negative intensities to missing values
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150 m[m < 0] <- NA
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151 if (!is.matrix(m))
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152 stop("Cannot transform and impute data - the supplied data is not in matrix form")
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153 if (nrow(m) == 0)
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154 stop("Cannot transform and impute data - data matrix has no rows")
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155 if (ncol(m) == 0)
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156 stop("Cannot transform and impute data - data matrix has no columns")
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157 suppressWarnings({
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158 # suppress warnings here since non-positive values will produce NaN's that will be fixed in the next step
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159 m <- log2(m)
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160 m[is.na(m)] <- NA
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161 })
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162 return ( my_imputation_function(m) )
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163 }
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164 } else {
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165 function(m) {
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166 # convert negative intensities to missing values
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167 m[m < 0] <- NA
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168 if (!is.matrix(m))
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169 stop("Cannot transform and impute data - the supplied data is not in matrix form")
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170 if (nrow(m) == 0)
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171 stop("Cannot transform and impute data - data matrix has no rows")
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172 if (ncol(m) == 0)
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173 stop("Cannot transform and impute data - data matrix has no columns")
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174 suppressWarnings({
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175 # suppress warnings here since non-positive values will produce NaN's that will be fixed in the next step
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176 m[is.na(m)] <- NA
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177 })
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178 return ( my_imputation_function(m) )
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179 }
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180 }
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181
0
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182 ##------------------------------
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183 ## Computation
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184 ##------------------------------
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185
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186 result <- w4m_filter_by_sample_class(
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187 dataMatrix_in = dataMatrix_in
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188 , sampleMetadata_in = sampleMetadata_in
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189 , variableMetadata_in = variableMetadata_in
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190 , dataMatrix_out = dataMatrix_out
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191 , sampleMetadata_out = sampleMetadata_out
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192 , variableMetadata_out = variableMetadata_out
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193 , classes = sampleclassNames
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194 , include = inclusive
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195 , class_column = classnameColumn
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196 , samplename_column = samplenameColumn
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197 , order_vrbl = order_vrbl
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198 , order_smpl = order_smpl
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199 , centering = centering
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200 , variable_range_filter = variable_range_filter
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201 , failure_action = my_print
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202 , data_imputation = my_transformation_and_imputation
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203 )
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204
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205 my_print("\nResult of '", modNamC, "' Galaxy module call to 'w4mclassfilter::w4m_filter_by_sample_class' R function: ",
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206 as.character(result), "\n", sep = "")
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207
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208 ##--------
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209 ## Closing
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210 ##--------
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211
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212 my_print("\nEnd of '", modNamC, "' Galaxy module call: ",
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213 as.character(Sys.time()), "\n", sep = "")
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214
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215 #sink()
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216
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217 if (!file.exists(dataMatrix_out)) {
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218 print(sprintf("ERROR %s::w4m_filter_by_sample_class - file '%s' was not created", modNamC, dataMatrix_out))
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219 }# else { print(sprintf("INFO %s::w4m_filter_by_sample_class - file '%s' was exists", modNamC, dataMatrix_out)) }
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220
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221 if (!file.exists(variableMetadata_out)) {
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222 print(sprintf("ERROR %s::w4m_filter_by_sample_class - file '%s' was not created", modNamC, variableMetadata_out))
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223 } # else { print(sprintf("INFO %s::w4m_filter_by_sample_class - file '%s' was exists", modNamC, variableMetadata_out)) }
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224
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225 if (!file.exists(sampleMetadata_out)) {
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226 print(sprintf("ERROR %s::w4m_filter_by_sample_class - file '%s' was not created", modNamC, sampleMetadata_out))
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227 } # else { print(sprintf("INFO %s::w4m_filter_by_sample_class - file '%s' was exists", modNamC, sampleMetadata_out)) }
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228
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229 if( !result ) {
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230 stop(sprintf("ERROR %s::w4m_filter_by_sample_class - method failed", modNamC))
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231 }
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232
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233 rm(list = ls())