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planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 2799299a221358b648334c3f890c4024155af73b
author eschen42
date Fri, 02 Mar 2018 08:32:17 -0500
parents 330ee1d840db
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6
3f72a635a075 planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit 2799299a221358b648334c3f890c4024155af73b
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1 ##-----------------------------------------------
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2 ## helper functions for error detection/reporting
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3 ##-----------------------------------------------
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4
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5 # ISO 8601 date ref: https://en.wikipedia.org/wiki/ISO_8601
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6 iso_date <- function() {
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7 format(Sys.time(), "%Y-%m-%dT%H:%M:%S%z")
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8 }
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9
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10 # log-printing to stderr
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11 log_print <- function(x, ...) {
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12 cat(
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13 sep=""
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14 , file=stderr()
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15 , iso_date()
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16 , " "
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17 , c(x, ...)
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18 , "\n"
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19 )
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20 }
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21
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22 # format error for logging
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23 format_error <- function(e) {
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24 paste(c("Error { message:", e$message, ", call:", e$call, "}"), collapse = " ")
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25 }
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26
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27 # tryCatchFunc produces a list
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28 # func - a function that takes no arguments
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29 # On success of func(), tryCatchFunc produces
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30 # list(success = TRUE, value = func(), msg = "")
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31 # On failure of func(), tryCatchFunc produces
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32 # list(success = FALSE, value = NA, msg = "the error message")
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33 tryCatchFunc <- function(func) {
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34 retval <- NULL
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35 tryCatch(
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36 expr = {
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37 retval <- ( list( success = TRUE, value = func(), msg = "" ) )
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38 }
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39 , error = function(e) {
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40 retval <<- list( success = FALSE, value = NA, msg = format_error(e) )
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41 }
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42 )
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43 return (retval)
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44 }
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45
0
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46 # prepare.data.matrix - Prepare x.datamatrix for multivariate statistical analaysis (MVA)
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47 # - Motivation:
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48 # - Selection:
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49 # - You may want to exclude several samples from your analysis:
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50 # - If so, set the argument 'exclude.samples' to a vector of sample names
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51 # - You may want to exclude several features or features from your analysis:
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52 # - If so, set the argument 'exclude.features' to a vector of feature names
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53 # - Renaming samples:
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54 # - You may want to rename several samples from your analysis:
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55 # - If so, set the argument 'sample.rename.function' to a function accepting a vector
0
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56 # of sample names and producing a vector of strings of equivalent length
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57 # - MVA is confounded by missing values.
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58 # - By default, this function imputes missing values as zero.
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59 # - For a different imputation, set the 'data.imputation' argument to a function
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60 # accepting a single matrix argument and returning a matrix of the same
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61 # dimensions as the argument.
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62 # - Transformation
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63 # - It may be desirable to transform the intensity data to reduce the range.
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64 # - By default, this function performs an eigth-root transformation:
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65 # - Any root-tranformation has the advantage of never being negative.
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66 # - Calculation of the eight-root is four times faster in my hands than log10.
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67 # - However, it has the disadvantage that calculation of fold-differences
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68 # is not additive as with log-transformation.
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69 # - Rather, you must divide the values and raise to the eighth power.
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70 # - For a different transformation, set the 'data.transformation' argument
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71 # to a function accepting a single matrix argument.
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72 # - The function should be written to return a matrix of the same dimensions
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73 # as the argument.
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74 # arguments:
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75 # - x.matrix - matrix of intensities (or data.frame of sample metadata)
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76 # - one row per sample
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77 # - one column per feature or metadata attribute
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78 # - exclude.samples - vector of labels of matrix rows (samples) to omit from analysis
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79 # - exclude.features - vector of labels of matrix columnss (features) to omit from analysis
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80 # - sample.rename.function - function to be used to rename rows if necessary, or NULL
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81 # - e.g., sample.rename.function = function(x) {
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82 # sub("(.*)_.*","\\1", row.names(x))
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83 # }
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84 # - data.imputation - function applied to matrix to impute missing values
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85 # - e.g., data.imputation = function(m) {
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86 # m[is.na(m)] <- min(m, na.rm = TRUE) / 100
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87 # return (m)
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88 # }
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89 # - data.transformation - function applied to matrix cells
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90 # - e.g., data.transformation = function(x) { return( log10(x) ) }
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91 # or, data.transformation = log10
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92 # result value:
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93 # transformed, imputed x.datamatrix with renamed rows and with neither excluded values nor features
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94 #
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95 ################################
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96 ##
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97 ## Notes regarding the effectiveness and performance of the data transformation method.
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98 ##
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99 ## The two transformations that I tried (log10 and 8th root) required different imputation methods.
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100 ##
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101 ## For the LCMS resin data set that I was working with, separation in MVA was nearly equivalent for:
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102 ## data.imputation <- function(x.matrix) {
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103 ## x.matrix[is.na(x.matrix)] <- 0
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104 ## return (x.matrix)
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105 ## }
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106 ## data.transformation <- function(x) {
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107 ## sqrt( sqrt( sqrt(x) ) )
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108 ## }
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109 ## and
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110 ## data.imputation <- function(x.matrix) {
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111 ## x.matrix[is.na(x.matrix)] <- min(x.matrix, na.rm = TRUE) / 100
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112 ## return (x.matrix)
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113 ## }
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114 ## data.transformation <- function(x) {
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115 ## log10(x)
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116 ## }
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117 ##
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118 ## Note further that triple application of the square root:
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119 ## - may be four times faster than log10:
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120 ## - may be three times faster than log2:
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121 ##
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122 ## system.time( junk <- sqrt( sqrt( sqrt(1:100000000) ) ) )
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123 ## user system elapsed
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124 ## 0.832 0.236 1.069
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125 ## system.time( junk <- log10(1:100000000) )
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126 ## user system elapsed
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127 ## 3.936 0.400 4.337
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128 ## system.time( junk <- log2(1:100000000) )
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129 ## user system elapsed
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130 ## 2.784 0.320 3.101
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131 ##
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132 ################################
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133 #
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134 prepare.data.matrix <- function(
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135 x.matrix
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136 , exclude.samples = NULL
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137 , exclude.features = NULL
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138 , sample.rename.function = NULL
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139 , data.imputation =
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140 function(m) {
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141 # replace NA values with zero
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142 m[is.na(m)] <- 0
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143 # replace negative values with zero, if applicable (It should never be applicable!)
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144 if (min(m < 0)) {
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145 m <- matrix(lapply(X = m, FUN = function(z) {max(z,0)}), nrow = nrow(m) )
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146 }
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147 # return matrix as the result
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148 return (m)
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149 }
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150 , data.transformation = function(x) {
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151 sqrt( sqrt( sqrt(x) ) )
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152 }
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153 , en = new.env()
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154 ) {
6
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155 # log to environment
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156 if ( !exists("log", envir = en) ) {
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157 en$log <- c()
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158 }
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159 enlog <- function(s) { en$log <- c(en$log, s); s }
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160 #enlog("foo")
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161
0
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162 # MatVar - Compute variance of rows or columns of a matrix
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163 # ref: http://stackoverflow.com/a/25100036
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164 # For row variance, dim == 1, for col variance, dim == 2
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165 MatVar <- function(x, dim = 1) {
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166 if (dim == 1) {
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167 dim.x.2 <- dim(x)[2]
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168 if ( dim.x.2 == 0 )
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169 stop("MatVar: there are zero columns")
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170 if ( dim.x.2 == 1 ) {
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171 stop("MatVar: a single column is insufficient to calculate a variance")
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172 # return ( rep.int(x = 0, times = nrow(x)) )
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173 } else {
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174 return ( rowSums( (x - rowMeans(x))^2 ) / ( dim(x)[2] - 1 ) )
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175 }
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176 } else if (dim == 2) {
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177 dim.x.1 <- dim(x)[1]
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178 if ( dim.x.1 == 0 ) {
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179 stop("MatVar: there are zero rows")
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180 }
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181 if ( dim.x.1 == 1 ) {
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182 stop("MatVar: a single row is insufficient to calculate a variance")
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183 # return ( rep.int(x = 0, times = ncol(x)) )
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184 } else {
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185 return ( rowSums( (t(x) - colMeans(x))^2 ) / ( dim(x)[1] - 1 ) )
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186 }
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187 } else stop("Please enter valid dimension, for rows, dim = 1; for colums, dim = 2")
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188 }
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189
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190 nonzero.var <- function(x) {
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191 if (nrow(x) == 0) {
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192 stop("matrix has no rows")
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193 }
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194 if (ncol(x) == 0) {
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195 stop("matrix has no columns")
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196 }
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197 if ( is.numeric(x) ) {
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198 # exclude any rows with zero variance
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199 row.vars <- MatVar(x, dim = 1)
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200 nonzero.row.vars <- row.vars > 0
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201 nonzero.rows <- row.vars[nonzero.row.vars]
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202 if ( length(rownames(x)) != length(rownames(nonzero.rows)) ) {
330ee1d840db planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit ae8ee04dc44eedf9121644ce8577c622da2a5f8c-dirty
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203 row.names <- attr(nonzero.rows,"names")
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204 x <- x[ row.names, , drop = FALSE ]
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205 }
6
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206
0
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207 # exclude any columns with zero variance
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208 column.vars <- MatVar(x, dim = 2)
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209 nonzero.column.vars <- column.vars > 0
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210 nonzero.columns <- column.vars[nonzero.column.vars]
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211 if ( length(colnames(x)) != length(colnames(nonzero.columns)) ) {
330ee1d840db planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit ae8ee04dc44eedf9121644ce8577c622da2a5f8c-dirty
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212 column.names <- attr(nonzero.columns,"names")
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213 x <- x[ , column.names, drop = FALSE ]
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214 }
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215 }
330ee1d840db planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit ae8ee04dc44eedf9121644ce8577c622da2a5f8c-dirty
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216 return (x)
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217 }
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218
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219 if (is.null(x.matrix)) {
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220 stop("FATAL ERROR - prepare.data.matrix was called with null x.matrix")
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221 }
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222
6
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223 enlog("prepare.data.matrix - get matrix")
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224
0
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225 en$xpre <- x <- x.matrix
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226
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227 # exclude any samples as indicated
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228 if ( !is.null(exclude.features) ) {
6
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229 enlog("prepare.data.matrix - exclude any samples as indicated")
0
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230 my.colnames <- colnames(x)
330ee1d840db planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit ae8ee04dc44eedf9121644ce8577c622da2a5f8c-dirty
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231 my.col.diff <- setdiff(my.colnames, exclude.features)
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232 x <- x[ , my.col.diff , drop = FALSE ]
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233 }
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234
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diff changeset
235 # exclude any features as indicated
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236 if ( !is.null(exclude.samples) ) {
6
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237 enlog("prepare.data.matrix - exclude any features as indicated")
0
330ee1d840db planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit ae8ee04dc44eedf9121644ce8577c622da2a5f8c-dirty
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238 my.rownames <- rownames(x)
330ee1d840db planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit ae8ee04dc44eedf9121644ce8577c622da2a5f8c-dirty
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239 my.row.diff <- setdiff(my.rownames, exclude.samples)
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240 x <- x[ my.row.diff, , drop = FALSE ]
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241 }
330ee1d840db planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit ae8ee04dc44eedf9121644ce8577c622da2a5f8c-dirty
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242
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243 # rename rows if desired
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244 if ( !is.null(sample.rename.function) ) {
6
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245 enlog("prepare.data.matrix - rename rows if desired")
0
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246 renamed <- sample.rename.function(x)
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247 rownames(x) <- renamed
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248 }
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249
6
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250 enlog("prepare.data.matrix - save redacted x.datamatrix to environment")
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251
0
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252 # save redacted x.datamatrix to environment
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253 en$redacted.data.matrix <- x
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254
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255 # impute values missing from the x.datamatrix
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256 if ( !is.null(data.imputation) ) {
6
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257 enlog("prepare.data.matrix - impute values missing from the x.datamatrix")
0
330ee1d840db planemo upload for repository https://github.com/HegemanLab/w4mkmeans_galaxy_wrapper/tree/master commit ae8ee04dc44eedf9121644ce8577c622da2a5f8c-dirty
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258 x <- data.imputation(x)
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259 }
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260
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parents:
diff changeset
261 # perform transformation if desired
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262 if ( !is.null(data.transformation) ) {
6
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263 enlog("prepare.data.matrix - perform transformation")
0
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264 x <- data.transformation(x)
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265 } else {
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266 x <- x
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267 }
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268
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269 # purge rows and columns that have zero variance
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270 if ( is.numeric(x) ) {
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271 enlog("prepare.data.matrix - purge rows and columns that have zero variance")
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272 x <- nonzero.var(x)
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273 }
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274
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275 # save imputed, transformed x.datamatrix to environment
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276 en$imputed.transformed.data.matrix <- x
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277
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278 return(x)
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279 }
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280
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281 # vim: sw=2 ts=2 et :