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annotate maaslin-4450aa4ecc84/src/lib/BoostGLM.R @ 1:a87d5a5f2776

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Uploaded the version running on the prod server
author george-weingart
date Sun, 08 Feb 2015 23:08:38 -0500
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1 #####################################################################################
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2 #Copyright (C) <2012>
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3 #
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4 #Permission is hereby granted, free of charge, to any person obtaining a copy of
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5 #this software and associated documentation files (the "Software"), to deal in the
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6 #Software without restriction, including without limitation the rights to use, copy,
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7 #modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
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8 #and to permit persons to whom the Software is furnished to do so, subject to
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9 #the following conditions:
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10 #
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11 #The above copyright notice and this permission notice shall be included in all copies
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12 #or substantial portions of the Software.
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13 #
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14 #THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
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15 #INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
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16 #PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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17 #HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
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18 #OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
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19 #SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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20 #
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21 # This file is a component of the MaAsLin (Multivariate Associations Using Linear Models),
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22 # authored by the Huttenhower lab at the Harvard School of Public Health
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23 # (contact Timothy Tickle, ttickle@hsph.harvard.edu).
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24 #####################################################################################
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25
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26 inlinedocs <- function(
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27 ##author<< Curtis Huttenhower <chuttenh@hsph.harvard.edu> and Timothy Tickle <ttickle@hsph.harvard.edu>
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28 ##description<< Manages the quality control of data and the performance of analysis (univariate or multivariate), regularization, and data (response) transformation.
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29 ) { return( pArgs ) }
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30
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31 ### Load libraries quietly
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32 suppressMessages(library( gam, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE))
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33 suppressMessages(library( gbm, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE))
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34 suppressMessages(library( logging, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE))
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35 suppressMessages(library( outliers, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE))
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36 suppressMessages(library( robustbase, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE))
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37 suppressMessages(library( pscl, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE))
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38
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39 ### Get constants
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40 #source(file.path("input","maaslin","src","Constants.R"))
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41 #source("Constants.R")
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42
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43 ## Get logger
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44 c_logrMaaslin <- getLogger( "maaslin" )
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45
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46 funcDoGrubbs <- function(
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47 ### Use the Grubbs Test to identify outliers
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48 iData,
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49 ### Column index in the data frame to test
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50 frmeData,
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51 ### The data frame holding the data
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52 dPOutlier,
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53 ### P-value threshold to indicate an outlier is significant
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54 lsQC
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55 ### List holding the QC info of the cleaning step. Which indices are outliers is added.
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56 ){
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57 adData <- frmeData[,iData]
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58
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59 # Original number of NA
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60 viNAOrig = which(is.na(adData))
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61
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62 while( TRUE )
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63 {
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64 lsTest <- try( grubbs.test( adData ), silent = TRUE )
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65 if( ( class( lsTest ) == "try-error" ) || is.na( lsTest$p.value ) || ( lsTest$p.value > dPOutlier ) )
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66 {break}
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67 viOutliers = outlier( adData, logical = TRUE )
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68 adData[viOutliers] <- NA
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69 }
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70
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71 # Record removed data
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72 viNAAfter = which(is.na(adData))
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73
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74 # If all were set to NA then ignore the filtering
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75 if(length(adData)==length(viNAAfter))
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76 {
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77 viNAAfter = viNAOrig
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78 adData = frmeData[,iData]
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79 c_logrMaaslin$info( paste("Grubbs Test:: Identifed all data as outliers so was inactived for index=",iData," data=",paste(as.vector(frmeData[,iData]),collapse=","), "number zeros=", length(which(frmeData[,iData]==0)), sep = " " ))
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80 } else if(mean(adData, na.rm=TRUE) == 0) {
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81 viNAAfter = viNAOrig
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82 adData = frmeData[,iData]
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83 c_logrMaaslin$info( paste("Grubbs Test::Removed all values but 0, ignored. Index=",iData,".",sep=" " ) )
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84 } else {
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85 # Document removal
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86 if( sum( is.na( adData ) ) )
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87 {
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88 c_logrMaaslin$info( "Grubbs Test::Removing %d outliers from %s", sum( is.na( adData ) ), colnames(frmeData)[iData] )
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89 c_logrMaaslin$info( format( rownames( frmeData )[is.na( adData )] ))
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90 }
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91 }
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92
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93 return(list(data=adData,outliers=length(viNAAfter)-length(viNAOrig),indices=setdiff(viNAAfter,viNAOrig)))
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94 }
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95
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96 funcDoFenceTest <- function(
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97 ### Use a threshold based on the quartiles of the data to identify outliers
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98 iData,
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99 ### Column index in the data frame to test
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100 frmeData,
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101 ### The data frame holding the data
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102 dFence
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103 ### The fence outside the first and third quartiles to use as a threshold for cutt off.
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104 ### This many times the interquartile range +/- to the 3rd/1st quartiles
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105 ){
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106 # Establish fence
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107 adData <- frmeData[,iData]
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108 adQ <- quantile( adData, c(0.25, 0.5, 0.75), na.rm = TRUE )
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109
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110 dIQR <- adQ[3] - adQ[1]
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111 if(!dIQR)
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112 {
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113 dIQR = sd(adData,na.rm = TRUE)
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114 }
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115 dUF <- adQ[3] + ( dFence * dIQR )
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116 dLF <- adQ[1] - ( dFence * dIQR )
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117
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118 # Record indices of values outside of fence to remove and remove.
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119 aiRemove <- c()
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120 for( j in 1:length( adData ) )
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121 {
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122 d <- adData[j]
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123 if( !is.na( d ) && ( ( d < dLF ) || ( d > dUF ) ) )
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124 {
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125 aiRemove <- c(aiRemove, j)
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126 }
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127 }
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128
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129 if(length(aiRemove)==length(adData))
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130 {
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131 aiRemove = c()
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132 c_logrMaaslin$info( "OutliersByFence:: Identified all data as outlier so was inactivated for index=", iData,"data=", paste(as.vector(frmeData[,iData]),collapse=","), "number zeros=", length(which(frmeData[,iData]==0)), sep=" " )
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133 } else {
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134 adData[aiRemove] <- NA
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135
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136 # Document to screen
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137 if( length( aiRemove ) )
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138 {
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139 c_logrMaaslin$info( "OutliersByFence::Removing %d outliers from %s", length( aiRemove ), colnames(frmeData)[iData] )
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140 c_logrMaaslin$info( format( rownames( frmeData )[aiRemove] ))
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141 }
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142 }
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143
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144 return(list(data=adData,outliers=length(aiRemove),indices=aiRemove))
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145 }
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146
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147 funcZerosAreUneven = function(
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148 ###
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149 vdRawData,
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150 ### Raw data to be checked during transformation
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151 funcTransform,
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152 ### Data transform to perform
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153 vsStratificationFeatures,
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154 ### Groupings to check for unevenness
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155 dfData
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156 ### Data frame holding the features
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157 ){
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158 # Return indicator of unevenness
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159 fUneven = FALSE
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160
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161 # Transform the data to compare
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162 vdTransformed = funcTransform( vdRawData )
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163
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164 # Go through each stratification of data
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165 for( sStratification in vsStratificationFeatures )
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166 {
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167 # Current stratification
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168 vFactorStrats = dfData[[ sStratification ]]
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169
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170 # If the metadata is not a factor then skip
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171 # Only binned data can be evaluated this way.
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172 if( !is.factor( vFactorStrats )){ next }
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173
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174 viZerosCountsRaw = c()
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175 for( sLevel in levels( vFactorStrats ) )
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176 {
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177 vdTest = vdRawData[ which( vFactorStrats == sLevel ) ]
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178 viZerosCountsRaw = c( viZerosCountsRaw, length(which(vdTest == 0)))
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179 vdTest = vdTransformed[ which( vFactorStrats == sLevel ) ]
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180 }
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181 dExpectation = 1 / length( viZerosCountsRaw )
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182 dMin = dExpectation / 2
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183 dMax = dExpectation + dMin
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184 viZerosCountsRaw = viZerosCountsRaw / sum( viZerosCountsRaw )
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185 if( ( length( which( viZerosCountsRaw <= dMin ) ) > 0 ) || ( length( which( viZerosCountsRaw >= dMax ) ) > 0 ) )
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186 {
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187 return( TRUE )
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188 }
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189 }
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190 return( fUneven )
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191 }
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192
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193 funcTransformIncreasesOutliers = function(
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194 ### Checks if a data transform increases outliers in a distribution
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195 vdRawData,
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196 ### Raw data to check for outlier zeros
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197 funcTransform
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198 ){
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199 iUnOutliers = length( boxplot( vdRawData, plot = FALSE )$out )
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200 iTransformedOutliers = length( boxplot( funcTransform( vdRawData ), plot = FALSE )$out )
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201
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202 return( iUnOutliers <= iTransformedOutliers )
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203 }
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204
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205 funcClean <- function(
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206 ### Properly clean / get data ready for analysis
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207 ### Includes custom analysis from the custom R script if it exists
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208 frmeData,
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209 ### Data frame, input data to be acted on
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210 funcDataProcess,
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211 ### Custom script that can be given to perform specialized processing before MaAsLin does.
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212 aiMetadata,
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213 ### Indices of columns in frmeData which are metadata for analysis.
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214 aiData,
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215 ### Indices of column in frmeData which are (abundance) data for analysis.
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216 lsQCCounts,
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217 ### List that will hold the quality control information which is written in the output directory.
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218 astrNoImpute = c(),
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219 ### An array of column names of frmeData not to impute.
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220 dMinSamp,
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221 ### Minimum number of samples
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222 dMinAbd,
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223 # Minimum sample abundance
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224 dFence,
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225 ### How many quartile ranges defines the fence to define outliers.
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226 funcTransform,
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227 ### The data transformation function or a dummy function that does not affect the data
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228 dPOutlier = 0.05
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229 ### The significance threshold for the grubbs test to identify an outlier.
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230 ){
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231 # Call the custom script and set current data and indicies to the processes data and indicies.
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232 c_logrMaaslin$debug( "Start Clean")
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233 if( !is.null( funcDataProcess ) )
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234 {
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235 c_logrMaaslin$debug("Additional preprocess function attempted.")
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236
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237 pTmp <- funcDataProcess( frmeData=frmeData, aiMetadata=aiMetadata, aiData=aiData)
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238 frmeData = pTmp$frmeData
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239 aiMetadata = pTmp$aiMetadata
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240 aiData = pTmp$aiData
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241 lsQCCounts$lsQCCustom = pTmp$lsQCCounts
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242 }
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243 # Set data indicies after custom QC process.
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244 lsQCCounts$aiAfterPreprocess = aiData
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245
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246 # Remove missing data, remove any sample that has less than dMinSamp * the number of data or low abundance
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247 aiRemove = c()
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248 aiRemoveLowAbundance = c()
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249 for( iCol in aiData )
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250 {
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251 adCol = frmeData[,iCol]
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252 adCol[!is.finite( adCol )] <- NA
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253 if( ( sum( !is.na( adCol ) ) < ( dMinSamp * length( adCol ) ) ) ||
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254 ( length( unique( na.omit( adCol ) ) ) < 2 ) )
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255 {
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256 aiRemove = c(aiRemove, iCol)
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257 }
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258 if( sum(adCol > dMinAbd, na.rm=TRUE ) < (dMinSamp * length( adCol)))
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259 {
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260 aiRemoveLowAbundance = c(aiRemoveLowAbundance, iCol)
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261 }
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262 }
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263 # Remove and document
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264 aiData = setdiff( aiData, aiRemove )
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265 aiData = setdiff( aiData, aiRemoveLowAbundance )
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266 lsQCCounts$iMissingData = aiRemove
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267 lsQCCounts$iLowAbundanceData = aiRemoveLowAbundance
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268 if(length(aiRemove))
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269 {
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270 c_logrMaaslin$info( "Removing the following for data lower bound.")
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271 c_logrMaaslin$info( format( colnames( frmeData )[aiRemove] ))
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272 }
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273 if(length(aiRemoveLowAbundance))
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274 {
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275 c_logrMaaslin$info( "Removing the following for too many low abundance bugs.")
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276 c_logrMaaslin$info( format( colnames( frmeData )[aiRemoveLowAbundance] ))
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277 }
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278
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279 #Transform data
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280 iTransformed = 0
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281 viNotTransformedData = c()
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282 for(aiDatum in aiData)
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283 {
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284 adValues = frmeData[,aiDatum]
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285 # if( ! funcTransformIncreasesOutliers( adValues, funcTransform ) )
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286 # {
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287 frmeData[,aiDatum] = funcTransform( adValues )
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288 # iTransformed = iTransformed + 1
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289 # } else {
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290 # viNotTransformedData = c( viNotTransformedData, aiDatum )
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291 # }
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292 }
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293 c_logrMaaslin$info(paste("Number of features transformed = ",iTransformed))
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294
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295 # Metadata: Properly factorize all logical data and integer and number data with less than iNonFactorLevelThreshold
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296 # Also record which are numeric metadata
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297 aiNumericMetadata = c()
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298 for( i in aiMetadata )
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299 {
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300 if( ( class( frmeData[,i] ) %in% c("integer", "numeric", "logical") ) &&
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301 ( length( unique( frmeData[,i] ) ) < c_iNonFactorLevelThreshold ) ) {
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302 c_logrMaaslin$debug(paste("Changing metadatum from numeric/integer/logical to factor",colnames(frmeData)[i],sep="="))
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303 frmeData[,i] = factor( frmeData[,i] )
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304 }
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305 if( class( frmeData[,i] ) %in% c("integer","numeric") )
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306 {
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307 aiNumericMetadata = c(aiNumericMetadata,i)
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308 }
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309 }
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310
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311 # Remove outliers
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312 # If the dFence Value is set use the method of defining the outllier as
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313 # dFence * the interquartile range + or - the 3rd and first quartile respectively.
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314 # If not the gibbs test is used.
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315 lsQCCounts$aiDataSumOutlierPerDatum = c()
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316 lsQCCounts$aiMetadataSumOutlierPerDatum = c()
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317 lsQCCounts$liOutliers = list()
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318
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319 if( dFence > 0.0 )
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320 {
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321 # For data
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322 for( iData in aiData )
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323 {
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324 lOutlierInfo <- funcDoFenceTest(iData=iData,frmeData=frmeData,dFence=dFence)
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325 frmeData[,iData] <- lOutlierInfo[["data"]]
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326 lsQCCounts$aiDataSumOutlierPerDatum <- c(lsQCCounts$aiDataSumOutlierPerDatum,lOutlierInfo[["outliers"]])
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327 if(lOutlierInfo[["outliers"]]>0)
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328 {
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329 lsQCCounts$liOutliers[[paste(iData,sep="")]] <- lOutlierInfo[["indices"]]
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330 }
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331 }
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332
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333 # Remove outlier non-factor metadata
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334 for( iMetadata in aiNumericMetadata )
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335 {
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336 lOutlierInfo <- funcDoFenceTest(iData=iMetadata,frmeData=frmeData,dFence=dFence)
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337 frmeData[,iMetadata] <- lOutlierInfo[["data"]]
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338 lsQCCounts$aiMetadataSumOutlierPerDatum <- c(lsQCCounts$aiMetadataSumOutlierPerDatum,lOutlierInfo[["outliers"]])
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339 if(lOutlierInfo[["outliers"]]>0)
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340 {
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341 lsQCCounts$liOutliers[[paste(iMetadata,sep="")]] <- lOutlierInfo[["indices"]]
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342 }
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343 }
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344 #Do not use the fence, use the Grubbs test
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345 } else if(dPOutlier!=0.0){
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346 # For data
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347 for( iData in aiData )
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348 {
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349 lOutlierInfo <- funcDoGrubbs(iData=iData,frmeData=frmeData,dPOutlier=dPOutlier)
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350 frmeData[,iData] <- lOutlierInfo[["data"]]
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351 lsQCCounts$aiDataSumOutlierPerDatum <- c(lsQCCounts$aiDataSumOutlierPerDatum,lOutlierInfo[["outliers"]])
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352 if(lOutlierInfo[["outliers"]]>0)
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353 {
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354 lsQCCounts$liOutliers[[paste(iData,sep="")]] <- lOutlierInfo[["indices"]]
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355 }
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356 }
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357 for( iMetadata in aiNumericMetadata )
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358 {
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359 lOutlierInfo <- funcDoGrubbs(iData=iMetadata,frmeData=frmeData,dPOutlier=dPOutlier)
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360 frmeData[,iMetadata] <- lOutlierInfo[["data"]]
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361 lsQCCounts$aiMetadataSumOutlierPerDatum <- c(lsQCCounts$aiMetadataSumOutlierPerDatum,lOutlierInfo[["outliers"]])
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362 if(lOutlierInfo[["outliers"]]>0)
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363 {
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364 lsQCCounts$liOutliers[[paste(iMetadata,sep="")]] <- lOutlierInfo[["indices"]]
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365 }
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366 }
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367 }
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368
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369 # Metadata: Remove missing data
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370 # This is defined as if there is only one non-NA value or
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371 # if the number of NOT NA data is less than a percentage of the data defined by dMinSamp
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372 aiRemove = c()
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373 for( iCol in c(aiMetadata) )
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374 {
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375 adCol = frmeData[,iCol]
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376 if( ( sum( !is.na( adCol ) ) < ( dMinSamp * length( adCol ) ) ) ||
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377 ( length( unique( na.omit( adCol ) ) ) < 2 ) )
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378 {
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379 aiRemove = c(aiRemove, iCol)
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380 }
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381 }
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382
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383 # Remove metadata
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384 aiMetadata = setdiff( aiMetadata, aiRemove )
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385
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386 # Update the data which was removed.
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387 lsQCCounts$iMissingMetadata = aiRemove
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388 if(length(aiRemove))
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389 {
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390 c_logrMaaslin$info("Removing the following metadata for too much missing data or only one data value outside of NA.")
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391 c_logrMaaslin$info(format(colnames( frmeData )[aiRemove]))
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392 }
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393
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394 # Keep track of factor levels in a list for later use
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395 lslsFactors <- list()
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396 for( iCol in c(aiMetadata) )
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397 {
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398 aCol <- frmeData[,iCol]
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399 if( class( aCol ) == "factor" )
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400 {
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401 lslsFactors[[length( lslsFactors ) + 1]] <- list(iCol, levels( aCol ))
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402 }
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403 }
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404
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405 # Replace missing data values by the mean of the data column.
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406 # Remove samples that were all NA from the cleaning and so could not be imputed.
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407 aiRemoveData = c()
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408 for( iCol in aiData )
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409 {
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410 adCol <- frmeData[,iCol]
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411 adCol[is.infinite( adCol )] <- NA
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412 adCol[is.na( adCol )] <- mean( adCol[which(adCol>0)], na.rm = TRUE )
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413 frmeData[,iCol] <- adCol
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414
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415 if(length(which(is.na(frmeData[,iCol]))) == length(frmeData[,iCol]))
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416 {
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417 c_logrMaaslin$info( paste("Removing data", iCol, "for being all NA after QC"))
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418 aiRemoveData = c(aiRemoveData,iCol)
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419 }
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420 }
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421
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422 # Remove and document
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423 aiData = setdiff( aiData, aiRemoveData )
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424 lsQCCounts$iMissingData = c(lsQCCounts$iMissingData,aiRemoveData)
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425 if(length(aiRemoveData))
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426 {
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427 c_logrMaaslin$info( "Removing the following for having only NAs after cleaning (maybe due to only having NA after outlier testing).")
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428 c_logrMaaslin$info( format( colnames( frmeData )[aiRemoveData] ))
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429 }
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430
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431 #Use na.gam.replace to manage NA metadata
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432 aiTmp <- setdiff( aiMetadata, which( colnames( frmeData ) %in% astrNoImpute ) )
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433 # Keep tack of NAs so the may not be plotted later.
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434 liNaIndices = list()
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435 lsNames = names(frmeData)
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436 for( i in aiTmp)
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437 {
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438 liNaIndices[[lsNames[i]]] = which(is.na(frmeData[,i]))
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439 }
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440 frmeData[,aiTmp] <- na.gam.replace( frmeData[,aiTmp] )
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441
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442 #If NA is a value in factor data, set the NA as a level.
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443 for( lsFactor in lslsFactors )
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444 {
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445 iCol <- lsFactor[[1]]
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446 aCol <- frmeData[,iCol]
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447 if( "NA" %in% levels( aCol ) )
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448 {
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449 if(! lsNames[iCol] %in% astrNoImpute)
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450 {
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451 liNaIndices[[lsNames[iCol]]] = union(which(is.na(frmeData[,iCol])),which(frmeData[,iCol]=="NA"))
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452 }
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453 frmeData[,iCol] <- factor( aCol, levels = c(lsFactor[[2]], "NA") )
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454 }
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455 }
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456
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457 # Make sure there is a minimum number of non-0 measurements
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458 aiRemove = c()
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459 for( iCol in aiData )
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460 {
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461 adCol = frmeData[,iCol]
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462 if(length( which(adCol!=0)) < ( dMinSamp * length( adCol ) ) )
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463 {
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464 aiRemove = c(aiRemove, iCol)
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465 }
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466 }
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467
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468 # Remove and document
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469 aiData = setdiff( aiData, aiRemove)
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470 lsQCCounts$iZeroDominantData = aiRemove
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471 if(length(aiRemove))
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472 {
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473 c_logrMaaslin$info( "Removing the following for having not enough non-zero measurments for analysis.")
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474 c_logrMaaslin$info( format( colnames( frmeData )[aiRemove] ))
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475 }
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476
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477 c_logrMaaslin$debug("End FuncClean")
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478 return( list(frmeData = frmeData, aiMetadata = aiMetadata, aiData = aiData, lsQCCounts = lsQCCounts, liNaIndices=liNaIndices, viNotTransformedData = viNotTransformedData) )
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479 ### Return list of
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480 ### frmeData: The Data after cleaning
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481 ### aiMetadata: The indices of the metadata still being used after filtering
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482 ### aiData: The indices of the data still being used after filtering
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483 ### lsQCCOunts: QC info
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484 }
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485
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486 funcBugs <- function(
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487 ### Run analysis of all data features against all metadata
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488 frmeData,
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489 ### Cleaned data including metadata, and data
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490 lsData,
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491 ### This list is a general container for data as the analysis occurs, think about it as a cache for the analysis
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492 aiMetadata,
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493 ### Indices of metadata used in analysis
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494 aiData,
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495 ### Indices of response data
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496 aiNotTransformedData,
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497 ### Indicies of the data not transformed
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498 strData,
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499 ### Log file name
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500 dSig,
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501 ### Significance threshold for the qvalue cut off
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502 fInvert=FALSE,
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503 ### Invert images to have a black background
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504 strDirOut = NA,
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505 ### Output project directory
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506 funcReg=NULL,
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507 ### Function for regularization
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508 funcTransform=NULL,
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509 ### Function used to transform the data
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510 funcUnTransform=NULL,
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511 ### If a transform is used the opposite of that transfor must be used on the residuals in the partial residual plots
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512 lsNonPenalizedPredictors=NULL,
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513 ### These predictors will not be penalized in the feature (model) selection step
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514 funcAnalysis=NULL,
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515 ### Function to perform association analysis
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516 lsRandomCovariates=NULL,
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517 ### List of string names of metadata which will be treated as random covariates
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518 funcGetResults=NULL,
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519 ### Function to unpack results from analysis
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520 fDoRPlot=TRUE,
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521 ### Plot residuals
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522 fOmitLogFile = FALSE,
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523 ### Stops the creation of the log file
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524 fAllvAll=FALSE,
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525 ### Flag to turn on all against all comparisons
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526 liNaIndices = list(),
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527 ### Indicies of imputed NA data
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528 lxParameters=list(),
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529 ### List holds parameters for different variable selection techniques
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530 strTestingCorrection = "BH",
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531 ### Correction for multiple testing
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532 fIsUnivariate = FALSE,
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533 ### Indicates if the function is univariate
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534 fZeroInflated = FALSE
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535 ### Indicates to use a zero infalted model
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536 ){
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537 c_logrMaaslin$debug("Start funcBugs")
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538 # If no output directory is indicated
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539 # Then make it the current directory
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540 if( is.na( strDirOut ) || is.null( strDirOut ) )
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541 {
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542 if( !is.na( strData ) )
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543 {
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544 strDirOut <- paste( dirname( strData ), "/", sep = "" )
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545 } else { strDirOut = "" }
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546 }
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547
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548 # Make th log file and output file names based on the log file name
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549 strLog = NA
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550 strBase = ""
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551 if(!is.na(strData))
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552 {
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553 strBaseOut <- paste( strDirOut, sub( "\\.([^.]+)$", "", basename(strData) ), sep = "/" )
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554 strLog <- paste( strBaseOut,c_sLogFileSuffix, ".txt", sep = "" )
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555 }
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556
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557 # If indicated, stop the creation of the log file
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558 # Otherwise delete the log file if it exists and log
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559 if(fOmitLogFile){ strLog = NA }
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560 if(!is.na(strLog))
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561 {
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562 c_logrMaaslin$info( "Outputting to: %s", strLog )
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563 unlink( strLog )
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564 }
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565
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566 # Will contain pvalues
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567 adP = c()
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568 adPAdj = c()
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569
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570 # List of lists with association information
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571 lsSig <- list()
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572 # Go through each data that was not previously removed and perform inference
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573 for( iTaxon in aiData )
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574 {
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575 # Log to screen progress per 10 associations.
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576 # Can be thown off if iTaxon is missing a mod 10 value
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577 # So the taxons may not be logged every 10 but not a big deal
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578 if( !( iTaxon %% 10 ) )
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579 {
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580 c_logrMaaslin$info( "Taxon %d/%d", iTaxon, max( aiData ) )
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581 }
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582
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583 # Call analysis method
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584 lsOne <- funcBugHybrid( iTaxon=iTaxon, frmeData=frmeData, lsData=lsData, aiMetadata=aiMetadata, dSig=dSig, adP=adP, lsSig=lsSig, funcTransform=funcTransform, funcUnTransform=funcUnTransform, strLog=strLog, funcReg=funcReg, lsNonPenalizedPredictors=lsNonPenalizedPredictors, funcAnalysis=funcAnalysis, lsRandomCovariates=lsRandomCovariates, funcGetResult=funcGetResults, fAllvAll=fAllvAll, fIsUnivariate=fIsUnivariate, lxParameters=lxParameters, fZeroInflated=fZeroInflated, fIsTransformed= ! iTaxon %in% aiNotTransformedData )
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585
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586 # If you get a NA (happens when the lmm gets all random covariates) move on
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587 if( is.na( lsOne ) ){ next }
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588
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589 # The updating of the following happens in the inference method call in the funcBugHybrid call
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590 # New pvalue array
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591 adP <- lsOne$adP
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592 # New lsSig contains data about significant feature v metadata comparisons
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593 lsSig <- lsOne$lsSig
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594 # New qc data
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595 lsData$lsQCCounts = lsOne$lsQCCounts
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596 }
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597
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598 # Log the QC info
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599 c_logrMaaslin$debug("lsData$lsQCCounts")
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600 c_logrMaaslin$debug(format(lsData$lsQCCounts))
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601
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602 if( is.null( adP ) ) { return( NULL ) }
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603
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604 # Perform bonferonni corrections on factor data (for levels), calculate the number of tests performed, and FDR adjust for multiple hypotheses
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605 # Perform Bonferonni adjustment on factor data
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606 for( iADIndex in 1:length( adP ) )
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607 {
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608 # Only perform on factor data
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609 if( is.factor( lsSig[[ iADIndex ]]$metadata ) )
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610 {
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611 adPAdj = c( adPAdj, funcBonferonniCorrectFactorData( dPvalue = adP[ iADIndex ], vsFactors = lsSig[[ iADIndex ]]$metadata, fIgnoreNAs = length(liNaIndices)>0) )
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612 } else {
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613 adPAdj = c( adPAdj, adP[ iADIndex ] )
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614 }
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615 }
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616
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617 iTests = funcCalculateTestCounts(iDataCount = length(aiData), asMetadata = intersect( lsData$astrMetadata, colnames( frmeData )[aiMetadata] ), asForced = lsNonPenalizedPredictors, asRandom = lsRandomCovariates, fAllvAll = fAllvAll)
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618
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619 #Get indices of sorted data after the factor correction but before the multiple hypothesis corrections.
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620 aiSig <- sort.list( adPAdj )
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621
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622 # Perform FDR BH
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623 adQ = p.adjust(adPAdj, method=strTestingCorrection, n=max(length(adPAdj), iTests))
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624
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625 # Find all covariates that had significant associations
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626 astrNames <- c()
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627 for( i in 1:length( lsSig ) )
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628 {
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629 astrNames <- c(astrNames, lsSig[[i]]$name)
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630 }
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631 astrNames <- unique( astrNames )
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632
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633 # Sets up named label return for global plotting
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634 lsReturnTaxa <- list()
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635 for( j in aiSig )
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636 {
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637 if( adQ[j] > dSig ) { next }
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638 strTaxon <- lsSig[[j]]$taxon
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639 if(strTaxon %in% names(lsReturnTaxa))
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640 {
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641 lsReturnTaxa[[strTaxon]] = min(lsReturnTaxa[[strTaxon]],adQ[j])
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642 } else { lsReturnTaxa[[strTaxon]] = adQ[j]}
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643 }
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644
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645 # For each covariate with significant associations
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646 # Write out a file with association information
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647 for( strName in astrNames )
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648 {
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649 strFileTXT <- NA
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650 strFilePDF <- NA
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651 for( j in aiSig )
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652 {
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653 lsCur <- lsSig[[j]]
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654 strCur <- lsCur$name
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655
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656 if( strCur != strName ) { next }
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657
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658 strTaxon <- lsCur$taxon
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659 adData <- lsCur$data
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660 astrFactors <- lsCur$factors
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661 adCur <- lsCur$metadata
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662 adY <- adData
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663
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664 if( is.na( strData ) ) { next }
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665
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666 ## If the text file output is not written to yet
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667 ## make the file names, and delete any previous file output
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668 if( is.na( strFileTXT ) )
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parents:
diff changeset
669 {
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670 strFileTXT <- sprintf( "%s-%s.txt", strBaseOut, strName )
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671 unlink(strFileTXT)
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672 funcWrite( c("Variable", "Feature", "Value", "Coefficient", "N", "N not 0", "P-value", "Q-value"), strFileTXT )
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673 }
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674
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675 ## Write text output
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676 funcWrite( c(strName, strTaxon, lsCur$orig, lsCur$value, length( adData ), sum( adData > 0 ), adP[j], adQ[j]), strFileTXT )
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677
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678 ## If the significance meets the threshold
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679 ## Write PDF file output
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680 if( adQ[j] > dSig ) { next }
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681
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682 # Do not make residuals plots if univariate is selected
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683 strFilePDF = funcPDF( frmeTmp=frmeData, lsCur=lsCur, curPValue=adP[j], curQValue=adQ[j], strFilePDF=strFilePDF, strBaseOut=strBaseOut, strName=strName, funcUnTransform= funcUnTransform, fDoResidualPlot=fDoRPlot, fInvert=fInvert, liNaIndices=liNaIndices )
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684 }
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685 if( dev.cur( ) != 1 ) { dev.off( ) }
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686 }
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687 aiTmp <- aiData
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688
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689 logdebug("End funcBugs", c_logMaaslin)
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690 return(list(lsReturnBugs=lsReturnTaxa, lsQCCounts=lsData$lsQCCounts))
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parents:
diff changeset
691 ### List of data features successfully associated without error and quality control data
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692 }
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parents:
diff changeset
693
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parents:
diff changeset
694 #Lightly Tested
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parents:
diff changeset
695 ### Performs analysis for 1 feature
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parents:
diff changeset
696 ### iTaxon: integer Taxon index to be associated with data
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parents:
diff changeset
697 ### frmeData: Data frame The full data
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diff changeset
698 ### lsData: List of all associated data
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parents:
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699 ### aiMetadata: Numeric vector of indices
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parents:
diff changeset
700 ### dSig: Numeric significance threshold for q-value cut off
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parents:
diff changeset
701 ### adP: List of pvalues from associations
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parents:
diff changeset
702 ### lsSig: List which serves as a cache of data about significant associations
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parents:
diff changeset
703 ### strLog: String file to log to
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parents:
diff changeset
704 funcBugHybrid <- function(
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diff changeset
705 ### Performs analysis for 1 feature
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parents:
diff changeset
706 iTaxon,
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parents:
diff changeset
707 ### integer Taxon index to be associated with data
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parents:
diff changeset
708 frmeData,
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parents:
diff changeset
709 ### Data frame, the full data
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diff changeset
710 lsData,
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parents:
diff changeset
711 ### List of all associated data
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diff changeset
712 aiMetadata,
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diff changeset
713 ### Numeric vector of indices
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diff changeset
714 dSig,
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parents:
diff changeset
715 ### Numeric significance threshold for q-value cut off
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parents:
diff changeset
716 adP,
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parents:
diff changeset
717 ### List of pvalues from associations
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diff changeset
718 lsSig,
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parents:
diff changeset
719 ### List which serves as a cache of data about significant associations
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parents:
diff changeset
720 funcTransform,
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parents:
diff changeset
721 ### The tranform used on the data
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parents:
diff changeset
722 funcUnTransform,
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parents:
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723 ### The reverse transform on the data
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parents:
diff changeset
724 strLog = NA,
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parents:
diff changeset
725 ### String, file to which to log
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parents:
diff changeset
726 funcReg=NULL,
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parents:
diff changeset
727 ### Function to perform regularization
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parents:
diff changeset
728 lsNonPenalizedPredictors=NULL,
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parents:
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729 ### These predictors will not be penalized in the feature (model) selection step
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parents:
diff changeset
730 funcAnalysis=NULL,
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parents:
diff changeset
731 ### Function to perform association analysis
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diff changeset
732 lsRandomCovariates=NULL,
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parents:
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733 ### List of string names of metadata which will be treated as random covariates
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parents:
diff changeset
734 funcGetResult=NULL,
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parents:
diff changeset
735 ### Function to unpack results from analysis
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parents:
diff changeset
736 fAllvAll=FALSE,
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parents:
diff changeset
737 ### Flag to turn on all against all comparisons
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parents:
diff changeset
738 fIsUnivariate = FALSE,
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parents:
diff changeset
739 ### Indicates the analysis function is univariate
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parents:
diff changeset
740 lxParameters=list(),
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parents:
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741 ### List holds parameters for different variable selection techniques
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parents:
diff changeset
742 fZeroInflated = FALSE,
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parents:
diff changeset
743 ### Indicates if to use a zero infalted model
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parents:
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744 fIsTransformed = TRUE
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parents:
diff changeset
745 ### Indicates that the bug is transformed
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parents:
diff changeset
746 ){
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parents:
diff changeset
747 #dTime00 <- proc.time()[3]
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parents:
diff changeset
748 #Get metadata column names
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parents:
diff changeset
749 astrMetadata = intersect( lsData$astrMetadata, colnames( frmeData )[aiMetadata] )
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parents:
diff changeset
750
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parents:
diff changeset
751 #Get data measurements that are not NA
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752 aiRows <- which( !is.na( frmeData[,iTaxon] ) )
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753
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parents:
diff changeset
754 #Get the dataframe of non-na data measurements
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diff changeset
755 frmeTmp <- frmeData[aiRows,]
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756
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parents:
diff changeset
757 #Set the min boosting selection frequency to a default if not given
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758 if( is.na( lxParameters$dFreq ) )
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759 {
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760 lxParameters$dFreq <- 0.5 / length( c(astrMetadata) )
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761 }
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762
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parents:
diff changeset
763 # Get the full data for the bug feature
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parents:
diff changeset
764 adCur = frmeTmp[,iTaxon]
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diff changeset
765 lxParameters$sBugName = names(frmeTmp[iTaxon])
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766
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parents:
diff changeset
767 # This can run multiple models so some of the results are held in lists and some are not
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parents:
diff changeset
768 llmod = list()
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diff changeset
769 liTaxon = list()
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parents:
diff changeset
770 lastrTerms = list()
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parents:
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771
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parents:
diff changeset
772 # Build formula for simple mixed effects models
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parents:
diff changeset
773 # Removes random covariates from variable selection
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parents:
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774 astrMetadata = setdiff(astrMetadata, lsRandomCovariates)
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diff changeset
775 strFormula <- paste( "adCur ~", paste( sprintf( "`%s`", astrMetadata ), collapse = " + " ), sep = " " )
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diff changeset
776
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parents:
diff changeset
777 # Document the model
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parents:
diff changeset
778 funcWrite( c("#taxon", colnames( frmeTmp )[iTaxon]), strLog )
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parents:
diff changeset
779 funcWrite( c("#metadata", astrMetadata), strLog )
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parents:
diff changeset
780 funcWrite( c("#samples", rownames( frmeTmp )), strLog )
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781
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parents:
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782 #Model terms
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parents:
diff changeset
783 astrTerms <- c()
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diff changeset
784
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parents:
diff changeset
785 # Attempt feature (model) selection
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parents:
diff changeset
786 if(!is.na(funcReg))
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parents:
diff changeset
787 {
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parents:
diff changeset
788 #Count model selection method attempts
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diff changeset
789 lsData$lsQCCounts$iBoosts = lsData$lsQCCounts$iBoosts + 1
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parents:
diff changeset
790 #Perform model selection
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diff changeset
791 astrTerms <- funcReg(strFormula=strFormula, frmeTmp=frmeTmp, adCur=adCur, lsParameters=lxParameters, lsForcedParameters=lsNonPenalizedPredictors, strLog=strLog)
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792 #If the feature selection function is set to None, set all terms of the model to all the metadata
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793 } else { astrTerms = astrMetadata }
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794
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795 # Get look through the boosting results to get a model
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796 # Holds the predictors in the predictors in the model that were selected by the boosting
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797 if(is.null( astrTerms )){lsData$lsQCCounts$iBoostErrors = lsData$lsQCCounts$iBoostErrors + 1}
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798
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799 # Get the indices that are transformed
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800 # Of those indices check for uneven metadata
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801 # Untransform any of the metadata that failed
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802 # Failed means true for uneven occurences of zeros
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803 # if( fIsTransformed )
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804 # {
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805 # vdUnevenZeroCheck = funcUnTransform( frmeData[[ iTaxon ]] )
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806 # if( funcZerosAreUneven( vdRawData=vdUnevenZeroCheck, funcTransform=funcTransform, vsStratificationFeatures=astrTerms, dfData=frmeData ) )
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807 # {
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808 # frmeData[[ iTaxon ]] = vdUnevenZeroCheck
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809 # c_logrMaaslin$debug( paste( "Taxon transformation reversed due to unevenness of zero distribution.", iTaxon ) )
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810 # }
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811 # }
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812
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813 # Run association analysis if predictors exist and an analysis function is specified
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814 # Run analysis
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815 if(!is.na(funcAnalysis) )
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816 {
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817 #If there are selected and forced fixed covariates
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818 if( length( astrTerms ) )
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819 {
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820 #Count the association attempt
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821 lsData$lsQCCounts$iLms = lsData$lsQCCounts$iLms + 1
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822
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823 #Make the lm formula
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824 #Build formula for simple mixed effects models using random covariates
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825 strRandomCovariatesFormula = NULL
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826 #Random covariates are forced
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827 if(length(lsRandomCovariates)>0)
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828 {
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829 #Format for lme
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830 #Needed for changes to not allowing random covariates through the boosting process
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831 strRandomCovariatesFormula <- paste( "adCur ~ ", paste( sprintf( "1|`%s`", lsRandomCovariates), collapse = " + " ))
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832 }
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833
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834 #Set up a list of formula containing selected fixed variables changing and the forced fixed covariates constant
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835 vstrFormula = c()
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836 #Set up suppressing forced covariates in a all v all scenario only
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837 asSuppress = c()
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838 #Enable all against all comparisons
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839 if(fAllvAll && !fIsUnivariate)
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840 {
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841 lsVaryingCovariates = setdiff(astrTerms,lsNonPenalizedPredictors)
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842 lsConstantCovariates = setdiff(lsNonPenalizedPredictors,lsRandomCovariates)
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843 strConstantFormula = paste( sprintf( "`%s`", lsConstantCovariates ), collapse = " + " )
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844 asSuppress = lsConstantCovariates
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845
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846 if(length(lsVaryingCovariates)==0L)
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847 {
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848 vstrFormula <- c( paste( "adCur ~ ", paste( sprintf( "`%s`", lsConstantCovariates ), collapse = " + " )) )
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849 } else {
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850 for( sVarCov in lsVaryingCovariates )
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851 {
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852 strTempFormula = paste( "adCur ~ `", sVarCov,"`",sep="")
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853 if(length(lsConstantCovariates)>0){ strTempFormula = paste(strTempFormula,strConstantFormula,sep=" + ") }
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854 vstrFormula <- c( vstrFormula, strTempFormula )
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855 }
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856 }
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857 } else {
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858 #This is either the multivariate case formula for all covariates in an lm or fixed covariates in the lmm
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859 vstrFormula <- c( paste( "adCur ~ ", paste( sprintf( "`%s`", astrTerms ), collapse = " + " )) )
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860 }
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861
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862 #Run the association
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863 for( strAnalysisFormula in vstrFormula )
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864 {
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865 i = length(llmod)+1
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866 llmod[[i]] = funcAnalysis(strFormula=strAnalysisFormula, frmeTmp=frmeTmp, iTaxon=iTaxon, lsHistory=list(adP=adP, lsSig=lsSig, lsQCCounts=lsData$lsQCCounts), strRandomFormula=strRandomCovariatesFormula, fZeroInflated=fZeroInflated)
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867
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868 liTaxon[[i]] = iTaxon
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869 lastrTerms[[i]] = funcFormulaStrToList(strAnalysisFormula)
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870 }
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871 } else {
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872 #If there are no selected or forced fixed covariates
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873 lsData$lsQCCounts$iNoTerms = lsData$lsQCCounts$iNoTerms + 1
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874 return(list(adP=adP, lsSig=lsSig, lsQCCounts=lsData$lsQCCounts))
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875 }
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876 }
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877
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878 #Call funcBugResults and return it's return
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879 if(!is.na(funcGetResult))
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880 {
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881 #Format the results to a consistent expected result.
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882 return( funcGetResult( llmod=llmod, frmeData=frmeData, liTaxon=liTaxon, dSig=dSig, adP=adP, lsSig=lsSig, strLog=strLog, lsQCCounts=lsData$lsQCCounts, lastrCols=lastrTerms, asSuppressCovariates=asSuppress ) )
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883 } else {
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884 return(list(adP=adP, lsSig=lsSig, lsQCCounts=lsData$lsQCCounts))
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885 }
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886 ### List containing a list of pvalues, a list of significant data per association, and a list of QC data
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887 }