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1 #!/usr/bin/env Rscript
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2 #####################################################################################
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3 #Copyright (C) <2012>
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4 #
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5 #Permission is hereby granted, free of charge, to any person obtaining a copy of
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6 #this software and associated documentation files (the "Software"), to deal in the
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7 #Software without restriction, including without limitation the rights to use, copy,
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8 #modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
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9 #and to permit persons to whom the Software is furnished to do so, subject to
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10 #the following conditions:
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11 #
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12 #The above copyright notice and this permission notice shall be included in all copies
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13 #or substantial portions of the Software.
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14 #
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15 #THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
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16 #INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
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17 #PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
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18 #HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
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19 #OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
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20 #SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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21 #
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22 # This file is a component of the MaAsLin (Multivariate Associations Using Linear Models),
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23 # authored by the Huttenhower lab at the Harvard School of Public Health
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24 # (contact Timothy Tickle, ttickle@hsph.harvard.edu).
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25 #####################################################################################
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26
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27 inlinedocs <- function(
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28 ##author<< Curtis Huttenhower <chuttenh@hsph.harvard.edu> and Timothy Tickle <ttickle@hsph.harvard.edu>
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29 ##description<< Main driver script. Should be called to perform MaAsLin Analysis.
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30 ) { return( pArgs ) }
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31
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32
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33 ### Install packages if not already installed
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34 vDepLibrary = c("agricolae", "gam", "gamlss", "gbm", "glmnet", "inlinedocs", "logging", "MASS", "nlme", "optparse", "outliers", "penalized", "pscl", "robustbase", "testthat")
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35 for(sDepLibrary in vDepLibrary)
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36 {
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37 if(! require(sDepLibrary, character.only=TRUE) )
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38 {
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39 install.packages(pkgs=sDepLibrary, repos="http://cran.us.r-project.org")
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40 }
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41 }
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42
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43 ### Logging class
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44 suppressMessages(library( logging, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE))
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45 ### Class for commandline argument processing
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46 suppressMessages(library( optparse, warn.conflicts=FALSE, quietly=TRUE, verbose=FALSE))
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47
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48
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49 ### Create command line argument parser
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50 pArgs <- OptionParser( usage = "%prog [options] <output.txt> <data.tsv>" )
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51
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52 # Input files for MaAsLin
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53 ## Data configuration file
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54 pArgs <- add_option( pArgs, c("-i", "--input_config"), type="character", action="store", dest="strInputConfig", metavar="data.read.config", help="Optional configuration file describing data input format.")
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55 ## Data manipulation/normalization file
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56 pArgs <- add_option( pArgs, c("-I", "--input_process"), type="character", action="store", dest="strInputR", metavar="data.R", help="Optional configuration script normalizing or processing data.")
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57
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58 # Settings for MaAsLin
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59 ## Maximum false discovery rate
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60 pArgs <- add_option( pArgs, c("-d", "--fdr"), type="double", action="store", dest="dSignificanceLevel", default=0.25, metavar="significance", help="The threshold to use for significance for the generated q-values (BH FDR). Anything equal to or lower than this is significant. [Default %default]")
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61 ## Minimum feature relative abundance filtering
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62 pArgs <- add_option( pArgs, c("-r", "--minRelativeAbundance"), type="double", action="store", dest="dMinAbd", default=0.0001, metavar="minRelativeAbundance", help="The minimum relative abundance allowed in the data. Values below this are removed and imputed as the median of the sample data. [Default %default]")
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63 ## Minimum feature prevalence filtering
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64 pArgs <- add_option( pArgs, c("-p", "--minPrevalence"), type="double", action="store", dest="dMinSamp", default=0.1, metavar="minPrevalence", help="The minimum percentage of samples a feature can have abundance in before being removed. Also is the minimum percentage of samples a metadata can have that are not NA before being removed. [Default %default]")
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65 ## Fence for outlier, if not set Grubbs test is used
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66 pArgs <- add_option( pArgs, c("-o", "--outlierFence"), type="double", action="store", dest="dOutlierFence", default=0, metavar="outlierFence", help="Outliers are defined as this number times the interquartile range added/subtracted from the 3rd/1st quartiles respectively. If set to 0 (default), outliers are defined by the Grubbs test. [Default %default]")
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67 ## Significance for Grubbs test
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68 pArgs <- add_option(pArgs, c("-G","--grubbsSig"), type="double", action="store", dest="dPOutlier", default=0.05, metavar="grubbsAlpha", help="This is the significance cuttoff used to indicate an outlier or not. The closer to zero, the more significant an outlier must be to be removed. [Default %default]")
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69 ## Fixed (not random) covariates
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70 pArgs <- add_option( pArgs, c("-R","--random"), type="character", action="store", dest="strRandomCovariates", default=NULL, metavar="fixed", help="These metadata will be treated as random covariates. Comma delimited data feature names. These features must be listed in the read.config file. Example '-R RandomMetadata1,RandomMetadata2'. [Default %default]")
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71 ## Change the type of correction fo rmultiple corrections
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72 pArgs <- add_option( pArgs, c("-T","--testingCorrection"), type="character", action="store", dest="strMultTestCorrection", default="BH", metavar="multipleTestingCorrection", help="This indicates which multiple hypothesis testing method will be used, available are holm, hochberg, hommel, bonferroni, BH, BY. [Default %default]")
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73 ## Use a zero inflated model of the inference method indicate in -m
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74 pArgs <- add_option( pArgs, c("-z","--doZeroInfated"), type="logical", action="store_true", default = FALSE, dest="fZeroInflated", metavar="fZeroInflated", help="If true, the zero inflated version of the inference model indicated in -m is used. For instance if using lm, zero-inflated regression on a gaussian distribution is used. [Default %default].")
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75
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76 # Arguments used in validation of MaAsLin
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77 ## Model selection (enumerate) c("none","boost","penalized","forward","backward")
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78 pArgs <- add_option( pArgs, c("-s", "--selection"), type="character", action="store", dest="strModelSelection", default="boost", metavar="model_selection", help="Indicates which of the variable selection techniques to use. [Default %default]")
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79 ## Argument indicating which method should be ran (enumerate) c("univariate","lm","neg_binomial","quasi")
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80 pArgs <- add_option( pArgs, c("-m", "--method"), type="character", action="store", dest="strMethod", default="lm", metavar="analysis_method", help="Indicates which of the statistical inference methods to run. [Default %default]")
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81 ## Argument indicating which link function is used c("none","asinsqrt")
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82 pArgs <- add_option( pArgs, c("-l", "--link"), type="character", action="store", dest="strTransform", default="asinsqrt", metavar="transform_method", help="Indicates which link or transformation to use with a glm, if glm is not selected this argument will be set to none. [Default %default]")
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83 pArgs <- add_option( pArgs, c("-Q","--NoQC"), type="logical", action="store_true", default=FALSE, dest="fNoQC", metavar="Do_Not_Run_QC", help="Indicates if the quality control will be ran on the metadata/data. Default is true. [Default %default]")
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84
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85 # Arguments to suppress MaAsLin actions on certain data
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86 ## Do not perform model selection on the following data
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87 pArgs <- add_option( pArgs, c("-F","--forced"), type="character", action="store", dest="strForcedPredictors", default=NULL, metavar="forced_predictors", help="Metadata features that will be forced into the model seperated by commas. These features must be listed in the read.config file. Example '-F Metadata2,Metadata6,Metadata10'. [Default %default]")
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88 ## Do not impute the following
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89 pArgs <- add_option( pArgs, c("-n","--noImpute"), type="character", action="store", dest="strNoImpute", default=NULL, metavar="no_impute", help="These data will not be imputed. Comma delimited data feature names. Example '-n Feature1,Feature4,Feature6'. [Default %default]")
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90
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91 #Miscellaneouse arguments
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92 ### Argument to control logging (enumerate)
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93 strDefaultLogging = "DEBUG"
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94 pArgs <- add_option( pArgs, c("-v", "--verbosity"), type="character", action="store", dest="strVerbosity", default=strDefaultLogging, metavar="verbosity", help="Logging verbosity [Default %default]")
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95 ### Run maaslin without creating a log file
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96 pArgs <- add_option( pArgs, c("-O","--omitLogFile"), type="logical", action="store_true", default=FALSE, dest="fOmitLogFile", metavar="omitlogfile",help="Including this flag will stop the creation of the output log file. [Default %default]")
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97 ### Argument for inverting background to black
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98 pArgs <- add_option( pArgs, c("-t", "--invert"), type="logical", action="store_true", dest="fInvert", default=FALSE, metavar="invert", help="When given, flag indicates to invert the background of figures to black. [Default %default]")
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99 ### Selection Frequency
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100 pArgs <- add_option( pArgs, c("-f","--selectionFrequency"), type="double", action="store", dest="dSelectionFrequency", default=NA, metavar="selectionFrequency", help="Selection Frequency for boosting (max 1 will remove almost everything). Interpreted as requiring boosting to select metadata 100% percent of the time (or less if given a number that is less). Value should be between 1 (100%) and 0 (0%), NA (default is determined by data size).")
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101 ### All v All
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102 pArgs <- add_option( pArgs, c("-a","--allvall"), type="logical", action="store_true", dest="fAllvAll", default=FALSE, metavar="compare_all", help="When given, the flag indicates that each fixed covariate that is not indicated as Forced is compared once at a time per data feature (bug). Made to be used with the -F option to specify one part of the model while allowing the other to cycle through a group of covariates. Does not affect Random covariates, which are always included when specified. [Default %default]")
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103 pArgs <- add_option( pArgs, c("-N","--PlotNA"), type="logical", action="store_true", default=FALSE, dest="fPlotNA", metavar="plotNAs",help="Plot data that was originally NA, by default they are not plotted. [Default %default]")
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104 ### Alternative methodology settings
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105 pArgs <- add_option( pArgs, c("-A","--pAlpha"), type="double", action="store", dest="dPenalizedAlpha", default=0.95, metavar="PenalizedAlpha",help="The alpha for penalization (1.0=L1 regularization, LASSO; 0.0=L2 regularization, ridge regression. [Default %default]")
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106 ### Pass an alternative library dir
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107 pArgs <- add_option( pArgs, c("-L", "--libdir"), action="store", dest="sAlternativeLibraryLocation", default=file.path( "","usr","share","biobakery" ), metavar="AlternativeLibraryDirectory", help="An alternative location to find the lib directory. This dir and children will be searched for the first maaslin/src/lib dir.")
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108
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109 ### Misc biplot arguments
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110 pArgs <- add_option( pArgs, c("-M","--BiplotMetadataScale"), type="double", action="store", dest="dBiplotMetadataScale", default=1, metavar="scaleForMetadata", help="A real number used to scale the metadata labels on the biplot (otherwise a default will be selected from the data). [Default %default]")
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111 pArgs <- add_option( pArgs, c("-C", "--BiplotColor"), type="character", action="store", dest="strBiplotColor", default=NULL, metavar="BiplotColorCovariate", help="A continuous metadata that will be used to color samples in the biplot ordination plot (otherwise a default will be selected from the data). Example Age [Default %default]")
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112 pArgs <- add_option( pArgs, c("-S", "--BiplotShapeBy"), type="character", action="store", dest="strBiplotShapeBy", default=NULL, metavar="BiplotShapeCovariate", help="A discontinuous metadata that will be used to indicate shapes of samples in the Biplot ordination plot (otherwise a default will be selected from the data). Example Sex [Default %default]")
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113 pArgs <- add_option( pArgs, c("-P", "--BiplotPlotFeatures"), type="character", action="store", dest="strBiplotPlotFeatures", default=NULL, metavar="BiplotFeaturesToPlot", help="Metadata and data features to plot (otherwise a default will be selected from the data). Comma Delimited.")
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114 pArgs <- add_option( pArgs, c("-D", "--BiplotRotateMetadata"), type="character", action="store", dest="sRotateByMetadata", default=NULL, metavar="BiplotRotateMetadata", help="Metadata to use to rotate the biplot. Format 'Metadata,value'. 'Age,0.5' . [Default %default]")
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115 pArgs <- add_option( pArgs, c("-B", "--BiplotShapes"), type="character", action="store", dest="sShapes", default=NULL, metavar="BiplotShapes", help="Specify shapes specifically for metadata or metadata values. [Default %default]")
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116 pArgs <- add_option( pArgs, c("-b", "--BugCount"), type="integer", action="store", dest="iNumberBugs", default=3, metavar="PlottedBugCount", help="The number of bugs automatically selected from the data to plot. [Default %default]")
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117 pArgs <- add_option( pArgs, c("-E", "--MetadataCount"), type="integer", action="store", dest="iNumberMetadata", default=NULL, metavar="PlottedMetadataCount", help="The number of metadata automatically selected from the data to plot. [Default all significant metadata and minimum is 1]")
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118
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119 #pArgs <- add_option( pArgs, c("-c","--MFAFeatureCount"), type="integer", action="store", dest="iMFAMaxFeatures", default=3, metavar="maxMFAFeature", help="Number of features or number of bugs to plot (default=3; 3 metadata and 3 data).")
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120
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121 main <- function(
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122 ### The main function manages the following:
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123 ### 1. Optparse arguments are checked
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124 ### 2. A logger is created if requested in the optional arguments
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125 ### 3. The custom R script is sourced. This is the input *.R script named
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126 ### the same as the input *.pcl file. This script contains custom formating
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127 ### of data and function calls to the MFA visualization.
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128 ### 4. Matrices are written to the project folder as they are read in seperately as metadata and data and merged together.
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129 ### 5. Data is cleaned with custom filtering if supplied in the *.R script.
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130 ### 6. Transformations occur if indicated by the optional arguments
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131 ### 7. Standard quality control is performed on data
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132 ### 8. Cleaned metadata and data are written to output project for documentation.
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133 ### 9. A regularization method is ran (boosting by default).
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134 ### 10. An analysis method is performed on the model (optionally boosted model).
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135 ### 11. Data is summarized and PDFs are created for significant associations
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136 ### (those whose q-values {BH FDR correction} are <= the threshold given in the optional arguments.
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137 pArgs
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138 ### Parsed commandline arguments
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139 ){
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140 lsArgs <- parse_args( pArgs, positional_arguments = TRUE )
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141 #logdebug("lsArgs", c_logrMaaslin)
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142 #logdebug(paste(lsArgs,sep=" "), c_logrMaaslin)
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143
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144 # Parse parameters
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145 lsForcedParameters = NULL
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146 if(!is.null(lsArgs$options$strForcedPredictors))
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147 {
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148 lsForcedParameters = unlist(strsplit(lsArgs$options$strForcedPredictors,","))
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149 }
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150 xNoImpute = NULL
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151 if(!is.null(lsArgs$options$strNoImpute))
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152 {
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153 xNoImpute = unlist(strsplit(lsArgs$options$strNoImpute,"[,]"))
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154 }
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155 lsRandomCovariates = NULL
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156 if(!is.null(lsArgs$options$strRandomCovariates))
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157 {
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158 lsRandomCovariates = unlist(strsplit(lsArgs$options$strRandomCovariates,"[,]"))
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159 }
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160 lsFeaturesToPlot = NULL
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161 if(!is.null(lsArgs$options$strBiplotPlotFeatures))
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162 {
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163 lsFeaturesToPlot = unlist(strsplit(lsArgs$options$strBiplotPlotFeatures,"[,]"))
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164 }
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165
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166 #If logging is not an allowable value, inform user and set to INFO
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167 if(length(intersect(names(loglevels), c(lsArgs$options$strVerbosity))) == 0)
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168 {
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169 print(paste("Maaslin::Error. Did not understand the value given for logging, please use any of the following: DEBUG,INFO,WARN,ERROR."))
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170 print(paste("Maaslin::Warning. Setting logging value to \"",strDefaultLogging,"\"."))
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171 }
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172
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173 # Do not allow mixed effect models and zero inflated models, don't have implemented
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174 if(lsArgs$options$fZeroInflated && !is.null(lsArgs$options$strRandomCovariates))
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175 {
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176 stop("MaAsLin Error:: The combination of zero inflated models and mixed effects models are not supported.")
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177 }
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178
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179 ### Create logger
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180 c_logrMaaslin <- getLogger( "maaslin" )
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181 addHandler( writeToConsole, c_logrMaaslin )
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182 setLevel( lsArgs$options$strVerbosity, c_logrMaaslin )
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183
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184 #Get positional arguments
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185 if( length( lsArgs$args ) != 2 ) { stop( print_help( pArgs ) ) }
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186 ### Output file name
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187 strOutputTXT <- lsArgs$args[1]
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188 ### Input TSV data file
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189 strInputTSV <- lsArgs$args[2]
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190
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191 # Get analysis method options
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192 # includes data transformations, model selection/regularization, regression models/links
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193 lsArgs$options$strModelSelection = tolower(lsArgs$options$strModelSelection)
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194 if(!lsArgs$options$strModelSelection %in% c("none","boost","penalized","forward","backward"))
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195 {
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196 logerror(paste("Received an invalid value for the selection argument, received '",lsArgs$options$strModelSelection,"'"), c_logrMaaslin)
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197 stop( print_help( pArgs ) )
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198 }
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199 lsArgs$options$strMethod = tolower(lsArgs$options$strMethod)
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200 if(!lsArgs$options$strMethod %in% c("univariate","lm","neg_binomial","quasi"))
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201 {
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202 logerror(paste("Received an invalid value for the method argument, received '",lsArgs$options$strMethod,"'"), c_logrMaaslin)
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203 stop( print_help( pArgs ) )
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204 }
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205 lsArgs$options$strTransform = tolower(lsArgs$options$strTransform)
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206 if(!lsArgs$options$strTransform %in% c("none","asinsqrt"))
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207 {
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208 logerror(paste("Received an invalid value for the transform/link argument, received '",lsArgs$options$strTransform,"'"), c_logrMaaslin)
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209 stop( print_help( pArgs ) )
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210 }
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211
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212 if(!lsArgs$options$strMultTestCorrection %in% c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY"))
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213 {
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214 logerror(paste("Received an invalid value for the multiple testing correction argument, received '",lsArgs$options$strMultTestCorrection,"'"), c_logrMaaslin)
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215 stop( print_help( pArgs ) )
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216 }
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217
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218 ### Necessary local import files
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219 ### Check to make sure the lib is in the expected place (where the script is)
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220 ### if not, then try the alternative lib location
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221 ### This will happen if, for instance the script is linked or
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222 ### on the path.
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223 # Get the first choice relative path
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224 initial.options <- commandArgs(trailingOnly = FALSE)
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225 script.name <- sub("--file=", "", initial.options[grep("--file=", initial.options)])
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226 strDir = file.path( dirname( script.name ), "lib" )
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227 # If this does not have the lib file then go for the alt lib
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228 if( !file.exists(strDir) )
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229 {
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230 lsPotentialListLocations = dir( path = lsArgs$options$sAlternativeLibraryLocation, pattern = "lib", recursive = TRUE, include.dirs = TRUE)
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231 if( length( lsPotentialListLocations ) > 0 )
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232 {
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233 sLibraryPath = file.path( "maaslin","src","lib" )
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234 iLibraryPathLength = nchar( sLibraryPath )
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235 for( strSearchDir in lsPotentialListLocations )
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236 {
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237 # Looking for the path where the end of the path is equal to the library path given earlier
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238 # Also checks before hand to make sure the path is atleast as long as the library path so no errors occur
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239 if ( substring( strSearchDir, 1 + nchar( strSearchDir ) - iLibraryPathLength ) == sLibraryPath )
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240 {
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241 strDir = file.path( lsArgs$options$sAlternativeLibraryLocation, strSearchDir )
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242 break
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243 }
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244 }
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245 }
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246 }
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247
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248 strSelf = basename( script.name )
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249 for( strR in dir( strDir, pattern = "*.R$" ) )
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250 {
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251 if( strR == strSelf ) {next}
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252 source( file.path( strDir, strR ) )
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253 }
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254
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255 # Get analysis modules
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256 afuncVariableAnalysis = funcGetAnalysisMethods(lsArgs$options$strModelSelection,lsArgs$options$strTransform,lsArgs$options$strMethod,lsArgs$options$fZeroInflated)
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257
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258 # Set up parameters for variable selection
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259 lxParameters = list(dFreq=lsArgs$options$dSelectionFrequency, dPAlpha=lsArgs$options$dPenalizedAlpha)
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260 if((lsArgs$options$strMethod == "lm")||(lsArgs$options$strMethod == "univariate"))
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261 { lxParameters$sFamily = "gaussian"
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262 } else if(lsArgs$options$strMethod == "neg_binomial"){ lxParameters$sFamily = "binomial"
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263 } else if(lsArgs$options$strMethod == "quasi"){ lxParameters$sFamily = "poisson"}
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264
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265 #Indicate start
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266 logdebug("Start MaAsLin", c_logrMaaslin)
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267 #Log commandline arguments
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268 logdebug("Commandline Arguments", c_logrMaaslin)
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269 logdebug(lsArgs, c_logrMaaslin)
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270
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271 ### Output directory for the study based on the requested output file
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272 outputDirectory = dirname(strOutputTXT)
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273 ### Base name for the project based on the read.config name
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274 strBase <- sub("\\.[^.]*$", "", basename(strInputTSV))
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275
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276 ### Sources in the custom script
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277 ### If the custom script is not there then
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278 ### defaults are used and no custom scripts are ran
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279 funcSourceScript <- function(strFunctionPath)
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280 {
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281 #If is specified, set up the custom func clean variable
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282 #If the custom script is null then return
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283 if(is.null(strFunctionPath)){return(NULL)}
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284
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285 #Check to make sure the file exists
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286 if(file.exists(strFunctionPath))
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287 {
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288 #Read in the file
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289 source(strFunctionPath)
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290 } else {
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291 #Handle when the file does not exist
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292 stop(paste("MaAsLin Error: A custom data manipulation script was indicated but was not found at the file path: ",strFunctionPath,sep=""))
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293 }
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294 }
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295
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296 #Read file
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297 inputFileData = funcReadMatrices(lsArgs$options$strInputConfig, strInputTSV, log=TRUE)
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298 if(is.null(inputFileData[[c_strMatrixMetadata]])) { names(inputFileData)[1] <- c_strMatrixMetadata }
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299 if(is.null(inputFileData[[c_strMatrixData]])) { names(inputFileData)[2] <- c_strMatrixData }
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300
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301 #Metadata and bug names
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302 lsOriginalMetadataNames = names(inputFileData[[c_strMatrixMetadata]])
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303 lsOriginalFeatureNames = names(inputFileData[[c_strMatrixData]])
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304
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305 #Dimensions of the datasets
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306 liMetaData = dim(inputFileData[[c_strMatrixMetadata]])
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307 liData = dim(inputFileData[[c_strMatrixData]])
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308
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309 #Merge data files together
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310 frmeData = merge(inputFileData[[c_strMatrixMetadata]],inputFileData[[c_strMatrixData]],by.x=0,by.y=0)
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311 #Reset rownames
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312 row.names(frmeData) = frmeData[[1]]
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313 frmeData = frmeData[-1]
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314
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315 #Write QC files only in certain modes of verbosity
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316 # Read in and merge files
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317 if( c_logrMaaslin$level <= loglevels["DEBUG"] ) {
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318 # If the QC internal file does not exist, make
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319 strQCDir = file.path(outputDirectory,"QC")
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320 dir.create(strQCDir, showWarnings = FALSE)
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321 # Write metadata matrix before merge
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322 funcWriteMatrices(dataFrameList=list(Metadata = inputFileData[[c_strMatrixMetadata]]), saveFileList=c(file.path(strQCDir,"metadata.tsv")), configureFileName=c(file.path(strQCDir,"metadata.read.config")), acharDelimiter="\t")
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323 # Write data matrix before merge
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324 funcWriteMatrices(dataFrameList=list(Data = inputFileData[[c_strMatrixData]]), saveFileList=c(file.path(strQCDir,"data.tsv")), configureFileName=c(file.path(strQCDir,"data.read.config")), acharDelimiter="\t")
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325 #Record the data as it has been read
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326 funcWriteMatrices(dataFrameList=list(Merged = frmeData), saveFileList=c(file.path(strQCDir,"read-Merged.tsv")), configureFileName=c(file.path(strQCDir,"read-Merged.read.config")), acharDelimiter="\t")
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327 }
|
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328
|
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329 #Data needed for the MaAsLin environment
|
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330 #List of lists (one entry per file)
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331 #Is contained by a container of itself
|
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332 #lslsData = list()
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333 #List
|
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334 lsData = c()
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335
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336 #List of metadata indicies
|
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337 aiMetadata = c(1:liMetaData[2])
|
|
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338 lsData$aiMetadata = aiMetadata
|
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339 #List of data indicies
|
|
|
340 aiData = c(1:liData[2])+liMetaData[2]
|
|
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341 lsData$aiData = aiData
|
|
|
342 #Add a list to hold qc metrics and counts
|
|
|
343 lsData$lsQCCounts$aiDataInitial = aiData
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344 lsData$lsQCCounts$aiMetadataInitial = aiMetadata
|
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345
|
|
|
346 #Raw data
|
|
|
347 lsData$frmeRaw = frmeData
|
|
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348
|
|
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349 #Load script if it exists, stop on error
|
|
|
350 funcProcess <- NULL
|
|
|
351 if(!is.null(funcSourceScript(lsArgs$options$strInputR))){funcProcess <- get(c_strCustomProcessFunction)}
|
|
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352
|
|
|
353 #Clean the data and update the current data list to the cleaned data list
|
|
|
354 funcTransformData = afuncVariableAnalysis[[c_iTransform]]
|
|
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355 lsQCCounts = list(aiDataCleaned = c(), aiMetadataCleaned = c())
|
|
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356 lsRet = list(frmeData=frmeData, aiData=aiData, aiMetadata=aiMetadata, lsQCCounts=lsQCCounts, liNaIndices=c())
|
|
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357
|
|
|
358 viNotTransformedDataIndices = c()
|
|
|
359 if(!lsArgs$options$fNoQC)
|
|
|
360 {
|
|
|
361 c_logrMaaslin$info( "Running quality control." )
|
|
|
362 lsRet = funcClean( frmeData=frmeData, funcDataProcess=funcProcess, aiMetadata=aiMetadata, aiData=aiData, lsQCCounts=lsData$lsQCCounts, astrNoImpute=xNoImpute, dMinSamp = lsArgs$options$dMinSamp, dMinAbd = lsArgs$options$dMinAbd, dFence=lsArgs$options$dOutlierFence, funcTransform=funcTransformData, dPOutlier=lsArgs$options$dPOutlier)
|
|
|
363
|
|
|
364 viNotTransformedDataIndices = lsRet$viNotTransformedData
|
|
|
365
|
|
|
366 #If using a count based model make sure all are integer (QCing can add in numeric values during interpolation for example)
|
|
|
367 if(lsArgs$options$strMethod %in% c_vCountBasedModels)
|
|
|
368 {
|
|
|
369 c_logrMaaslin$info( "Assuring the data matrix is integer." )
|
|
|
370 for(iDataIndex in aiData)
|
|
|
371 {
|
|
|
372 lsRet$frmeData[ iDataIndex ] = round( lsRet$frmeData[ iDataIndex ] )
|
|
|
373 }
|
|
|
374 }
|
|
|
375 } else {
|
|
|
376 c_logrMaaslin$info( "Not running quality control, attempting transform." )
|
|
|
377 ### Need to do transform if the QC is not performed
|
|
|
378 iTransformed = 0
|
|
|
379 for(iDataIndex in aiData)
|
|
|
380 {
|
|
|
381 if( ! funcTransformIncreasesOutliers( lsRet$frmeData[iDataIndex], funcTransformData ) )
|
|
|
382 {
|
|
|
383 lsRet$frmeData[iDataIndex]=funcTransformData(lsRet$frmeData[iDataIndex])
|
|
|
384 iTransformed = iTransformed + 1
|
|
|
385 } else {
|
|
|
386 viNotTransformedDataIndices = c(viNotTransformedDataIndices, iDataIndex)
|
|
|
387 }
|
|
|
388 }
|
|
|
389 c_logrMaaslin$info(paste("Number of features transformed = ", iTransformed))
|
|
|
390 }
|
|
|
391
|
|
|
392 logdebug("lsRet", c_logrMaaslin)
|
|
|
393 logdebug(format(lsRet), c_logrMaaslin)
|
|
|
394 #Update the variables after cleaning
|
|
|
395 lsRet$frmeRaw = frmeData
|
|
|
396 lsRet$lsQCCounts$aiDataCleaned = lsRet$aiData
|
|
|
397 lsRet$lsQCCounts$aiMetadataCleaned = lsRet$aiMetadata
|
|
|
398
|
|
|
399 #Add List of metadata string names
|
|
|
400 astrMetadata = colnames(lsRet$frmeData)[lsRet$aiMetadata]
|
|
|
401 lsRet$astrMetadata = astrMetadata
|
|
|
402
|
|
|
403 # If plotting NA data reset the NA metadata indices to empty so they will not be excluded
|
|
|
404 if(lsArgs$options$fPlotNA)
|
|
|
405 {
|
|
|
406 lsRet$liNaIndices = list()
|
|
|
407 }
|
|
|
408
|
|
|
409 #Write QC files only in certain modes of verbosity
|
|
|
410 if( c_logrMaaslin$level <= loglevels["DEBUG"] ) {
|
|
|
411 #Record the data after cleaning
|
|
|
412 funcWriteMatrices(dataFrameList=list(Cleaned = lsRet$frmeData[union(lsRet$aiMetadata,lsRet$aiData)]), saveFileList=c(file.path(strQCDir,"read_cleaned.tsv")), configureFileName=c(file.path(strQCDir,"read_cleaned.read.config")), acharDelimiter="\t") }
|
|
|
413
|
|
|
414 #These variables will be used to count how many features get analysed
|
|
|
415 lsRet$lsQCCounts$iBoosts = 0
|
|
|
416 lsRet$lsQCCounts$iBoostErrors = 0
|
|
|
417 lsRet$lsQCCounts$iNoTerms = 0
|
|
|
418 lsRet$lsQCCounts$iLms = 0
|
|
|
419
|
|
|
420 #Indicate if the residuals plots should occur
|
|
|
421 fDoRPlot=TRUE
|
|
|
422 #Should not occur for univariates
|
|
|
423 if(lsArgs$options$strMethod %in% c("univariate")){ fDoRPlot=FALSE }
|
|
|
424
|
|
|
425 #Run analysis
|
|
|
426 alsRetBugs = funcBugs( frmeData=lsRet$frmeData, lsData=lsRet, aiMetadata=lsRet$aiMetadata, aiData=lsRet$aiData, aiNotTransformedData=viNotTransformedDataIndices, strData=strBase, dSig=lsArgs$options$dSignificanceLevel, fInvert=lsArgs$options$fInvert,
|
|
|
427 strDirOut=outputDirectory, funcReg=afuncVariableAnalysis[[c_iSelection]], funcTransform=funcTransformData, funcUnTransform=afuncVariableAnalysis[[c_iUnTransform]], lsNonPenalizedPredictors=lsForcedParameters,
|
|
|
428 funcAnalysis=afuncVariableAnalysis[[c_iAnalysis]], lsRandomCovariates=lsRandomCovariates, funcGetResults=afuncVariableAnalysis[[c_iResults]], fDoRPlot=fDoRPlot, fOmitLogFile=lsArgs$options$fOmitLogFile,
|
|
|
429 fAllvAll=lsArgs$options$fAllvAll, liNaIndices=lsRet$liNaIndices, lxParameters=lxParameters, strTestingCorrection=lsArgs$options$strMultTestCorrection,
|
|
|
430 fIsUnivariate=afuncVariableAnalysis[[c_iIsUnivariate]], fZeroInflated=lsArgs$options$fZeroInflated )
|
|
|
431
|
|
|
432 #Write QC files only in certain modes of verbosity
|
|
|
433 if( c_logrMaaslin$level <= loglevels["DEBUG"] ) {
|
|
|
434 funcWriteQCReport(strProcessFileName=file.path(strQCDir,"ProcessQC.txt"), lsQCData=alsRetBugs$lsQCCounts, liDataDim=liData, liMetadataDim=liMetaData)
|
|
|
435
|
|
|
436 ### Write out the parameters used in the run
|
|
|
437 unlink(file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
438 funcWrite("Parameters used in the MaAsLin run", file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
439 funcWrite(paste("Optional input read.config file=",lsArgs$options$strInputConfig), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
440 funcWrite(paste("Optional R file=",lsArgs$options$strInputR), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
441 funcWrite(paste("FDR threshold for pdf generation=",lsArgs$options$dSignificanceLevel), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
442 funcWrite(paste("Minimum relative abundance=",lsArgs$options$dMinAbd), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
443 funcWrite(paste("Minimum percentage of samples with measurements=",lsArgs$options$dMinSamp), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
444 funcWrite(paste("The fence used to define outliers with a quantile based analysis. If set to 0, the Grubbs test was used=",lsArgs$options$dOutlierFence), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
445 funcWrite(paste("Ignore if the Grubbs test was not used. The significance level used as a cut-off to define outliers=",lsArgs$options$dPOutlier), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
446 funcWrite(paste("These covariates are treated as random covariates and not fixed covariates=",lsArgs$options$strRandomCovariates), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
447 funcWrite(paste("The type of multiple testing correction used=",lsArgs$options$strMultTestCorrection), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
448 funcWrite(paste("Zero inflated inference models were turned on=",lsArgs$options$fZeroInflated), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
449 funcWrite(paste("Feature selection step=",lsArgs$options$strModelSelection), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
450 funcWrite(paste("Statistical inference step=",lsArgs$options$strMethod), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
451 funcWrite(paste("Numeric transform used=",lsArgs$options$strTransform), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
452 funcWrite(paste("Quality control was run=",!lsArgs$options$fNoQC), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
453 funcWrite(paste("These covariates were forced into each model=",lsArgs$options$strForcedPredictors), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
454 funcWrite(paste("These features' data were not changed by QC processes=",lsArgs$options$strNoImpute), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
455 funcWrite(paste("Output verbosity=",lsArgs$options$strVerbosity), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
456 funcWrite(paste("Log file was generated=",!lsArgs$options$fOmitLogFile), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
457 funcWrite(paste("Data plots were inverted=",lsArgs$options$fInvert), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
458 funcWrite(paste("Ignore unless boosting was used. The threshold for the rel.inf used to select features=",lsArgs$options$dSelectionFrequency), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
459 funcWrite(paste("All verses all inference method was used=",lsArgs$options$fAllvAll), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
460 funcWrite(paste("Ignore unless penalized feature selection was used. Alpha to determine the type of penalty=",lsArgs$options$dPenalizedAlpha), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
461 funcWrite(paste("Biplot parameter, user defined metadata scale=",lsArgs$options$dBiplotMetadataScale), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
462 funcWrite(paste("Biplot parameter, user defined metadata used to color the plot=",lsArgs$options$strBiplotColor), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
463 funcWrite(paste("Biplot parameter, user defined metadata used to dictate the shapes of the plot markers=",lsArgs$options$strBiplotShapeBy), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
464 funcWrite(paste("Biplot parameter, user defined user requested features to plot=",lsArgs$options$strBiplotPlotFeatures), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
465 funcWrite(paste("Biplot parameter, user defined metadata used to rotate the plot ordination=",lsArgs$options$sRotateByMetadata), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
466 funcWrite(paste("Biplot parameter, user defined custom shapes for metadata=",lsArgs$options$sShapes), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
467 funcWrite(paste("Biplot parameter, user defined number of bugs to plot =",lsArgs$options$iNumberBugs), file.path(strQCDir,"Run_Parameters.txt"))
|
|
|
468 }
|
|
|
469
|
|
|
470 ### Write summary table
|
|
|
471 # Summarize output files based on a keyword and a significance threshold
|
|
|
472 # Look for less than or equal to the threshold (appropriate for p-value and q-value type measurements)
|
|
|
473 # DfSummary is sorted by the q.value when it is returned
|
|
|
474 dfSummary = funcSummarizeDirectory(astrOutputDirectory=outputDirectory,
|
|
|
475 strBaseName=strBase,
|
|
|
476 astrSummaryFileName=file.path(outputDirectory,paste(strBase,c_sSummaryFileSuffix, sep="")),
|
|
|
477 astrKeyword=c_strKeywordEvaluatedForInclusion,
|
|
|
478 afSignificanceLevel=lsArgs$options$dSignificanceLevel)
|
|
|
479
|
|
|
480 if( !is.null( dfSummary ) )
|
|
|
481 {
|
|
|
482 ### Start biplot
|
|
|
483 # Get metadata of interest and reduce to default size
|
|
|
484 lsSigMetadata = unique(dfSummary[[1]])
|
|
|
485 if( is.null( lsArgs$options$iNumberMetadata ) )
|
|
|
486 {
|
|
|
487 lsSigMetadata = lsSigMetadata[ 1:length( lsSigMetadata ) ]
|
|
|
488 } else {
|
|
|
489 lsSigMetadata = lsSigMetadata[ 1:min( length( lsSigMetadata ), max( lsArgs$options$iNumberMetadata, 1 ) ) ]
|
|
|
490 }
|
|
|
491
|
|
|
492 # Convert to indices (ordered numerically here)
|
|
|
493 liSigMetadata = which( colnames( lsRet$frmeData ) %in% lsSigMetadata )
|
|
|
494
|
|
|
495 # Get bugs of interest and reduce to default size
|
|
|
496 lsSigBugs = unique(dfSummary[[2]])
|
|
|
497
|
|
|
498 # Reduce the bugs to the right size
|
|
|
499 if(lsArgs$options$iNumberBugs < 1)
|
|
|
500 {
|
|
|
501 lsSigBugs = c()
|
|
|
502 } else if( is.null( lsArgs$options$iNumberBugs ) ) {
|
|
|
503 lsSigBugs = lsSigBugs[ 1 : length( lsSigBugs ) ]
|
|
|
504 } else {
|
|
|
505 lsSigBugs = lsSigBugs[ 1 : lsArgs$options$iNumberBugs ]
|
|
|
506 }
|
|
|
507
|
|
|
508 # Set color by and shape by features if not given
|
|
|
509 # Selects the continuous (for color) and factor (for shape) data with the most significant association
|
|
|
510 if(is.null(lsArgs$options$strBiplotColor)||is.null(lsArgs$options$strBiplotShapeBy))
|
|
|
511 {
|
|
|
512 for(sMetadata in lsSigMetadata)
|
|
|
513 {
|
|
|
514 if(is.factor(lsRet$frmeRaw[[sMetadata]]))
|
|
|
515 {
|
|
|
516 if(is.null(lsArgs$options$strBiplotShapeBy))
|
|
|
517 {
|
|
|
518 lsArgs$options$strBiplotShapeBy = sMetadata
|
|
|
519 if(!is.null(lsArgs$options$strBiplotColor))
|
|
|
520 {
|
|
|
521 break
|
|
|
522 }
|
|
|
523 }
|
|
|
524 }
|
|
|
525 if(is.numeric(lsRet$frmeRaw[[sMetadata]]))
|
|
|
526 {
|
|
|
527 if(is.null(lsArgs$options$strBiplotColor))
|
|
|
528 {
|
|
|
529 lsArgs$options$strBiplotColor = sMetadata
|
|
|
530 if(!is.null(lsArgs$options$strBiplotShapeBy))
|
|
|
531 {
|
|
|
532 break
|
|
|
533 }
|
|
|
534 }
|
|
|
535 }
|
|
|
536 }
|
|
|
537 }
|
|
|
538
|
|
|
539 #If a user defines a feature, make sure it is in the bugs/data indices
|
|
|
540 if(!is.null(lsFeaturesToPlot) || !is.null(lsArgs$options$strBiplotColor) || !is.null(lsArgs$options$strBiplotShapeBy))
|
|
|
541 {
|
|
|
542 lsCombinedFeaturesToPlot = unique(c(lsFeaturesToPlot,lsArgs$options$strBiplotColor,lsArgs$options$strBiplotShapeBy))
|
|
|
543 lsCombinedFeaturesToPlot = lsCombinedFeaturesToPlot[!is.null(lsCombinedFeaturesToPlot)]
|
|
|
544
|
|
|
545 # If bugs to plot were given then do not use the significant bugs from the MaAsLin output which is default
|
|
|
546 if(!is.null(lsFeaturesToPlot))
|
|
|
547 {
|
|
|
548 lsSigBugs = c()
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549 liSigMetadata = c()
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550 }
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551 liSigMetadata = unique(c(liSigMetadata,which(colnames(lsRet$frmeData) %in% setdiff(lsCombinedFeaturesToPlot, lsOriginalFeatureNames))))
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552 lsSigBugs = unique(c(lsSigBugs, intersect(lsCombinedFeaturesToPlot, lsOriginalFeatureNames)))
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553 }
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554
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555 # Convert bug names and metadata names to comma delimited strings
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556 vsBugs = paste(lsSigBugs,sep=",",collapse=",")
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557 vsMetadata = paste(colnames(lsRet$frmeData)[liSigMetadata],sep=",",collapse=",")
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558 vsMetadataByLevel = c()
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559
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560 # Possibly remove the NA levels depending on the preferences
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561 vsRemoveNA = c(NA, "NA", "na", "Na", "nA")
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562 if(!lsArgs$options$fPlotNA){ vsRemoveNA = c() }
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563 for(aiMetadataIndex in liSigMetadata)
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564 {
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565 lxCurMetadata = lsRet$frmeData[[aiMetadataIndex]]
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566 sCurName = names(lsRet$frmeData[aiMetadataIndex])
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567 if(is.factor(lxCurMetadata))
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568 {
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569 vsMetadataByLevel = c(vsMetadataByLevel,paste(sCurName, setdiff( levels(lxCurMetadata), vsRemoveNA),sep="_"))
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570 } else {
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571 vsMetadataByLevel = c(vsMetadataByLevel,sCurName)
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572 }
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573 }
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574
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575 # If NAs should not be plotted, make them the background color
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576 # Unless explicitly asked to be plotted
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577 sPlotNAColor = "white"
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578 if(lsArgs$options$fInvert){sPlotNAColor = "black"}
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579 if(lsArgs$options$fPlotNA){sPlotNAColor = "grey"}
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580 sLastMetadata = lsOriginalMetadataNames[max(which(lsOriginalMetadataNames %in% names(lsRet$frmeData)))]
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581
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582 # Plot biplot
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583 logdebug("PlotBiplot:Started")
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584 funcDoBiplot(
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585 sBugs = vsBugs,
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586 sMetadata = vsMetadataByLevel,
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587 sColorBy = lsArgs$options$strBiplotColor,
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588 sPlotNAColor = sPlotNAColor,
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589 sShapeBy = lsArgs$options$strBiplotShapeBy,
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590 sShapes = lsArgs$options$sShapes,
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591 sDefaultMarker = "16",
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592 sRotateByMetadata = lsArgs$options$sRotateByMetadata,
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593 dResizeArrow = lsArgs$options$dBiplotMetadataScale,
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594 sInputFileName = lsRet$frmeRaw,
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595 sLastMetadata = sLastMetadata,
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596 sOutputFileName = file.path(outputDirectory,paste(strBase,"-biplot.pdf",sep="")))
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597 logdebug("PlotBiplot:Stopped")
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598 }
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599 }
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600
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601 # This is the equivalent of __name__ == "__main__" in Python.
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602 # That is, if it's true we're being called as a command line script;
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603 # if it's false, we're being sourced or otherwise included, such as for
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604 # library or inlinedocs.
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605 if( identical( environment( ), globalenv( ) ) &&
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606 !length( grep( "^source\\(", sys.calls( ) ) ) ) {
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607 main( pArgs ) }
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