comparison NmrNormalization_script.R @ 0:e1b29d705286 draft

planemo upload for repository https://github.com/workflow4metabolomics/nmr_normalization commit 0a2ec9e30fbf7690a80695c751e6ea432b10a759-dirty

0:e1b29d705286

#################################################################################################################
# SPECTRA NORMALIZATION FROM BUCKETED AND INTEGRATED SPECTRA                                                    #
# User : Galaxy                                                                                                 #
# Original data : --                                                                                            #
# Starting date : 20-10-2014                                                                                    #
# Version 1 : 27-01-2015                                                                                        #
# Version 2 : 27-02-2015                                                                                        #
#                                                                                                               #
# Input files:                                                                                                  #
#   - Data matrix containing bucketed and integrated spectra to normalize                                       #
#   - Sample metadata matrix containing at least biological factor of interest                                  #
#   - Scaling method: Total intensity/Probabilistic Quotient Normalization                                      #
#   - Control group: name of control to compute median reference spectra                                        #
#   - Graph: normalization result representation                                                                #
#################################################################################################################
NmrNormalization <- function(dataMatrix,scalingMethod=c("None","Total","PQN","BioFactor"),sampleMetadata=NULL,
                             bioFactor=NULL,ControlGroup=NULL,graph=c("None","Overlay","One_per_individual"),
                             nomFichier=NULL,savLog.txtC=NULL)
{

  ## Option
  ##---------------
  strAsFacL <- options()$stringsAsFactors
  options(stingsAsFactors = FALSE)
  options(warn = -1)
  
  
  ## Constants
  ##---------------
  topEnvC <- environment()
  flgC <- "\n"
  
  ## Log file (in case of integration into Galaxy)
  ##----------------------------------------------
  if(!is.null(savLog.txtC))
    sink(savLog.txtC, append = TRUE)
  
  ## Functions definition
  ##---------------------  
  #################################################################################################################
  # Total intensity normalization
  # Input parameters
  #   - data : bucketed spectra (rows=buckets; columns=samples)
  #################################################################################################################  
  NmrBrucker_total <- function(data)
  {
    # Total intensity normalization
    data.total <- apply(data,2,sum)
    data.normalized <- data[,1]/data.total[1]
    for (i in 2:ncol(data))
      data.normalized <- cbind(data.normalized,data[,i]/data.total[i]) 
    colnames(data.normalized) <- colnames(data)
    rownames(data.normalized) <- rownames(data)
    return(data.normalized)
  }

  
  #################################################################################################################
  # Biological factor normalization
  # Input parameters
  #   - data : bucketed spectra (rows=buckets; columns=samples)
  #   - sampleMetadata : dataframe with biological factor of interest measured for each invidual
  #   - bioFactor : name of the column cotaining the biological factor of interest
  #################################################################################################################
  NmrBrucker_bioFact <- function(data,sampleMetadata,bioFactor)
  {
    # Total intensity normalization
    data.normalized <- data[,1]/bioFactor[1]
    for (i in 2:ncol(data))
      data.normalized <- cbind(data.normalized,data[,i]/bioFactor[i]) 
    colnames(data.normalized) <- colnames(data)
    rownames(data.normalized) <- rownames(data)
    return(data.normalized)
  }
  
  
  #################################################################################################################
  # Probabilistic quotient normalization (PQN)
  # Input parameters
  #   - data : bucketed spectra (rows=buckets; columns=samples)
  #   - sampleMetadata : dataframe with treatment group of inviduals
  #   - pqnFactor : number of the column cotaining the biological facor of interest
  #   - nomControl : name of the treatment group
  #################################################################################################################
  NmrBrucker_pqn <- function(data,sampleMetadata,pqnFactor,nomControl)
  {
    # Total intensity normalization
    data.total <- apply(data,2,sum)
    data.normalized <- data[,1]/data.total[1]
    for (i in 2:ncol(data))
      data.normalized <- cbind(data.normalized,data[,i]/data.total[i]) 
    colnames(data.normalized) <- colnames(data)
    rownames(data.normalized) <- rownames(data)
    
    # Reference spectrum
    # Recuperation spectres individus controle
    control.spectra <- data.normalized[,sampleMetadata[,pqnFactor]==nomControl]
    spectrum.ref <- apply(control.spectra,1,median)
    
    # Ratio between normalized and reference spectra
    data.normalized.ref <- data.normalized/spectrum.ref
    
    # Median ratio
    data.normalized.ref.median <- apply(data.normalized.ref,1,median)
    
    # Normalization
    data.normalizedPQN <- data.normalized[,1]/data.normalized.ref.median
    for (i in 2:ncol(data))
      data.normalizedPQN <- cbind(data.normalizedPQN,data.normalized[,i]/data.normalized.ref.median)
    colnames(data.normalizedPQN) <- colnames(data)
    rownames(data.normalizedPQN) <- rownames(data)
    
    return(data.normalizedPQN)
  }
  
  
  ## Tests
  if (scalingMethod=="QuantitativeVariable")
  {
    if(mode(sampleMetadata[,bioFactor]) == "character")
       bioFact <- factor(sampleMetadata[,bioFactor])
    else
       bioFact <- sampleMetadata[,bioFactor]
  }
  
  ## Spectra scaling depending on the user choice
  if (scalingMethod == "None")
  {
    NormalizedBucketedSpectra <- dataMatrix
  }
  else if (scalingMethod == "Total")
  {
    NormalizedBucketedSpectra <- NmrBrucker_total(dataMatrix)    
  }
  else if (scalingMethod == "PQN")
  {
    NormalizedBucketedSpectra <- NmrBrucker_pqn(dataMatrix,sampleMetadata,bioFactor,ControlGroup)
  }
  else if (scalingMethod == "QuantitativeVariable")
  {
    NormalizedBucketedSpectra <- NmrBrucker_bioFact(dataMatrix,sampleMetadata,bioFact)
  }
  
  # Graphic
  if (graph != "None")
  {
    # Graphic Device opening
    pdf(nomFichier,onefile=TRUE)
    if (graph == "Overlay")
    {
      ymax <- max(NormalizedBucketedSpectra)
      plot(1:length(NormalizedBucketedSpectra[,1]),NormalizedBucketedSpectra[,1],ylim=c(0,ymax),
            type='l',col=1,xlab="Chemical shift",ylab="Intensity")
      for (i in 2:ncol(NormalizedBucketedSpectra))
      {
        spectre <- NormalizedBucketedSpectra[,i]
        lines(spectre,col=i)
      }
    }
    else
    {
      for (i in 1:ncol(NormalizedBucketedSpectra))
      {
        plot(1:length(NormalizedBucketedSpectra[,i]),NormalizedBucketedSpectra[,i],type='l',col=1,
             xlab="Chemical shift",ylab="Intensity")
      }
    }
    dev.off()
  }
  
  # Output datasets creation
  if (scalingMethod == "None" || scalingMethod == "Total")
  {
      sampleMetadata <- data.frame(1:ncol(NormalizedBucketedSpectra))
      rownames(sampleMetadata) <- colnames(NormalizedBucketedSpectra)
      colnames(sampleMetadata) <- "SampleOrder"
  }
   
  variableMetadata <- data.frame(1:nrow(NormalizedBucketedSpectra))
  rownames(variableMetadata) <- rownames(NormalizedBucketedSpectra)
  colnames(variableMetadata) <- "VariableOrder"

  return(list(NormalizedBucketedSpectra,sampleMetadata,variableMetadata))

}