R/retrieveDataFromESets_CCR.R

In DoroChilds/TPP: Analyze thermal proteome profiling (TPP) experiments

retrieveDataFromESets_CCR <- function(data){
  ## Merge row annotations and fold changes from different expressionSets
  ## containing CCR data
  
  ## Initialize variables to prevent "no visible binding for global
  ## variable" NOTE by R CMD check:
  Protein_ID <- NULL
  
  ## 1. Preparation
  expNames<- names(data)
  list1=list2=list4=list3=list5=list6=list7=list8=list9=list10= 
    vector(mode="list", length=length(expNames))
  names(list1)=names(list2)=names(list4)=names(list3)=names(list5)=names(list6)=
    names(list7)=names(list8)=names(list9)=names(list10) = expNames
  
  ## 2. Iterate over all experiments and retrieve data
  for (en in expNames){
    setTmp <- data[[en]]
    
    ## Split annotation data (stored as featureData in the expressionSets) into
    ## a data frame of curve parameters, model information, fold changes, and
    ## annotation columns.
    fDat <- pData(featureData(setTmp))
    fNames <- colnames(fDat)
    fcNames <- sampleNames(setTmp)
    idsTmp <- featureNames(setTmp)
    
    ## Specify column names:
    cols1 <- drCurveParamNames(names = TRUE, info = FALSE)
    cols2 <- drCurveParamNames(names = FALSE, info = TRUE)
    cols3 <- paste(fcNames, "unmodified", sep="_")
    cols4 <- paste(fcNames, "median_normalized", sep="_")
    cols5 <- paste(fcNames, "transformed", sep="_")
    cols10 <- paste(fcNames, "normalized_to_lowest_conc", sep="_")
    cols6 <- "plot"
    cols9 <- c("compound_effect", "meets_FC_requirement")
    cols7 <- setdiff(fNames, c(cols1,cols2, cols3, cols4, cols5, cols6, cols9, cols10))
    
    ## Split featureData into separate data frames:
    df1  <- subset(fDat, select=cols1)
    df2  <- subset(fDat, select=cols2)
    df3  <- subset(fDat, select=cols3)
    df4  <- subset(fDat, select=cols4)
    df5  <- subset(fDat, select=cols5)
    df6  <- subset(fDat, select=cols6)
    df7  <- subset(fDat, select=cols7)
    df9  <- subset(fDat, select=cols9)
    df10  <- subset(fDat, select=cols10)
    
    ## Data frame with indicators which proteins were identified per experiment:
    df8 <- data.frame("protein_identified_in" = rep(TRUE, nrow(setTmp)))
    
    ## Append experiment id to all data frame columns to make them unique when 
    ## combined to big experiment-spanning results table:
    colnames(df1) <- paste(colnames(df1), en, sep="_")
    colnames(df2) <- paste(colnames(df2), en, sep="_")
    colnames(df3) <- paste(colnames(df3), en, sep="_")
    colnames(df4) <- paste(colnames(df4), en, sep="_")
    colnames(df5) <- paste(colnames(df5), en, sep="_")
    colnames(df6) <- paste(colnames(df6), en, sep="_")
    colnames(df7) <- paste(colnames(df7), en, sep="_")
    colnames(df8) <- paste(colnames(df8), en, sep="_")
    colnames(df9) <- paste(colnames(df9), en, sep="_")
    colnames(df10) <- paste(colnames(df10), en, sep="_")
    
    ## Add protein ID column so that the data frames of multiple experiment 
    ## (with different subsets of proteins detected in each experiment) can 
    ## later be merged together in a robust way:
    df1 <- data.frame(Protein_ID=idsTmp, df1, stringsAsFactors=FALSE)
    df2 <- data.frame(Protein_ID=idsTmp, df2, stringsAsFactors=FALSE)
    df3 <- data.frame(Protein_ID=idsTmp, df3, stringsAsFactors=FALSE)
    df4 <- data.frame(Protein_ID=idsTmp, df4, stringsAsFactors=FALSE)
    df5 <- data.frame(Protein_ID=idsTmp, df5, stringsAsFactors=FALSE)
    df6 <- data.frame(Protein_ID=idsTmp, df6, stringsAsFactors=FALSE)
    df7 <- data.frame(Protein_ID=idsTmp, df7, stringsAsFactors=FALSE)
    df8 <- data.frame(Protein_ID=idsTmp, df8, stringsAsFactors=FALSE)
    df9 <- data.frame(Protein_ID=idsTmp, df9, stringsAsFactors=FALSE)
    df10 <- data.frame(Protein_ID=idsTmp, df10, stringsAsFactors=FALSE)
    
    ## Store data frames of each experiment in a list. This will enable
    ## easy and robust merging using plyr::join_all.
    list1[[en]] <- df1
    list2[[en]] <- df2
    list3[[en]] <- df3
    list4[[en]] <- df4
    list5[[en]] <- df5
    list6[[en]] <- df6
    list7[[en]] <- df7
    list8[[en]] <- df8
    list9[[en]] <- df9
    list10[[en]] <- df10
  }
  merged1 <- arrange(join_all(list1, by="Protein_ID", type="full"), Protein_ID)
  merged2 <- arrange(join_all(list2, by="Protein_ID", type="full"), Protein_ID)
  merged3 <- arrange(join_all(list3, by="Protein_ID", type="full"), Protein_ID)
  merged4 <- arrange(join_all(list4, by="Protein_ID", type="full"), Protein_ID)
  merged5 <- arrange(join_all(list5, by="Protein_ID", type="full"), Protein_ID)
  merged6 <- arrange(join_all(list6, by="Protein_ID", type="full"), Protein_ID)
  merged7 <- arrange(join_all(list7, by="Protein_ID", type="full"), Protein_ID)
  merged8 <- arrange(join_all(list8, by="Protein_ID", type="full"), Protein_ID)
  merged9 <- arrange(join_all(list9, by="Protein_ID", type="full"), Protein_ID)
  merged10 <- arrange(join_all(list10, by="Protein_ID", type="full"), Protein_ID)
  
  ## Insert FALSE if a protein was not present in an experiment (instead of the
  ## NAs generated by the join_all function):
  for (en in expNames){
    name <- paste("protein_identified_in", en, sep="_")
    x    <- merged8[, name]
    x[is.na(x)] <- FALSE
    merged8[, name] <- x
  }
  
  ## Merge plot columns (columns of individual experiments can contain missing 
  ## values if experiment did not provide enough data for plotting):
  plotCols <- grep("plot", colnames(merged6), value = TRUE)
  if (length(plotCols)>0){
    allPlots <- data.frame(
      Protein_ID = merged6$Protein_ID, 
      plot = merge_cols(data = merged6[,plotCols], 
                        fun = paste,
                        collapse = '|')
    )
    merged6 <- join(merged6, allPlots, by="Protein_ID")
  }
  merged6 <- subset(merged6, select = !colnames(merged6) %in% plotCols)
  
  ## Return results:
  return(list(modelPars    = merged1,
              modelInfo    = merged2,
              fcOrig       = merged3,
              fcRefNorm    = merged10,
              fcNorm       = merged4,
              fcTransf     = merged5,
              plotCol      = merged6,
              otherAnnotDF = merged7,
              presenceDF   = merged8,
              transfDF     = merged9))
}