R/clean.data.r

In hubentu/PAIRADISE: PAIRADISE: Paired analysis of differential isoform expression

#' clean.data
#'
#'
#' Removes missing data and invalid pairs from the matched pair data
#' to be analyzed by PAIRADISE.
#'
#' @name clean.data
#' @param my.data Data frame containing grouped data to be analyzed.
#' @return The function clean.data returns a list containing the
#'     following entries:
#' \item{I1}{Group 1 isoform 1 counts for each replicate.}
#' \item{S1}{Group 1 isoform 2 counts for each replicate.}
#' \item{I2}{Group 2 isoform 1 counts for each replicate.}
#' \item{S2}{Group 2 isoform 2 counts for each replicate.}
#' \item{length_I}{Effective lengths of isoform 1.}
#' \item{length_S}{Effective lengths of isoform 2.}
#' \item{exonList}{IDs of the exons/events.}
#' \item{nExon}{Number of exons/events.}
#' \item{M}{Vector containing the number of
#'     replicates per exon/event.}
#' @details The data frame has 7 columns, arranged as follows:
#' Column 1 contains the ID of the exons/events.
#' Column 2 contains counts of isoform 1 corresponding to the first group.
#' Column 3 contains counts of isoform 2 corresponding to the first group.
#' Column 4 contains counts of isoform 1 corresponding to the
#'     second group.
#' Column 5 contains counts of isoform 2 corresponding to the second group.
#' Replicates in columns 2-5 should be separated by commas, e.g. 1623,432,6 for three replicates.
#' Column 6 contains the effective length of isoform 1.
#' Column 7 contains the effective length of isoform 2.
#' @keywords internal
clean.data <- function(my.data) {
    
    ## Unpack data list
    data.list <- load.data(my.data)
    I1.raw <- data.list$I1.raw
    S1.raw <- data.list$S1.raw
    I2.raw <- data.list$I2.raw
    S2.raw <- data.list$S2.raw
    length_I.raw <- data.list$length_I.raw
    length_S.raw <- data.list$length_S.raw
    exonList.raw <- data.list$exonList.raw
    nExon.raw <- data.list$nExon.raw
    M <- data.list$M
    
    ## Clean missing/invalid data
    miss_total <- c()
    missIndex <- 1
    for (iExon in seq_len(nExon.raw)) {
        
        ## Find total number of NA values.
        I1.miss <- sum(is.na(I1.raw[[iExon]]))
        S1.miss <- sum(is.na(S1.raw[[iExon]]))
        I2.miss <- sum(is.na(I2.raw[[iExon]]))
        S2.miss <- sum(is.na(S2.raw[[iExon]]))
        
        valid <- (I1.raw[[iExon]] + S1.raw[[iExon]] != 0) &
            (I2.raw[[iExon]] + S2.raw[[iExon]] != 0)
        valid[is.na(valid)] <- FALSE
        
        if (I1.miss == M[iExon] | S1.miss == M[iExon] |
            I2.miss == M[iExon] | S2.miss == M[iExon] |
            sum(valid) == 0) {
            miss_total[missIndex] <- iExon
            missIndex <- missIndex + 1
        }
    }
    
    ## If all the values are missing, skip this exon/event.
    if (length(miss_total) > 0) {
        I1.raw <- I1.raw[-miss_total]
        S1.raw <- S1.raw[-miss_total]
        
        I2.raw <- I2.raw[-miss_total]
        S2.raw <- S2.raw[-miss_total]
        
        length_I.raw <- length_I.raw[-miss_total]
        length_S.raw <- length_S.raw[-miss_total]
        
        exonList.raw <- exonList.raw[-miss_total]
        
        M <- M[-miss_total]
        
    }
    
    nExon <- length(I1.raw)
    output <- list(I1.raw, S1.raw, I2.raw, S2.raw,
                   length_I.raw, length_S.raw, exonList.raw, 
                   nExon, M)
    names(output) <- c("I1", "S1", "I2", "S2",
                       "length_I", "length_S", "exonList", 
                       "nExon", "M")
    
    output
    
}