R/getNSetterFuns.R

In c-mertes/FRASER: Find RAre Splicing Events in RNA-Seq Data

#' Getter/Setter functions
#' 
#' This is a collection of small accessor/setter functions for easy access to
#' the values within the FRASER model.
#' 
#' @param fds An FraserDataSet object.
#' @param type The type of psi (psi5, psi3 or theta)
#' @param byGroup If TRUE, aggregation by donor/acceptor site will be done.
#' @param dist Distribution for which the p-values should be extracted.
#' @param level Indicates if the retrieved p values should be adjusted on the 
#'          donor/acceptor site-level (default) or if unadjusted junction-level 
#'          p values should be returned.
#' @param subsetName The name of a subset of genes of interest for which FDR 
#'          corrected pvalues were previously computed. 
#'          Default is NULL (using transcriptome-wide FDR corrected pvalues).
#' @param filters A named list giving the filters that were applied for masking 
#'          during p value correction. Used for storing and retrieving the 
#'          correct set of requested p values.
#' @param value The new value to be assigned.
#' @param all Logical value indicating whether \code{hyperParams(fds)} should 
#'          return the results of all evaluated parameter combinations or only 
#'          for the optimal parameter combination.
#' @param ... Internally used parameters. 
#' @return A (delayed) matrix or vector dependent on the type of data retrieved.
#' 
#' @name getter_setter_functions
#' @rdname getter_setter_functions
#' @aliases zScore, pVals, padjVals, rho, bestQ
#' 
#' @examples 
#' fds <- createTestFraserDataSet()
#' 
#' # should assays be saved as hdf5? 
#' dontWriteHDF5(fds)
#' dontWriteHDF5 <- TRUE
#' 
#' # get/set the splice metric for which results should be retrieved 
#' currentType(fds) <- "jaccard"
#' currentType(fds)
#' 
#' # get fitted parameters
#' bestQ(fds)
#' predictedMeans(fds)
#' rho(fds)
#' 
#' # get statistics
#' pVals(fds)
#' padjVals(fds)
#' 
#' # zscore not calculated by default
#' fds <- calculateZscore(fds, type="jaccard")
#' zScores(fds)
#' 
#' # set and get pseudocount
#' pseudocount(4L)
#' pseudocount()
#' 
#' # retrieve or set a mask to exclude certain junctions in the fitting step
#' featureExclusionMask(fds, type="jaccard") <- sample(
#'         c(FALSE, TRUE), nrow(mcols(fds, type="jaccard")), replace=TRUE)
#' featureExclusionMask(fds, type="jaccard")
#' 
#' # controlling the verbosity level of the output of some algorithms
#' verbose(fds) <- 2
#' verbose(fds)
NULL

#' @describeIn getter_setter_functions Retrieves a logical vector indicating
#' for each junction whether it is included or excluded during the fitting
#' procedure.
#' @aliases featureExclusionMask, `featureExclusionMask<-`
#' @export featureExclusionMask
featureExclusionMask <- function(fds, type=currentType(fds)){
    ans <- rep(TRUE, nrow(mcols(fds, type=type)))
    if(paste0('featureExclude_', type) %in% colnames(mcols(fds, type=type))){
        ans <- mcols(fds, type=type)[[paste0('featureExclude_', type)]]
    }
    # TODO names(ans, type=type) <- rownames(fds, type=type)
    return(ans)
}

#' @describeIn getter_setter_functions To remove certain junctions from 
#' being used in the train step of the encoding dimension we can set the 
#' \code{featureExclusion} vector to \code{FALSE}. This can be helpfull 
#' if we have local linkage between features which we do not want to 
#' model by the autoencoder.
#' @export "featureExclusionMask<-"
`featureExclusionMask<-` <- function(fds, type=currentType(fds), value){
    if(isScalarLogical(value)){
        value <- rep(value, nrow(mcols(fds, type=type)))
    }
    mcols(fds, type=type)[[paste0('featureExclude_', type)]] <- value
    return(fds)
}

#' @rdname counts
#' @export
K <- function(fds, type=currentType(fds)){
    K <- counts(fds, type=type, side="ofInterest")
    return(K);
}

#' @rdname counts
#' @export
N <- function(fds, type=currentType(fds)){
    N <- K(fds, type=type) + counts(fds, type=type, side="other")
    return(N);
}

x <- function(fds, type=currentType(fds), all=FALSE,
                    noiseAlpha=currentNoiseAlpha(fds), center=TRUE){
    K <- K(fds, type=type)
    N <- N(fds, type=type)

    # compute logit ratio with pseudocounts
    x <- t((K + pseudocount())/(N + (2*pseudocount())))
    x <- qlogis(x)
    
    if(any(is.infinite(x))){
        x[is.infinite(x) & x > 0] <- NA
        x[is.na(x)] <- max(x, na.rm=TRUE) + 1
    }

    # corrupt x if required
    if(!is.null(noiseAlpha)){
        noise <- noise(fds, type=type)
        if(is.null(noise)){
            noise <- matrix(rnorm(prod(dim(x))), ncol=ncol(x), nrow=nrow(x))
            noise(fds, type=type) <- noise
        }
        x <- x + t(colSds(x) * noiseAlpha * t(noise))
    }

    if(isFALSE(all)){
        x <- x[,featureExclusionMask(fds, type=type)]
    }
    if(isTRUE(center)){
        x <- t(t(x) - colMeans2(x))
    }

    return(x)
}

H <- function(fds, type=currentType(fds), noiseAlpha=NULL){
    x(fds, all=FALSE, type=type, noiseAlpha=noiseAlpha) %*% E(fds, type=type)
}

`D<-` <- function(fds, type=currentType(fds), value){
    if(!is.matrix(value)){
        value <- matrix(value, nrow=nrow(fds))
    }
    metadata(fds)[[paste0('D_', type)]] <- value
    return(fds)
}

D <- function(fds, type=currentType(fds)){
    return(metadata(fds)[[paste0('D_', type)]])
}

`E<-` <- function(fds, type=currentType(fds), value){
    if(!is.matrix(value)){
        value <- matrix(value, nrow=sum(featureExclusionMask(fds, type=type)))
    }
    metadata(fds)[[paste0('E_', type)]] <- value
    return(fds)
}

E <- function(fds, type=currentType(fds)){
    return(metadata(fds)[[paste0('E_', type)]])
}

`b<-` <- function(fds, type=currentType(fds), value){
    mcols(fds, type=type)[[paste0('b_', type)]] <- value
    return(fds)
}

b <- function(fds, type=currentType(fds)){
    return(mcols(fds, type=type)[[paste0('b_', type)]])
}

`rho<-` <- function(fds, type=currentType(fds), value){
    mcols(fds, type=type)[[paste0('rho_', type)]] <- value
    return(fds)
}

#' @describeIn getter_setter_functions Returns the fitted rho values for the 
#' beta-binomial distribution
#' @export
rho <- function(fds, type=currentType(fds)){
    return(mcols(fds, type=type)[[paste0('rho_', type)]])
}

predictMu <- function(fds, type=currentType(fds), noiseAlpha=NULL){
    y <- predictY(fds, type=type, noiseAlpha=noiseAlpha)
    mu <- predictMuCpp(y)
    return(t(mu))
}

predictY <- function(fds, type=currentType(fds), noiseAlpha=NULL){
    D <- D(fds, type=type)
    b <- b(fds, type=type)
    H <- H(fds, type=type, noiseAlpha=noiseAlpha)

    y <- predictYCpp(as.matrix(H), D, b)

    return(t(y))
}


`setAssayMatrix<-` <- function(fds, name, type=currentType(fds), ..., value){
    if(!is.matrix(value)){
        value <- matrix(value, ncol=ncol(fds), nrow=nrow(mcols(fds, type=type)))
    }
    if(is.null(colnames(value))){
        colnames(value) <- colnames(fds)
    }
    if(is.null(rownames(value))){
        rownames(value) <- rownames(counts(fds, type=type))
    }
    if(missing(name)){
        name <- type
    } else {
        name <- paste(name, type, sep="_")
    }
    assay(fds, name, ...) <- value
    fds
}

getAssayMatrix <- function(fds, name, type=currentType(fds), byGroup=FALSE){
    if(missing(name)){
        name <- type
    } else {
        name <- paste(name, type, sep="_")
    }
    ans <- assay(fds, name)
    idx <- TRUE
    if(isTRUE(byGroup)){
        idx <- !duplicated(getSiteIndex(fds, type=type))
    }
    ans[idx,]
}

#' @describeIn getter_setter_functions This returns the calculated z-scores.
#' @export
zScores <- function(fds, type=currentType(fds), byGroup=FALSE, ...){
    ans <- getAssayMatrix(fds, name='zScores', type=type)
    if(isTRUE(byGroup)){
        ans <- getAbsMaxByGroup(fds, type, ans, ...)
    }
    ans
}

`zScores<-` <- function(fds, type=currentType(fds), ..., value){
    setAssayMatrix(fds, name="zScores", type=type, ...) <- value
    return(fds)
}
#' @describeIn getter_setter_functions This returns the calculated p-values.
#' @export
pVals <- function(fds, type=currentType(fds), level="site", 
                    filters=list(), dist="BetaBinomial", ...){
    level <- match.arg(level, choices=c("site", "junction", "gene"))
    dist <- match.arg(dist, choices=c("BetaBinomial", "Binomial", "Normal"))
    aname <- paste0("pvalues", dist)
    if(level == "junction"){
        if( paste(paste0(aname, "_junction"), type, sep="_") %in% 
            assayNames(fds)){
            aname <- paste0(aname, "_junction")
        } else{
            warning("Did not find junction-level p values. ",
                    "Using site-level p values instead.")
        }
    } else{ 
        aname <- ifelse(level == "gene", paste0(aname, "_gene"), aname)
        # add information on used filters
        if(is.null(names(filters))){
            filters <- list(rho=1)
        }
        for(n in sort(names(filters))){
            aname_new <- paste0(aname, "_", n, filters[[n]])
            if(n == "rho" && filters[[n]] == 1){
                if(any(grepl(aname_new, assayNames(fds))) ||
                        any(grepl(aname_new, names(metadata(fds))))){
                    aname <- aname_new
                }
            }else{
                aname <- aname_new
            }
        }
        if(level == "gene"){
            if(!paste(aname, type, sep="_") %in% names(metadata(fds))){
                stop("Did not find gene-level p values. ",
                        "Please compute them first.")
            }
            return(metadata(fds)[[paste(aname, type, sep="_")]])
        }
    }
    
    getAssayMatrix(fds, aname, type=type, ...)
}

`pVals<-` <- function(fds, type=currentType(fds), level="site",
                    filters=list(),
                    dist="BetaBinomial", ..., value){
    level <- match.arg(level, choices=c("site", "junction", "gene"))
    dist <- match.arg(dist, choices=c("BetaBinomial", "Binomial", "Normal"))
    aname <- paste0("pvalues", dist)
    if(level == "junction"){
        aname <- paste0(aname, "_junction")
        setAssayMatrix(fds, name=aname, type=type, ...) <- value
        return(fds)
    } else if(level == "gene"){
        aname <- paste0(aname, "_gene")
    }
    # add information on used filters
    for(n in sort(names(filters))){
        aname <- paste0(aname, "_", n, filters[[n]])
    }
    
    if(level == "gene"){
        if(is.null(rownames(value))){
            stop("Missing rownames when storing gene-level pvalues.")
        }
        metadata(fds)[[paste(aname, type, sep="_")]] <- value    
    } else{
        setAssayMatrix(fds, name=aname, type=type, ...) <- value
    }
    return(fds)
}

#' @describeIn getter_setter_functions This returns the adjusted p-values.
#' @export
padjVals <- function(fds, type=currentType(fds), dist=c("BetaBinomial"), 
                    level="site", subsetName=NULL, filters=list(), ...){
    level <- match.arg(level, choices=c("site", "gene"))
    dist <- match.arg(dist, choices=c("BetaBinomial", "Binomial", "Normal"))
    aname <- paste0("padj", dist)
    aname <- ifelse(level == "gene", paste0(aname, "_gene"), aname)
    if(!is.null(subsetName)){
        aname <- paste0(aname, "_", subsetName)
    }
    # add information on used filters
    if(is.null(names(filters))){
        filters <- list(rho=1)
    }
    for(n in sort(names(filters))){
        aname_new <- paste0(aname, "_", n, filters[[n]])
        if(n == "rho" && filters[[n]] == 1){
            if(any(grepl(aname_new, assayNames(fds))) ||
                    any(grepl(aname_new, names(metadata(fds))))){
                aname <- aname_new
            }
        }else{
            aname <- aname_new
        }
    }
    if(level == "gene"){
        if(!paste(aname, type, sep="_") %in% names(metadata(fds))){
            stop("Did not find gene-level padj values. ",
                "Please compute them first.")
        }
        return(metadata(fds)[[paste(aname, type, sep="_")]])
    }
    return(getAssayMatrix(fds, aname, type=type, ...))
}

`padjVals<-` <- function(fds, type=currentType(fds), level="site",
                    dist="BetaBinomial", subsetName=NULL, filters=list(), ..., 
                    value){
    level <- match.arg(level, choices=c("site", "gene"))
    dist <- match.arg(dist, choices=c("BetaBinomial", "Binomial", "Normal"))
    aname <- paste0("padj", dist)
    aname <- ifelse(level == "gene", paste0(aname, "_gene"), aname)
    if(!is.null(subsetName)){
        aname <- paste0(aname, "_", subsetName)
    }
    # add information on used filters
    for(n in sort(names(filters))){
        aname <- paste0(aname, "_", n, filters[[n]])
    }
    if(level == "gene"){
        if(is.null(rownames(value))){
            stop("Missing rownames when storing gene-level pvalues.")
        }
        metadata(fds)[[paste(aname, type, sep="_")]] <- value
    } else{
        setAssayMatrix(fds, name=aname, type=type, ...) <- value
    }
    return(fds)
}

#' @describeIn getter_setter_functions This returns the names of FDR subsets 
#'     for which adjusted p values have been calculated.
#' @export
availableFDRsubsets <- function(fds){
    ans <- metadata(fds)[["FDRsubsets"]]
    return(ans)
}

`availableFDRsubsets<-` <- function(fds, value){
    metadata(fds)[["FDRsubsets"]] <- value
    return(fds)
}

`addToAvailableFDRsubsets<-` <- function(fds, value){
    if(!isScalarCharacter(value)){
        stop("The assigned value needs to be a scalar character.")
    }
    ans <- metadata(fds)[["FDRsubsets"]]
    if(is.null(ans)){
        metadata(fds)[["FDRsubsets"]] <- value
    } else{
        metadata(fds)[["FDRsubsets"]] <- unique(c(ans, value))
    }
    return(fds)
}

#' @describeIn getter_setter_functions This returns the fitted mu (i.e. psi) 
#' values.
#' @export
predictedMeans <- function(fds, type=currentType(fds)){
    return(getAssayMatrix(fds, name="predictedMeans", type=type))
}

`predictedMeans<-` <- function(fds, type=currentType(fds), ..., value){
    setAssayMatrix(fds, name="predictedMeans", type=type, ...) <- value
    return(fds)
}

#' @describeIn getter_setter_functions Returns the difference between the 
#' observed and the fitted psi values.
#' @export
deltaPsiValue <- function(fds, type=currentType(fds)){
    return(assay(fds, type) - predictedMeans(fds, type=type))
}


#' @describeIn getter_setter_functions Returns the psi type that is used 
#' within several methods in the FRASER package (defaults to jaccard).
#' @export
currentType <- function(fds){
    curType <- metadata(fds)[['currentType']]
    if(is.null(curType)){
        curType <- "jaccard"
    }
    return(curType)
}

#' @describeIn getter_setter_functions Sets the psi type that is to be used 
#' within several methods in the FRASER package.
#' @export 
`currentType<-` <- function(fds, value){
    stopifnot(isScalarCharacter(whichPSIType(value)))
    metadata(fds)[['currentType']] <- whichPSIType(value)
    return(fds)
}

#' @describeIn getter_setter_functions Returns the splice metrics that will be
#' fitted (defaults to jaccard, used within several methods in the 
#' FRASER package).
#' @export
fitMetrics <- function(fds){
    metrics <- metadata(fds)[['fit_metrics']]
    if(is.null(metrics)){
        metrics <- "jaccard"
    }
    return(metrics)
}

#' @describeIn getter_setter_functions Sets the splice metrics that will be 
#' fitted (used within several methods in the FRASER package).
#' @export 
`fitMetrics<-` <- function(fds, value){
    stopifnot(is.character(whichPSIType(value)))
    metadata(fds)[['fit_metrics']] <- whichPSIType(value)
    return(fds)
}

#' @describeIn getter_setter_functions Sets and returns the pseudo count used 
#' within the FRASER fitting procedure.
#' @export
pseudocount <- function(value=NULL){
    # return if not provided
    if(is.null(value)){
        ans <- options()[['FRASER.pseudoCount']]
        if(isScalarNumeric(ans)){
            return(ans)
        }
        return(1)
    }

    # set pseudo count if provided
    stopifnot(isScalarNumeric(value))
    stopifnot(value >= 0)
    value <- as.numeric(value)
    options('FRASER.pseudoCount'=value)
    devNULL <- .setPseudoCount(value)
    stopifnot(value == devNULL)

    invisible(value)
}

currentNoiseAlpha <- function(fds){
    return(metadata(fds)[['noiseAlpha']])
}

`currentNoiseAlpha<-` <- function(fds, value){
    metadata(fds)[['noiseAlpha']] <- value
    return(fds)
}

noise <- function(fds, type=currentType(fds)){
    return(t(getAssayMatrix(fds, name="noise", type=type)))
}

`noise<-` <- function(fds, type=currentType(fds), HDF5=FALSE, ..., value){
    if(!is.matrix(value)){
        value <- matrix(value, nrow=nrow(mcols(fds, type=type)), ncol=ncol(fds))
    }
    setAssayMatrix(fds, name='noise', type=type, HDF5=HDF5) <- t(value)
    return(fds)
}

#' @describeIn getter_setter_functions This returns the results of the 
#' hyperparameter optimization NULL if the hyperparameter 
#' opimization was not run yet.
#' @export
hyperParams <- function(fds, type=currentType(fds), all=FALSE){
    ans <- metadata(fds)[[paste0("hyperParams_", type)]]
    if(is.null(ans)){
        return(ans)
    }
    if(isFALSE(all)){
        ans <- ans[aroc == max(aroc)][1]
    }
    ans
}

`hyperParams<-` <- function(fds, type=currentType(fds), value){
    metadata(fds)[[paste0("hyperParams_", type)]] <- value
    return(fds)
}

#' @describeIn getter_setter_functions This returns the optimal size of the 
#' latent space according to the hyperparameter optimization or a simple 
#' estimate of about a tenth of the number of samples if the hyperparameter 
#' opimization was not run yet.
#' @export
bestQ <- function(fds, type=currentType(fds)){
    ans <- hyperParams(fds, type=type)[1,q]
    if(is.null(ans) || is.na(ans)){
        warnings("Please set q by estimating it correctly.")
        ans <- min(100, max(2, round(ncol(fds)/10)))
    }
    return(as.integer(ans))
}

bestNoise <- function(fds, type=currentType(fds)){
    ans <- hyperParams(fds, type=type)[1,noise]
    if(is.null(ans)){
        warnings("Please set noise by estimating it correctly.")
        ans <- 1
    }
    as.numeric(as.character(ans))
}

#' @describeIn getter_setter_functions Gets the current value of whether the 
#' assays should be stored as hdf5 files.
#' @export
dontWriteHDF5 <- function(fds){
    ans <- metadata(fds)[['dontWriteHDF5']]
    if(is.null(ans)){
        ans <- FALSE
    }
    return(ans)
}

#' @describeIn getter_setter_functions Sets whether the assays should be stored 
#' as hdf5 files.
#' @export
`dontWriteHDF5<-` <- function(fds, value){
    metadata(fds)[['dontWriteHDF5']] <- isTRUE(value)
    return(fds)
}

getTrueOutliers <- function(fds, type=currentType(fds), byGroup=FALSE, ...){
    ans <- getAssayMatrix(fds, "trueOutliers", type)
    if(isTRUE(byGroup)){
        ans <- getAbsMaxByGroup(fds, type, ans, ...)
    }
    
    # remove secondary injections -> -1/0/+1 instead of -2/-1/0/+1/+2
    pmin(pmax(ans, -1), 1)
}

getTrueDeltaPsi <- function(fds, type=currentType(fds), byGroup=FALSE, ...){
    ans <- getAssayMatrix(fds, "trueDeltaPSI", type)
    if(isTRUE(byGroup)){
        ans <- getAbsMaxByGroup(fds, type, ans, ...)
    }
    ans
}

getAbsMaxByGroup <- function(fds, type=currentType(fds), mat, index=NULL, 
                                BPPARAM=bpparam()){
    if(is.null(index)){
        index <- getSiteIndex(fds, type)
    }

    dt <- cbind(data.table(id=index), as.data.table(mat))
    values <- matrix(ncol=ncol(mat), unlist(bplapply(colnames(mat), 
            BPPARAM=BPPARAM,
            function(i){
                    dttmp <- dt[,.(.I, id, value=get(i), abs=abs(get(i)))]
                    dttmp[,maxVal:=value[abs == max(abs)][1], by=id]
                    dttmp[!duplicated(id)][order(I)][,value]
            })))
    
    colnames(values) <- colnames(mat)
    rownames(values) <- index[!duplicated(index)]
    return(values)
}

getByGroup <- function(fds, type=currentType(fds), value){
    index <- getSiteIndex(fds, type)
    idx   <- !duplicated(index)
    return(value[idx,])
}

getDeltaPsi <- function(fds, type=currentType(fds), byGroup=FALSE, ...){
    mu <- predictedMeans(fds, type)
    dataPsi <- (K(fds, type) + pseudocount())/(N(fds, type) + 2*pseudocount())
    deltaPSI <- dataPsi - mu
    if(isTRUE(byGroup)){
        deltaPSI <- getAbsMaxByGroup(fds, type=psiType, mat=deltaPSI, ...)
    }
    deltaPSI
}


# calculate FRASER weights
calcFraserWeights <- function(fds, psiType=currentType(fds)){
    k <- as.matrix(K(fds, psiType))
    n <- as.matrix(N(fds, psiType))
    mu <- t(predictMu(fds, psiType))
    rho <- rho(fds, psiType)
    # dataPsi <- plogis(t(
    #         x(fds, type=psiType, all=TRUE, center=FALSE, noiseAlpha=NULL)))
    dataPsi <- k / n

    # pearson residuals for BB
    # on counts of success k
    # r <- ((k+pseudocount()) - (n+2*pseudocount()) * mu) / sqrt(
    #       (n+2*pseudocount()) * mu * (1-mu) *
    #       (1+((n+2*pseudocount())-1)*rho))
    # on probability of success mu
    r <- (dataPsi - mu) / sqrt(
            # mu * (1-mu) * (1+((n+2*pseudocount())-1)*rho) /
            # (n+2*pseudocount()))
            mu * (1-mu) * (1+(n-1)*rho) / n
        )

    # weights according to Huber function (as in edgeR)
    c <- 1.345; # constant, as suggested in edgeR paper
    w <- ifelse(abs(r) > c, c/abs(r) , 1)

    # set weights to 0 if NA (i.e. N=0)
    w[is.na(w)] <- 0

    return(w)
}

# get FRASER weights
weights <- function(fds, type=currentType(fds)){
    return(getAssayMatrix(fds, "weights", type))
}

# set FRASER weights
`weights<-` <- function(fds, type=currentType(fds), ..., value){
    setAssayMatrix(fds, name="weights", type=type, ...) <- value
    return(fds)
}

getIndexFromResultTable <- function(fds, resultTable){
    type <- as.character(resultTable$type)
    target <- makeGRangesFromDataFrame(resultTable)
    if(type == "theta"){
        gr <- granges(asSE(nonSplicedReads(fds)))
    } else {
        gr <- granges(asSE(fds))
    }

    hits <- findOverlaps(target, gr, type="equal")
    ov <- to(hits)
    if(!isScalarInteger(ov)){
        stop("Can not find the given range within the FRASER object.")
    }
    ov
}

getPlottingDT <- function(fds, axis=c("row", "col"), type=currentType(fds), 
                    result=NULL, idx=NULL, aggregate=FALSE, pvalLevel="site", 
                    Ncpus=3, geneColumn="hgnc_symbol", subsetName=NULL, ...){
    if(!is.null(result)){
        type <- as.character(result$type)
        idx  <- getIndexFromResultTable(fds, result)
    }

    axis <- match.arg(axis)
    idxrow <- idx
    idxcol <- TRUE
    if(axis == "col"){
        idxcol <- idx
        if(is.character(idx)){
            idxcol <- colnames(fds) %in% idx
        }
        idxrow <- TRUE
    }

    k <- K(fds, type)[idxrow, idxcol]
    n <- N(fds, type)[idxrow, idxcol]
    
    spliceID <- getSiteIndex(fds, type=type)[idxrow]
    feature_names <- rownames(mcols(fds, type=type))[idxrow]
    if(geneColumn %in% colnames(mcols(fds, type=type))){
        feature_names <- mcols(fds, type=type)[idxrow, geneColumn]
    }
    if(is.null(feature_names)){
        feature_names <- as.character(seq_row(mcols(fds, type=type)))[idxrow]
    }
    
    pvalLevel <- match.arg(pvalLevel, choices=c("site", "junction"))
    if(isTRUE(aggregate)){
        pvalLevel <- "site"
    }

    dt <- data.table(
            idx       = idx,
            sampleID  = factor(as.character(colnames(K(fds, type))[idxcol])),
            spliceID  = factor(spliceID),
            featureID = factor(feature_names),
            type      = factor(type),
            k         = c(k),
            n         = c(n),
            pval      = c(pVals(fds, type=type, 
                                level=pvalLevel)[idxrow, idxcol]),
            padj      = c(padjVals(fds, type=type, 
                                    subsetName=subsetName)[idxrow, idxcol]),
            obsPsi    = c(k/n),
            predPsi   = c(predictedMeans(fds, type)[idxrow, idxcol]),
            rho       = rep(rho(fds, type=type)[idxrow], 
                            ifelse(isTRUE(idxcol), ncol(fds), sum(idxcol))) 
    )
    dt[, deltaPsi:=obsPsi - predPsi]

    # if requested return gene p values
    if(isTRUE(aggregate)){
        # get gene-level aberrant status
        aberrantGeneLevel <- aberrant(fds[, idxcol], ..., aggregate=TRUE,
                                        subsetName=subsetName)
        aberrantGeneLevel <- melt(
                data.table(featureID=rownames(aberrantGeneLevel), 
                           aberrantGeneLevel), 
                value.name="aberrant", id.vars="featureID", 
                variable.name="sampleID")
        
        # split featureID into several rows if more than one
        dt <- dt[!is.na(featureID)]
        dt[, dt_idx:=seq_len(.N)]
        dt_tmp <- dt[, splitGenes(featureID), by="dt_idx"]
        dt <- dt[dt_tmp$dt_idx,]
        dt[,`:=`(featureID=dt_tmp$V1, dt_idx=NULL)]
        
        # get gene-level pvalue matrices
        pvalsGene <- lapply(c("pval", "padj"), function(x){
            if(x == "pval"){
                pvalsGene <- pVals(fds, type=type, 
                                    level="gene")[,idxcol,drop=FALSE]
            } else {
                pvalsGene <- padjVals(fds, type=type, subsetName=subsetName,
                                    level="gene")[,idxcol,drop=FALSE]
            }
            pvalsGene <- data.table(featureID=rownames(pvalsGene), pvalsGene)
            pvalsGene <- melt(pvalsGene, value.name=paste0("gene_", x), 
                                id.vars="featureID", variable.name="sampleID")
            return(pvalsGene)
        })
        pvalsGene <- merge(pvalsGene[[1]], pvalsGene[[2]], 
                            by=c("featureID", "sampleID"))
        
        # merge with gene level aberrant status
        pvalsGene <- merge(pvalsGene, aberrantGeneLevel, 
                           by=c("featureID", "sampleID"))
        
        # merge with gene pval matrix
        dt <- merge(dt, pvalsGene, by=c("featureID", "sampleID"))
        dt[,`:=`(pval=gene_pval, padj=gene_padj, 
                gene_pval=NULL, gene_padj=NULL)]
        
        # sort
        dt <- dt[order(sampleID, featureID, type, -aberrant,
                        padj, -abs(deltaPsi))][
                !duplicated(data.table(sampleID, featureID, type))]
    } else{
        # add aberrant information to it
        aberrantVec <- aberrant(fds, ..., padjVals=dt[,.(padj)],
                                dPsi=dt[,.(deltaPsi)], n=dt[,.(n)], 
                                rhoVals=dt[,.(rho)], aggregate=FALSE, 
                                subsetName=subsetName)
        dt[,aberrant:=aberrantVec]
    }
    
    # return object
    dt
}


#' @describeIn getter_setter_functions Dependent on the level of verbosity 
#' the algorithm reports more or less to the user. 0 means being quiet 
#' and 10 means everything.
#' @export
verbose <- function(fds){
    if("verbosity" %in% names(metadata(fds))){
        return(metadata(fds)[["verbosity"]])
    }
    return(0)
}

#' @describeIn getter_setter_functions Sets the verbosity level to a value 
#' between 0 and 10. 0 means being quiet and 10 means reporting everything.
#' @export
`verbose<-` <- function(fds, value){
    verbose <- value
    if(is.logical(verbose)){
        verbose <- verbose + 0
    }
    checkNaAndRange(verbose, min=0, max=10, na.ok=FALSE)
    metadata(fds)[["verbosity"]] <- floor(verbose)
    return(fds)
}