R/signalGrouping.R

In adductomicsR: Processing of adductomic mass spectral datasets

#' Signal grouping
#'
#' Euclidean distances between m/z signals are hierarchically clustering using
#' the average method and the composite spectrum groups determined by an
#' absolute
#' error cutoff
#'
#' @param spectrum.df a dataframe or matrix with two or more columns:
#' 1. Mass/ Mass-to-charge ratio
#' 2. Intensity
#' @param mzError interpeak absolute m/z error for signal grouping
#' (Default = 0.001)
#' @param minPeaks numeric minimum number of peaks to integrate
#' @usage signalGrouping(spectrum.df = NULL, mzError = 0.8, minPeaks = 5)
#' @return dataframe of m/z grouped signals, the m/z values of the input
#' dataframe/ matrix peak groups are averaged and the signal intensities
#' summed.
signalGrouping <-
    function(spectrum.df = NULL,
            mzError = 0.8,
            minPeaks = 5) {
        # error handling
        if (is.null(spectrum.df)) {
            stop("No spectrum matrix/dataframe supplied")
        } else {
            hr <-
                fastcluster::hclust.vector(spectrum.df[, 1], method = "median",
                                            members = NULL)
            # cut tree according to absolute m/z error
            spectrum_group <- cutree(hr, h = mzError)
            # calculate weighted mean of the m/z and
            #sum signal within each peak group
            mass <-
                do.call(c, as.list(by(spectrum.df, as.factor(spectrum_group),
                                        function(x) {
                                            weighted.mean(x[, 1], x[, 2])
                                        })))
            grouped.df <- data.frame(
                mass = mass,
                intensity = tapply(spectrum.df[, 2],
                                    as.factor(spectrum_group), sum),
                stringsAsFactors = FALSE
            )
            # average any additional columns i.e. retention time
            if (ncol(spectrum.df) > 2) {
                groupedCols <- apply(spectrum.df[, 3:ncol(spectrum.df),
                                                drop = FALSE], 2,
                                    function(x)
                                        tapply(x, as.factor(spectrum_group),
                                            mean))
                grouped.df <- cbind(grouped.df, groupedCols)
            }
            if (nrow(grouped.df) > minPeaks) {
                if (!is.null(colnames(spectrum.df))) {
                    colnames(grouped.df) <- colnames(spectrum.df)
                }
                return(grouped.df)
            } else {
                return("Less than minPeak")
            }
        }
    }