R/metabData.R

In hhabra/metabCombiner: Method for Combining LC-MS Metabolomics Feature Measurements

#' @title Constructor for the metabData object.
#'
#' @description
#' This is a constructor for objects of type \code{metabData}.
#'
#' @param table  Path to file containing feature table or data.frame object
#'               containing features
#'
#' @param mz    Character name(s) or regular expression associated with data
#' column containing m/z values. The first column whose name contains this
#' expression will be selected for analysis.
#'
#' @param rt Character name(s) or regular expression associated with data
#' column containing retention time values. The first column whose name
#' contains this expression will be selected for analysis.
#'
#' @param id Character name(s) or regular expression associated with data
#' column containing metabolomics feature identifiers. The first column whose
#' name contains this expression will be selected for analysis.
#'
#' @param adduct Character name(s) or regular expression associated with data
#' column containing adduct or chemical formula annotations. The first column
#' whose name contains this expression will be selected for analysis.
#'
#' @param samples Character name(s) or regular expression associated with data
#' columns. All numeric columns whose names contain these keywords are selected
#' for analysis. If no keywords given, program searches longest stretch of
#' remaining numeric columns.
#'
#' @param extra  Character names of columns containing additional feature
#' information, e.g.  non-analyzed sample values. All columns containing these
#' keywords selected and will be displayed in the final output.
#'
#' @param Q Character name(s) or regular expression associated with numeric
#' feature abundance quantiles. If NULL, abundance quantiles are calculated
#' from sample intensities.
#'
#' @param rtmin Numeric. Minimum retention time for analysis.
#'
#' @param rtmax Numeric. Maximum retention time for analysis.
#'
#' @param misspc Numeric. Threshold missingness percentage for analysis.
#'
#' @param zero Logical. Whether to consider zero values as missing.
#'
#' @param measure Central sample abundance measure, either "median" or "mean".
#'
#' @param duplicate list of duplicate feature removal parameters.
#' (see: \code{\link{opts.duplicate}})
#'
#' @details
#' Processed metabolomics feature table must contain columns for m/z, rt,
#' and numeric sample intensities. Some optional fields such as identity
#' \code{id} and \code{adduct} label columns may also be supplied. Non-analyzed
#' columns can be included into the final output by specifying the names of
#' these columns in the \code{extra} argument. All required arguments are
#' checked for validity (e.g. no negative m/z or rt values, each column is used
#' at most once, column types are valid, etc...).
#'
#' Following this is a pre-analysis filtering of rows that are either:
#' 1) Outside of a specified retention time range (\code{rtmin},\code{rtmax}),
#' 2) Missing in excess of \code{misspc} percent of analyzed samples, or
#' 3) deemed duplicates by small pairwise <m/z, rt> differences.
#' See: \code{\link{opts.duplicate}} on duplicate feature removal
#'
#' Remaining features are ranked by abundance quantiles, Q, using a central
#' \code{measure}, either "median" or "mean." Alternatively, the abundance
#' quantiles column can be specified in the argument \code{Q}.
#'
#' @return An object of class metabData containing the specific information
#' specified by \code{mz,rt, samples, id, adduct, Q, and extra} arguments, and
#' adjusted by pre-processing steps.
#'
#' @examples
#' data(plasma30)
#' data(plasma20)
#'
#' #samples: CHEAR; RedCross samples non-analyzed "extra" columns
#' p30 <- metabData(plasma30, mz = "mz", rt = "rt", id = "identity",
#'                  adduct = "adduct", samples = "CHEAR", extra = "RedCross")
#'
#' getSamples(p30)  #should print names of 5 CHEAR Sample column names
#' getExtra(p30)    #should print names of 5 Red Cross Sample column names
#'
#' #equivalent to above
#' p30 <- metabData(plasma30, id = "id", samples = "CHEAR", extra = "Red")
#'
#' #analyzing Red Cross samples with retention time limitations (0.5-17.5min)
#' p20 <- metabData(plasma20, samples = "Red", rtmin = 0.5, rtmax = 17.5)
#' data = getData(p20)
#' range(data$rt)
#'
#' #using regular expressions for field searches
#' p30 <- metabData(plasma30, id = "identity|id|ID", samples = ".[3-5]$")
#' getSamples(p30)    #should print all column names ending in .3, .4, .5
#'
#' @export
metabData <- function(table, mz = "mz", rt = "rt", id = "id",
                        adduct = "adduct", samples = NULL, Q = NULL,
                        extra = NULL, rtmin = "min", rtmax = "max",
                        misspc = 50, measure = c("median", "mean"),
                        zero = FALSE, duplicate = opts.duplicate())
{
    if(missing(table))
        stop("required argument 'table' is missing with no default")
    if(!is.character(mz))
        stop("non-character argument for variable 'mz'")
    if(!is.character(rt))
        stop("non-character argument for variable 'rt'")
    if(!is.null(samples) & !is.character(samples))
        stop("non-character argument for variable 'samples'")
    if(!is.character(id))
        id <- NULL
    if(!is.character(adduct))
        adduct <- NULL
    if(!is.character(extra))
        extra <- NULL
    if (misspc >= 100 | misspc < 0 | !is.numeric(misspc))
        stop("Parameter 'misspc' must be a numeric value from [0,100)")
    if(is.character(table))
        table <- readData(table)
    else if(dplyr::is.tbl(table))     #handling tbl error
        table <- as.data.frame(table)
    else if(!is.data.frame(table))
        stop("argument 'table' must be a data.frame or path to data file")
    if(!is.logical(zero))
        stop("argument 'zero' must be a logical")
    if(any(grepl("\\{|\\[|\\(|\\)|\\]|\\}", names(table))))
        warning("bracket characters in data column names may affect column",
                " detection accuracy")
    measure <- match.arg(measure)
    newData <- new("metabData")
    newData <- detectFields(Data = newData, table = table, mz = mz, rt = rt,
                            id = id, adduct = adduct, samples = samples,
                            extra = extra, Q = Q)
    newData <- adjustData(Data = newData, misspc = misspc, measure = measure,
                            rtmin = rtmin, rtmax = rtmax, zero = zero,
                            duplicate = duplicate)
    return(newData)
}