metaboData: Example Metabolomics Data Sets

#' The abr1 dataset
#' 
#' Real world FIE-MS dataset.
#' 
#' FIE-MS data matrices developed from analysis of samples representing a time
#' course of pathogen attack in a model plant species (Brachypodium
#' distachyon).  The data was developed in a single batch with all samples
#' randomised using a Thermo LTQ linear ion trap processed using
#' \code{fiems_ltq_main}. Both positive and negative ion mode are given
#' (\code{abr1$pos} and \code{abr1$neg}). To avoid confusions, variable names
#' are given with a letter corresponding to the ionisation mode followed by the
#' actual nominal mass value (e.g. P130 corresponds to the nominal mass 130 in
#' the positive mode).
#' 
#' Experimental factors are given in the \code{abr1$fact} data frame: \itemize{
#' \item \code{injorder:} sample injection order \item \code{name:} sample name
#' \item \code{rep:} biological replicate for a given class \item \code{day:}
#' number of days following infection after which the sample has been harvested
#' - Level H corresponds to an healthy plant.  \item \code{class:} identical to
#' day except that \code{class=6} when \code{day=H} \item
#' \code{pathcdf,filecdf,name.org,remark:} are generated from profile
#' processing and are kept for traceability purposes.  } Factor of interest for
#' classification are contained in \code{abr1$fact$day}.  There are 20
#' biological replicates in each class has
#' 
#' @name abr1
#' @source The FIEmspro package \url{https://github.com/aberHRML/FIEmspro}
#' @aliases abr1 FIEmspro
#' @docType data
#' @usage data(abr1)
#' @return A list with the following elements: \item{fact}{A data frame
#' containing experimental meta-data.} \item{pos}{A data frame for positive
#' data with 120 observations and 2000 variables.} \item{neg}{A data frame for
#' negative data with 120 observations and 2000 variables.}
#' @author Manfred Beckmann, David Enot and Wanchang Lin
#' @keywords datasets
#' @examples
#' 
#' # Load data set
#' data(abr1)
#' 
#' # Select data set
#' dat <- abr1$neg
#' 
#' # number of observations and variables in the negative mode matrix
#' dim(dat)
#' 
#' # names of the variables
#' dimnames(dat)[[2]] %>% 
#'  head()
#' 
#' # print out the experimental factors
#' abr1$fact %>% 
#'  head()
#' 
#' # check out the repartition of class
#' table(abr1$fact$class)
#' 
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