R/IsotopicLabelling.R

In RuggeroFerrazza/IsotopicLabelling: A package for the analysis of MS isotopic patterns in labelling experiments

#' IsotopicLabelling-package
#'
#' The \code{IsotopicLabelling} package allows to analyse the isotopic patterns in MS data 
#' obtained in isotopic labelling experiments.  From the experimental patterns, 
#' the package estimates the isotopic abundance of the stable isotope employed in 
#' the labelling experiment (either ^2H or ^13C) inside a specified compound.
#' 
#' 
#' @section Details:
#'Given a data frame of LC-MS or GC-MS peak intensities or areas (one column for each sample to analyse), 
#' the \code{\link{IsotopicLabelling}} package first extracts the isotopic patterns of the specified compound, 
#' and then performs an isotopic pattern analysis to estimate the isotopic abundance of the labelling isotope. 
#' This is performed through a weighted non-linear least squares fitting procedure, 
#' where the resulting estimate is the value for which the theoretical pattern best reproduces 
#' the experimental one. 
#' During the fitting, the experimental signals are given weights proportional to the square root 
#' of their intensity, to correct for the non uniform variance at different intensity levels. 
#' The theoretical patterns are computed using the \code{\link[ecipex]{ecipex}} R package.
#' 
#' @section Block diagram:
#' The isotopic pattern analysis can be divided into the following steps:
#' \enumerate{
#' \item Starting from a class \code{xcmsSet} object (from the \code{xcms} R package), 
#' generate a data frame of peak signal intensities or areas, 
#' with each column corresponding to a sample. 
#' This step can be avoided if the data frame is already available (obtained by other means);
#' \item Extract from the data frame the experimental isotopic patterns of the specified compound 
#' (one pattern for each sample). 
#' In the chemical formula of the compound, the element whose abundance is unknown is called "X";
#' \item Normalise the patterns and estimate the abundance of the label 
#' through a weighted non-linear least squares fitting procedure.
#' \item Summarize the results.
#' }
#'
#' @docType package
#' @name IsotopicLabelling
#' 
#' @import xcms
#' @import ecipex
#' @import stringr
#' @import gsubfn
#' 
#' 
#' 
#' @author Ruggero Ferrazza, Pietro Franceschi
#' 
#' 
#' 
#' 
#' 
#' 

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