find_abundance: Fit experimental isotopic patterns

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

Description

Function that takes each of the provided experimental MS isotopic patterns, and fits the best theoretical pattern that reproduces it through a weighted non-linear least squares procedure.

Usage

find_abundance(patterns, info, initial_abundance = NA, charge = 1)

Arguments

patterns	A matrix of experimental isotopic patterns (one column for each sample), with the first two columns representing m/z and retention time of the corresponding peaks
info	Named list containing isotopic information, output of the isotopic_information function
initial_abundance	Either NA, or a numeric vector of length equal to the number of samples, with the initial guesses on the percentage isotopic abundance of the labelling isotope (denoted as X, it can be either ^2H or ^13C). If provided, numbers between 0 and 100
charge	Natural number, denoting the charge state of the target adduct (1,2,3,...). If not provided, it is 1 by default

Value

An object of class labelling, which is a list containing the results of the fitting procedure:

compound	Character vector specifying the chemical formula of the compound of interest, with X being the element with unknown isotopic distribution (to be fitted)
best_estimate	Numeric vector of length equal to the number of samples, containing the estimated percentage abundances of the labelling isotope X (either ^2H or ^13C). Numbers between 0 and 100
std_error	Numeric vector with the standard errors of the estimates, output of the nls fitting procedure
dev_percent	Numeric vector with the percentage deviations between best fitted and related experimental patterns
x_scale	Numeric vector containing the m/z values relative to the signals of the experimental patterns
y_exp	Matrix of normalised experimental isotopic patterns (one column for each sample). The most intense signal of each pattern is set to 100
y_theor	Matrix of normalised fitted theoretical isotopic patterns (one column for each sample). The most intense signal of each pattern is set to 100
residuals	Matrix of residuals: each column is the difference between experimental and best fitted theoretical patterns
warnings	Character vector with possible warnings from the nls fitting procedure

Author(s)

Ruggero Ferrazza

See Also

isotopic_information

Examples

## Not run: 
fitted_abundances <- find_abundance(patterns, info, initial_abundance=NA, charge=1)

## End(Not run)

RuggeroFerrazza/IsotopicLabelling documentation built on May 9, 2019, 10:36 a.m.