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CorMID is an R-package providing functions to solve problems during metabolic flux analysis using HR-APCI-MS.

In metabolic flux experiments tracer molecules (often glucose containing labelled carbon) are incorporated in compounds measured using mass spectrometry. The mass isotopologue distributions (MIDs) of these compounds needs to be corrected for natural abundance of labelled carbon and other effects, which are specific on the compound and ionization technique applied. This package provides functions to correct such effects in high resolution gas chromatography atmospheric pressure chemical ionization mass spectrometry (GC-HR-APCI-MS) analyses.


You can install the development version of CorMID from GitHub with:

# install.packages("devtools")

or install the version from CRAN instead.

Quick Example

CorMID is supposed to disentangle a complex MID. Complex means that the ion intensities of the isotopes are influenced by natural abundance, artificial labeling (e.g. by a 13C-Glucose tracer) and mass spectrometry artifacts (i.e. several potential adducts).

You can create and visualize such a complex mass spectrum by providing a chemical formula, the true labeling status and an adduct distribution like follows:

# Lactic acid 2 TMS
fml <- "C9H22O3Si2"
# 10% U13C enriched
mid <- c(0.9, 0, 0, 0.1)
# Three different APCI adducts formed
r <- list("M+H" = 0.8, "M-H" = 0.1, "M+H2O-CH4" = 0.1)
# reconstruct and plot the measured intensity vector
(rMID <- CorMID::recMID(mid = mid, r = r, fml = fml))

Assuming that you have measured these intensities in your experiment, CorMID could estimate the underlying MID and r for you:

# disentangle the adduct ratios and true enrichment from the above test data
out <- CorMID::CorMID(int = rMID, fml=fml, prec=0.001, r=unlist(r))

Detailed documentation

You might either read the Vignette describing the package functions in detail or read the publication which shows a evaluation of the performance of CorMID on real data sets.