using analyzeGCMSdata

basic usage of analyteGCMSdata

As a reference, below are the key commands used to operate the integration app. This is the information that is covered in the overview video.

To control the chromatogram window:

  • shift + q = update
  • shift + a = add selected peak
  • shift + r = remove selected peak
  • shift + g = add global peak
  • shift + z = save table

To control the mass spectrum window:

  • shift+1 = extract mass spectra from highlighted chromatogram region, plot average mass spectrum in panel 1.
  • shift+2 = refresh mass spectrum in panel 1. This is used for zooming in on a region of the mass spectrum that you have highlighted. A spectrum needs to first be extracted for this to be possible.
  • shift+3 = extract mass spectra from highlighted chromatogram region, subtract their average from the mass spectrum in panel 1.
  • shift+4 = search current spectrum in panel 1 against library of mass spectra (only available if you load via phylochemistry).
  • shift+5 = save the current spectrum in panel 1 as a csv file (only available if you load via phylochemistry).

advanced usage of analyzeGCMSdata

You can ask analyzeGCMSdata to extract single ion chromatograms if you wish. Just specify a list of ions as an argument. Note that specifying โ€œ0โ€ corresponds to the total ion chromatogram and must be included as the first item in the list. Hereโ€™s an example:

analyzeGCMSdata("/Volumes/My_Drive/gc_data", ions = c("0", "218"))


Will return an interface that shows chromatograms for the total ion count and for ion 218.

At this point, note that you have a new set of files in your data-containing folder. There will be one *.CDF.csv file for each CDF file you have in the folder. This contains a matrix of all the mass measurements in your entire sample - the abundance of each m/z value during each scan. There is also a chromatograms.csv file. This is a list of all the chromatograms (total ion + whatever single ions were specified). These can be useful for creating plots of chromatograms via ggplot.