In ktoddbrown/FTICR_Processing: R package for post-processing of FT-ICR-MS data with a focus on organic carbons

There are a series of very common graphics used in FT-ICR-MS analysis. This vignette will walk you through several of them.

Load data

library(FTICRProcessing)
library(ggplot2)
library(knitr)

inputFile <- '../private/data/FTICRoutput.csv'

inputFile <- 'data/ProcessedOutput.csv'

Just to give you an idea of the file format:

fileFormat <- read.csv(inputFile)[,c('m.z',  'X.out1.',  'X.out10.',  'X.out11.', 'X.out12.', 'C', 'H',  'O', 'N', 'X13C', 'S', 'P')]
kable(head(fileFormat[fileFormat[, 'X.out1.'] > 0,]))

Van Krevlen plot

Create a Van Krevlen plot using both the log of the intensity and the precense of S or N for coloration.

for(setNum in 1){
  data.df <- readFTICR(inputFile, massHeader='m.z', sampleRegStr='X.out', samplesToRead=1:9+setNum*9, elementKey = list(C='C', H='H', O='O', N='N', S='S', P='P'))
  print(ggplot(data.df) + 
          geom_point(aes(x=OtoC, y=HtoC, color=log(intensity)), alpha=0.5) + 
          facet_wrap(~sample))
  print(ggplot(data.df) + 
          geom_point(aes(x=OtoC, y=HtoC, color=(S>0 | N > 0)), alpha=0.5) + 
          facet_wrap(~sample))
}

Plot oxygen histogram

for(setNum in 1){
  data.df <- readFTICR(inputFile, massHeader='m.z', elementKey = list(C='C', H='H', O='O', N='N', S='S', P='P'), sampleRegStr='X.out', samplesToRead=1:9+setNum*9)

  print(ggplot(data.df) + 
          geom_histogram(aes(x=O, y=..density..)) + xlim(1, max(data.df$O, na.rm=TRUE)) +
          facet_wrap(~sample))
  print(ggplot(data.df) + 
          geom_histogram(aes(x=O)) + xlim(1, max(data.df$O, na.rm=TRUE)) + 
          facet_wrap(~sample))
}

Plot Kendrick mass deficit

CO2mass <- 44.01
CH4mass <- 16.04
for(setNum in 1){
  msData <- readFTICR(inputFile, massHeader='m.z', sampleRegStr='X.out', samplesToRead=1:9+setNum*9)

  print(ggplot(msData) +
          geom_point(aes(x=m.z, y=m.z/CO2mass)))
}

ktoddbrown/FTICR_Processing documentation built on May 20, 2019, 7:05 p.m.