1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
|
library(IsoSpecR)
# We will model a glucose molecule where one of the carbons has been replaced with
# (14C) with 95% probability.
# First, we define an alternate isotopic table for elements that contains the readiocarbon: 14C with 95% probability,
# and either 12C or 13C with their natural relative probabilities otherwise. First, we load up standard isotopic data:
data(isotopicData)
isotopes = isotopicData$IsoSpec
# And we add an artificial element X, with 3 isotopes, that will represent the radiocarbon.
radiolabel = data.frame(
element = c('X', 'X', 'X'),
isotope = c('X12', 'X13', 'X14'),
# Grab the masses of 12C and 13C from IsoSpec builtin data, provide mass of 14C manually
mass = c(isotopes[isotopes$isotope == 'C12', 'mass'], isotopes[isotopes$isotope == 'C13', 'mass'], 14.003241989),
# Isotope abundances are: 95% 14C, and the remaining 5% is split between 12C and 13C proportionally to their natural abundances
abundance = c(isotopes[isotopes$isotope == 'C12', 'abundance'] * 0.05, isotopes[isotopes$isotope == 'C13', 'abundance'] * 0.05, 0.95),
# unused parameter, kept for backward compatibility, ignore that
ratioC = c(NA, NA, NA)
)
isotopes = rbind(isotopes, radiolabel)
radioglucose = c(C=5, H=12, O=6, X=1) # A glucose molecule: one of the carbons is replaced with an artificial element X (the readiocarbon)
p = .99 # joint probability of the output
IsoSpecify(radioglucose, p, isotopes = isotopes, showCounts = TRUE)
|
