Add missing FA annotations

Description

Add missing FA annotations

Usage

addFA(msbatch, dmz = 5, faid, adducts = "M-H", mz, from, to)

Arguments

Value

annotated msbatch.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

FA annotation

Description

FA annotation

Usage

annotateFA(msbatch, dmz = 5, rt, adducts = c("M-H"), db)

Arguments

Value

annotated msbatch.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Examples

## Not run: 
msbatch <- annotateFA(msbatch, dmz = 5)

## End(Not run)

substract blank samples.

Description

substract blank samples.

Usage

blankSubstraction(fadata, blankgroup = "blank", verbose = TRUE)

Arguments

Value

blank substracted fadata.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Modify rt peak limits of annotated FAs

Description

Modify rt peak limits of annotated FAs

Usage

changeFArt(msbatch, id, from, to)

Arguments

Value

annotated msbatch.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

check S and E steps based on isotopologue distribution.

Description

check S and E steps based on isotopologue distribution.

Usage

checkparameters(resp, M, n, SE, imported, D2)

Arguments

Value

SE list corrected.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

acetate combinations for M+0, M+1 and M+2.

Description

acetate combinations for M+0, M+1 and M+2.

Usage

combAcetate(M)

Arguments

Value

acetate combination for M carbon atoms.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

correct data for natural abundance of 13C using accucor algorithm.

Description

correct data for natural abundance of 13C using accucor algorithm.

Usage

correctNatAb13C(fadata, resolution = 140000, purity = 0.99)

Arguments

Value

corrected fadata.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

References

Su X, Lu W, Rabinowitz J (2017). “Metabolite Spectral Accuracy on Orbitraps.” Analytical Chemistry, 89(11), 5940-5948, PMID: 28471646, R package version 0.2.4 (2021), <https://doi.org/10.1021/acs.analchem.7b00396>.

Modify FA annotations

Description

after FA annotation using annotateFA, the resulting data frame can be modified to remove rows with unwanted annotation, iniRT and endRT can be changed to redefine peak limits and extra rows may be written to add new annotations. FAid should also be modified to contain unique names such as "FA(16:1)n7" and "FA(16:1)n10" instead of generic "FA(16:1)". For unknown fatty acids use FA(16:1)nx (nx, ny and nz are availables for all FA).

Internal standards can also be added to normalize data later. Leave ID and Adducts columns empty, write "IS" at the FAid column and add mz, RT, iniRT and endRT information.

Usage

curateFAannotations(msbatch, faid, dmz = 10)

Arguments

Details

Modify FA annotations

Value

annotated msbatch.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Examples

## Not run: 
msbatch <- annotateFA(msbatch, dmz = 5)

plots <- plotFA(msbatch, dmz = 10)

pdf("FAs.pdf")
for (p in 1:length(plots)){
  print(plots[[p]])
}
dev.off()

write.csv(msbatch$fas, file="faid.csv", row.names=FALSE)

faid <- read.csv("faid_curated.csv", sep=",", dec=".")

msbatch <- curateFAannotations(msbatch, faid)

## End(Not run)

Data correction for natural abundance of 13C and data normalization using internal standards followed by blank substraction.

Description

Data correction for natural abundance of 13C and data normalization using internal standards followed by blank substraction.

Usage

dataCorrection(
  fadata,
  correct13C = TRUE,
  blankgroup = "blank",
  externalnormalization = c(),
  resolution = 140000,
  purity13C = 0.99,
  verbose = TRUE
)

Arguments

Value

corrected fadata.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

References

Su X, Lu W, Rabinowitz J (2017). Metabolite Spectral Accuracy on Orbitraps. Analytical Chemistry, 89(11), 5940-5948, PMID: 28471646, R package version 0.2.4 (2021), <https://doi.org/10.1021/acs.analchem.7b00396>.

Examples

ssdata <- dataCorrection(ssexamplefadata, blankgroup="Blank")

data list for elongation analysis.

Description

data list for elongation analysis.

Usage

dataElong(S16, D1, D2, P, E1, E2, E3, E4, E5, M, vcomb16, mcombe)

Arguments

Value

data list for elongation analysis.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

data list for synthesis analysis.

Description

data list for synthesis analysis.

Usage

dataSynth(D1, D2, P, M, vcomb)

Arguments

Value

Data list for synthesis analysis.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Desaturation analysis of fatty acids.

Description

Desaturation analysis of fatty acids.

Usage

desaturationAnalysis(
  fadata,
  desaturationsdb = FAMetA::desaturationsdb,
  SEThr = 0.05
)

Arguments

Details

Once synthesis and elongation parameters have been estimated, these results can be used to calculate the FA fraction that comes from desaturation in unsaturated FA. For a given unsaturated FA (e.g. FA(18:1n9) we can conceptually consider a one-step elongation-desaturation reaction (in this example directly from FA(16:0) to FA(18:1n9) (E1') or a two-step elongation followed by desaturation process (in this example FA(16:0) is elongated to FA(18:0) (E1) and then desaturated to FA(18:1n9) (Des). Therefore, desaturation can be estimated based on the fraction of E1', which is E1 from FA(18:1)n9, and E1, which is E1 from FA(18:0). This same model can be used for all known desaturation steps (see FAMetA::desaturationsdb) as long as precursor and product FA isomers have been correctly and uniquely identified and stationary state has been reached.

Value

fadata list. Desaturation analysis results will be saved at the desaturation element of the fa list.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Examples

ssdata <- dataCorrection(ssexamplefadata, blankgroup="Blank")
ssdata <- synthesisAnalysis(ssdata, R2Thr = 0.95, maxiter = 1e3,
maxconvergence = 100, startpoints = 5)
ssdata <- elongationAnalysis(ssdata, R2Thr = 0.95, maxiter = 1e4,
maxconvergence=100, startpoints = 5, D2Thr = 0.1)
ssdata <- desaturationAnalysis(ssdata, SEThr = 0.05)


## Not run: 
fadata <- dataCorrection(examplefadata, blankgroup = "Blank")
fadata <- synthesisAnalysis(fadata, R2Thr = 0.95, maxiter = 1e3,
maxconvergence = 100, startpoints = 5)
fadata <- elongationAnalysis(fadata, R2Thr = 0.95, maxiter = 1e4,
maxconvergence=100, startpoints = 5, D2Thr = 0.1)
fadata <- desaturationAnalysis(fadata, SEThr = 0.05)

## End(Not run)

Desaturation reactions database.

Description

Desaturation reactions database.

Usage

data("desaturationsdb")

Format

A data frame with 13 observations on the following 3 variables.

precursor

character vector.

product

character vector.

parameter

parameter required to estimate desaturation.

Examples

data(desaturationsdb)

Elongation analysis of fatty acids longer than 16 carbons.

Description

Elongation analysis of fatty acids longer than 16 carbons.

Usage

elongationAnalysis(
  fadata,
  R2Thr = 0.98,
  maxiter = 10000,
  maxconvergence = 100,
  startpoints = 5,
  D2Thr = 0.1,
  parameters = FAMetA::parameters,
  verbose = TRUE
)

Arguments

Details

Main route of de novo synthesis plus elongation starts at 16 carbons and then adds blocks of 2 carbons. Therefore, isotopologue distributions for FA longer than 16 carbons will be modeled taking into account de novo synthesis until FA(16:0), followed by single and independent elongation steps (E1, E2 …, En). Parameters D0, D1 and D2 are imported from FA(16:0) or FA(14:0) and thus, the only relevant parameters to be estimated in the elongation analysis are Ei and I. For n6 and n3 series, elongation is expected from FA(18:2)n6 and FA(18:3)n3 so that synthesis (S16:0) and first elongation step (E1) are set to 0.

Value

fadata list. Elongation analysis results will be saved at the elongation element of the fa list.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Examples

ssdata <- dataCorrection(ssexamplefadata, blankgroup="Blank")
ssdata <- synthesisAnalysis(ssdata, R2Thr = 0.95, maxiter = 1e3,
maxconvergence = 100, startpoints = 5)
ssdata <- elongationAnalysis(ssdata, R2Thr = 0.95, maxiter = 1e4,
maxconvergence=100, startpoints = 5, D2Thr = 0.1)


## Not run: 
fadata <- dataCorrection(examplefadata, blankgroup = "Blank")
fadata <- synthesisAnalysis(fadata, R2Thr = 0.95, maxiter = 1e3,
maxconvergence = 100, startpoints = 5)
fadata <- elongationAnalysis(fadata, R2Thr = 0.95, maxiter = 1e4,
maxconvergence=100, startpoints = 5, D2Thr = 0.1)

## End(Not run)

calculate FA isotope distribution for elongated FAs using the quasi multinomial distribution.

Description

calculate FA isotope distribution for elongated FAs using the quasi multinomial distribution.

Usage

elongationqmult(S16, D1, D2, P, E1, E2, E3, E4, E5, M, vcomb16, mcombe)

Arguments

Value

FA isotope distribution.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Fit FA isotope distribution with non-linear regression using multiple starting points to find the best fit.

Description

Fit FA isotope distribution with non-linear regression using multiple starting points to find the best fit.

Usage

estimatePars(
  formula,
  gridStart,
  datanls,
  maxiter,
  maxconvergence,
  limitPhi = 0.1
)

Arguments

Value

model.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

References

Daniel Padfield and Granville Matheson (2020). nls.multstart: Robust Non-Linear Regression using AIC Scores. R package version 1.2.0. <https://CRAN.R-project.org/package=nls.multstart>

estimate synthesis parameters.

Description

estimate synthesis parameters.

Usage

estimateSynthesis(
  fa,
  M,
  R2Thr = 0.9,
  maxiter = 1000,
  maxconvergence = 100,
  D1 = NA,
  D2 = NA,
  P = NA,
  startpoints = 5
)

Arguments

Value

De novo-synthesis and elongation analysis results.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Example fadata list.

Description

Example fadata list.

Usage

data("examplefadata")

Format

A list with 4 elements.

metadata

data frame with metadata information for samples.

fattyacids

data frame with compound name and label for each isotopologue (intensities df).

IS

data frame with IS intensities for each sample.

intensities

data frame with isotopologue intensities for each sample.

Examples

data(examplefadata)

External normalization using additional measures (i.e. protein levels).

Description

External normalization using additional measures (i.e. protein levels).

Usage

externalNormalization(fadata, externalnormalization, verbose = TRUE)

Arguments

Value

normalised fadata by external measures.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Fatty Acids database.

Description

Fatty Acids database.

Usage

data("fattyacidsdb")

Format

A data frame with 35 observations on the following 3 variables.

formula

a character vector.

total

a character vector. Number of carbons and double bounds.

Mass

a numeric vector.

Examples

data(fattyacidsdb)

Fit FA isotope distribution with non-linear regression and calculate the sum of squared estimate of errors.

Description

Fit FA isotope distribution with non-linear regression and calculate the sum of squared estimate of errors.

Usage

fitSSE(formula, startpars, datanls, control, limitPhi = 0.1)

Arguments

Value

sum of squared estimate errors.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

References

Daniel Padfield and Granville Matheson (2020). nls.multstart: Robust Non-Linear Regression using AIC Scores. R package version 1.2.0. <https://CRAN.R-project.org/package=nls.multstart>

Get formula and neutral mass for annotated compounds

Description

Get formula and neutral mass for annotated compounds.

Usage

getFormula(df, dbs)

Arguments

Value

Data frame

Author(s)

M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>

References

M Isabel Alcoriza-Balaguer (2021). LipidMS: Lipid Annotation for LC-MS/MS DDA or DIA Data. R package version 3.0.1. <https://CRAN.R-project.org/package=LipidMS>

Obtain the final results table from synthesis, elongation and desaturation analysis.

Description

Obtain the final results table from synthesis, elongation and desaturation analysis.

Usage

getResultsTable(fadata, parameters = FAMetA::parameters)

Arguments

Value

results data frame.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Obtain a summary results table.

Description

Obtain a summary results table containing means and standard deviation for each relevant parameter for each fatty acids by sample groups.

Usage

getSummaryTable(fadata, resultstable, parameters = FAMetA::parameters)

Arguments

Value

summary data frame.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

generate starting points grid for elongation analysis.

Description

generate starting points grid for elongation analysis.

Usage

gridElong(S16 = NA, E1 = NA, E2 = NA, E3 = NA, E4 = NA, E5 = NA, startpoints)

Arguments

Value

Starting points grid for elongation analysis.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

generate starting points grid for synthesis analysis.

Description

generate starting points grid for synthesis analysis.

Usage

gridSynth(D1 = NA, D2 = NA, P = NA, S = NA, M, startpoints)

Arguments

Value

Starting points grid for synthesis analysis.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Plot results on a heatmap.

Description

Plot results on a heatmap.

Usage

heatmapResults(toPlot, cols, scale = "none", breaks, nacolor = "grey")

Arguments

Value

heatmap.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Extract FA data from an annotated msbatch.

Description

Extract FA data from an annotated msbatch.

Usage

msbatch2fadata(msbatch, faid)

Arguments

Value

fadata.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

mz match withing a vector of mz values

Description

This function searches marches between a given mz and a vector of mz values with certain mass tolerance and returns the index of the matched values. It is used by identification functions to find candidates of each class of lipid based on full MS information.

Usage

mzMatch(mz, mzvector, ppm)

Arguments

Value

Numeric vector indicating the index of matched mz values and ppms for each one of those matches (match1, ppm1, match2, ppm2, etc.)

Author(s)

M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>

References

M Isabel Alcoriza-Balaguer (2021). LipidMS: Lipid Annotation for LC-MS/MS DDA or DIA Data. R package version 3.0.1. <https://CRAN.R-project.org/package=LipidMS>

Data normalization using internal stardards.

Description

Data normalization using internal stardards.

Usage

normalizeIS(fadata, verbose = TRUE)

Arguments

Value

normalised fadata by IS.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Parameters for FA metabolic analysis.

Description

Parameters for FA metabolic analysis.

Usage

data("parameters")

Format

A data frame with 167 observations on the following 8 variables.

FattyAcid

a character vector.

M

integer vector. Number of carbons.

S16

De novo synthesis. If equal to 1 it is estimated.

E1

a numeric vector. If equal to 1 it is estimated.

E2

a numeric vector. If equal to 1 it is estimated.

E3

a numeric vector. If equal to 1 it is estimated.

E4

a numeric vector. If equal to 1 it is estimated.

E5

a numeric vector. If equal to 1 it is estimated.

Examples

data(paramters)

Plot observed and predicted isotope distributions.

Description

Plot observed and predicted isotope distributions.

Usage

plotDistributions(results, groups, title = "")

Arguments

Value

plot.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Plot FA EICs

Description

Plot FA EICs

Usage

plotFA(msbatch, dmz, verbose = TRUE)

Arguments

Value

annotated msbatch with saved plots.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Examples

## Not run: 
msbatch <- annotateFA(msbatch, dmz = 5)

plots <- plotFA(msbatch, dmz = 10)

pdf("FAs.pdf")
for (p in 1:length(plots)){
  print(plots[[p]])
}
dev.off()

## End(Not run)

Plot observed and predicted isotope distributions from the results obtained.

Description

Plot observed and predicted isotope distributions from the results obtained.

Usage

plotResults(results, groups, fas)

Arguments

Value

plot.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

read FA data from a csv file.

Description

First rows must contain metadata information such as sample groups (row named sampletype) and any other extra information like protein levels for external normalization. Then, the following row must contain a Compound and Label columns followed by all sample names. FA names must be unique and omega series must be indicated (i.e. FA(20:4)n3, FA(24:1)n9, FA(16:0)). Unknown FA series can be named as nx, ny, nz to differentiate between isomers. Labels must be specified with integer numbers for 0 to maximum number of carbons.

Usage

readfadatafile(file, sep = ",", dec = ".")

Arguments

Details

read FA data from a csv file.

Value

fadata.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Examples

## Not run: 
fadata <- readfadatafile("externafadata.csv", sep=",", dec=".")

## End(Not run)

Remove incorrect FA annotations

Description

Remove incorrect FA annotations

Usage

removeFA(msbatch, ids)

Arguments

Value

annotated msbatch.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

run the elongation analysis.

Description

run the elongation analysis.

Usage

runElongationAnalysis(
  fa,
  M,
  D1,
  D2,
  P,
  S16,
  E1,
  E2,
  E3,
  E4,
  E5,
  R2Thr = 0.9,
  maxiter = 10000,
  maxconvergence = 100,
  startpoints = 5,
  D2Thr = D2Thr
)

Arguments

Value

Elongation and importation analysis results.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

run synthesis analysis.

Description

run synthesis analysis.

Usage

runSynthesisAnalysis(
  fadata,
  toDo,
  R2Thr = R2Thr,
  maxiter = maxiter,
  maxconvergence = maxconvergence,
  D1 = D1,
  D2 = D2,
  P = P,
  startpoints = startpoints,
  parameters = FAMetA::parameters,
  verbose = TRUE
)

Arguments

Value

De novo-synthesis analysis results.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Search FA isotopes

Description

Search FA isotopes

Usage

searchFAisotopes(msbatch, dmz = 5, coelCutoff = 0.7)

Arguments

Value

fadata list.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Examples

## Not run: 
fadata <- searchFAisotopes(msbatch, dmz = 10, coelCutoff = 0.4)

## End(Not run)

Search internal stardards.

Description

Search internal stardards.

Usage

searchIS(msbatch, mz, rt, minRT, maxRT, dmz = 10)

Arguments

Value

annotated msbatch.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Toy example fadata list.

Description

Toy example fadata list.

Usage

data("ssexamplefadata")

Format

A list with 4 elements.

metadata

data frame with metadata information for samples.

fattyacids

data frame with compound name and label for each isotopologue (intensities df).

IS

data frame with IS intensities for each sample.

intensities

data frame with isotopologue intensities for each sample.

Examples

data(ssexamplefadata)

Obtain result tables and heatmaps that help interpreting your results.

Description

Obtain result tables and heatmaps that help interpreting your results.

Usage

summarizeResults(fadata, controlgroup = NA, parameters = FAMetA::parameters)

Arguments

Value

fadata list with a results element which contains: results data frame (results for the main parameters for each fatty acid and sample), summary data frame (mean and sd by sample groups for each parameter and fatty acids from the results table), different heatmaps representing pool size and results (values represented are also saved in data frames) and tables summarizing all parameters values for synthesis and elongation (S16, E1, E2, E3, E4 and E5).

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Examples

ssdata <- dataCorrection(ssexamplefadata, blankgroup="Blank")
ssdata <- synthesisAnalysis(ssdata, R2Thr = 0.95, maxiter = 1e3,
maxconvergence = 100, startpoints = 5)
ssdata <- elongationAnalysis(ssdata, R2Thr = 0.95, maxiter = 1e4,
maxconvergence=100, startpoints = 5, D2Thr = 0.1)
ssdata <- desaturationAnalysis(ssdata, SEThr = 0.05)
ssdata <- summarizeResults(ssdata)


## Not run: 
fadata <- dataCorrection(examplefadata, blankgroup = "Blank")
fadata <- synthesisAnalysis(fadata, R2Thr = 0.95, maxiter = 1e3,
maxconvergence = 100, startpoints = 5)
fadata <- elongationAnalysis(fadata, R2Thr = 0.95, maxiter = 1e4,
maxconvergence=100, startpoints = 5, D2Thr = 0.1)
fadata <- desaturationAnalysis(fadata, SEThr = 0.05)
fadata <- summarizeResults(fadata, controlgroup = "Control13Cglc")

## End(Not run)

De novo synthesis analysis of fatty acids until 16 carbons.

Description

De novo synthesis analysis of fatty acids until 16 carbons.

Usage

synthesisAnalysis(
  fadata,
  R2Thr = 0.98,
  maxiter = 1000,
  maxconvergence = 100,
  D1 = NA,
  D2 = NA,
  P = NA,
  startpoints = 5,
  parameters = FAMetA::parameters,
  propagateD = TRUE,
  verbose = TRUE
)

Arguments

Details

Synthesis analysis will model FA data for FA up to 16 carbons to estimate 13C-tracer contribution to the acetyl-CoA pool for FA synthesis (D) and the FA fraction that has been synthesized de novo. D0, D1 and D2 represent the contribution of M+0, M+1 and M+2 acetate, respectively, and P (phi) is the overdispersion parameter of the quasi-multinomial distribution. D0, D1, D2 can also be fixed if they are known. This is particularly useful in case inhibitors have been used as they could reduce S below the confidence interval and thus, S and D parameters could be misestimated.

Value

fadata list. Synthesis analysis results will be saved at the synthesis element of the fa list.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>

Examples

ssdata <- dataCorrection(ssexamplefadata, blankgroup="Blank")
ssdata <- synthesisAnalysis(ssdata, R2Thr = 0.95, maxiter = 1e3,
maxconvergence = 100, startpoints = 5)


## Not run: 
fadata <- dataCorrection(examplefadata, blankgroup = "Blank")
fadata <- synthesisAnalysis(fadata, R2Thr = 0.95, maxiter = 1e3,
maxconvergence = 100, startpoints = 5)

# If inhibitors have been used, make sure D2 has not been underestimated. If so,
# D2 could be set as the one calculated for 13-Glc Control samples to improve
# the results:

# D2 <- fadata$synthesis$results$D2[fadata$synthesis$results$FA == "FA(16:0)"]
# fadata$synthesis$results$Group[fadata$synthesis$results$FA == "FA(16:0)"]

# D2[4:12] <- rep(mean(D2[1:3]))

# relaunch synthesis analysis fixing D2
# fadata <- synthesisAnalysis(fadata, R2Thr = 0.95, maxiter = 1e3,
#                             maxconvergence = 100, startpoints = 5, D2 = D2)

## End(Not run)

calculate FA isotope distribution for newly synthesized FAs using the quasi multinomial distribution.

Description

calculate FA isotope distribution for newly synthesized FAs using the quasi multinomial distribution.

Usage

synthesisqmult(D1, D2, P, S, M, vcomb)

Arguments

Value

numeric vector with the FA isotope distribution.

Author(s)

M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>