Package 'LipidMS'

Description

In silico generated database for common acylceramides.

Usage

data("acylcerdb")

Format

Data frame with 192 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

Table of possible adducts to be employed by LipidMS and related information.

Usage

data("adductsTable")

Format

Data frame with 18 observations and the following 4 variables.

adduct

character vector with the adducts names.

mdiff

numeric vector indicating the mass differences.

charge

numeric vector indicating the charge.

n

numeric vector. It indicates if the ion is a monomer (1), a dimer (2), etc.

Description

Align samples from an msbatch to correct time drifts during acquisition queues.

Usage

alignmsbatch(
  msbatch,
  dmz = 5,
  drt = 30,
  minsamples,
  minsamplesfrac = 0.75,
  span = 0.4,
  parallel = FALSE,
  ncores,
  verbose = TRUE
)

Arguments

Details

First, peak partitions are created based on the enviPick algorithm to speed up the following clustering algorithm. Briefly, peaks are ordered increasingly by mz and RT and grouped based on user-defined tolerances (dmz and drt). Each peak is initialized as a partition and then, they are evaluated to decide whether or not they can be joined to the previous partition. If mz and RT of a peak matches tolerance of any of the peaks in the previous partition, it is reassigned. Then, clustering algorithm is executed to group peaks based on their RT following the next steps for each partition:

1. Each peak in the partition is initialized as a new cluster. For each cluster we will keep the minimum, maximum and mean value of the RT, which at this point have the same values. 2. Calculate a distance matrix between all clusters. This distance will be the greatest difference between minimum and maximum values of each cluster. Distances between clusters which share peaks from the same samples will be set to NA. 3. While any distance is different to NA, search the minimum distance between two clusters. 4. If distance is below the maximum distance allowed, join clusters and update minimum, maximum and mean values, else, set distance to NA and go back to point 3.

Then, clusters with a sample representation over minsamples or minsamplesfrac, will be used for alignment. To this end, an RT matrix is built containing the RT of the peaks for each sample from the selected clusters. Then, median RT is calculated for each cluster and an RT deviation matrix is obtained. Finally, time drifts for each sample are corrected using loess regression by constructing a function based on RT deviation and median.

Value

aligned msbatch

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

References

Partitioning algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html

Examples

## Not run: 
msbatch <- alignmsbatch(msbatch)

## End(Not run)

Description

Summarize annotation results of an msbatch into the feature table

Usage

annotatemsbatch(
  msbatch,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 5,
  coelCutoff = 0.8,
  lipidClassesPos = c("MG", "LPC", "LPE", "PC", "PCo", "PCp", "PE", "PEo", "PEp", "PG",
    "PI", "Sph", "SphP", "Cer", "CerP", "AcylCer", "SM", "Carnitines", "CE", "DG", "TG"),
  lipidClassesNeg = c("FA", "FAHFA", "LPC", "LPE", "LPG", "LPI", "LPS", "PC", "PCo",
    "PCp", "PE", "PEo", "PEp", "PG", "PI", "PS", "Sph", "SphP", "Cer", "CerP", "AcylCer",
    "SM", "CL", "BA"),
  dbs,
  simplifyAnnotations = FALSE,
  parallel = FALSE,
  ncores
)

Arguments

Value

msbatch

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msbatch <- annotatemsbatch(msbatch)

msbatch$features

## End(Not run)

Description

load all LipidMS default data bases required to run identification functions.

Usage

assignDB()

Value

list of data frames

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
dbs <- assignDB()

## End(Not run)

Description

Common bile acids conjugates. It can be modified to look for other BA species.

Usage

data("baconjdb")

Format

Data frame with 2 observations and the following 2 variables.

total

character vector indicating the names of the conjugates.

Mass

numeric vector with the neutral masses of the conjugates fragments.

Description

In silico generated database for common bile acids.

Usage

data("badb")

Format

Data frame with 9 observations and the following 5 variables.

formula

character vector with the molecular formulas.

total

character vector containing the names of the BAs (i.e. CA, TDCA, GLCA...).

Mass

numeric vector with the neutral masses.

conjugate

character vector containing the conjugate of each BA.

base

character vector containing the core of each BA.

Description

Process several mzXML files (peakpicking and isotope annotation) and create an msbatch for batch processing.

Usage

batchdataProcessing(
  files,
  metadata,
  polarity,
  dmzagglom = 15,
  drtagglom = 500,
  drtclust = 100,
  minpeak = c(5, 3),
  drtgap = 10,
  drtminpeak = 15,
  drtmaxpeak = c(100, 200),
  recurs = 5,
  sb = c(3, 2),
  sn = 2,
  minint = c(1000, 100),
  weight = c(2, 3),
  dmzIso = 10,
  drtIso = 5,
  parallel = FALSE,
  ncores,
  verbose = TRUE
)

Arguments

Details

This function executes 2 steps: 1) creates an msobject for each sample (using the dataProcessing function) and 2) sets an msbatch (setmsbatch function).

Numeric arguments accept one or two values for MS1 and MS2, respectively.

Value

msbatch

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

References

Peak-picking algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html

See Also

dataProcessing and setmsbatch

Examples

## Not run: 
# if metadata is a data frame:
msbatch <- batchdataProcessing(metadata$sample, metadata, polarity = "positive",
dmzagglom = 25, drtagglom = 500, drtclust = 60, minpeak = c(5, 3),
drtgap = 5, drtminpeak = 20, drtmaxpeak = 100, recurs = 5, sb = c(3, 2),
sn = 2, minint = c(1000, 100), weight = 2, dmzIso = 10, drtIso = 5)

# if metadata is a csv file:
msbatch <- batchdataProcessing(metadata = "metadata.csv", polarity = "positive",
dmzagglom = 25, drtagglom = 500, drtclust = 60, minpeak = c(5, 3),
drtgap = 5, drtminpeak = 20, drtmaxpeak = 100, recurs = 5, sb = c(3, 2),
sn = 2, minint = c(1000, 100), weight = 2, dmzIso = 10, drtIso = 5)

## End(Not run)

Description

In silico generated database for common carnitines.

Usage

data("carnitinesdb")

Format

Data frame with 30 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

In silico generated database for common CEs.

Usage

data("CEdb")

Format

Data frame with 30 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

In silico generated database for common ceramides.

Usage

data("cerdb")

Format

Data frame with 52 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

In silico generated database for common ceramides phosphate.

Usage

data("cerPdb")

Format

Data frame with 52 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

Search of specific fragments that inform about the chains structure.

Usage

chainFrags(coelfrags, chainfrags, ppm = 10, candidates, f = NULL, dbs)

Arguments

Details

The chainfrags argument must contain the fragmentation rules which inform about the chains structure. For example, in the case of PG subclass, the chain in sn1 position is identified by the lysoPG as M-H resulting from the loss of the FA chain of sn2; and the chain in sn2 position is identified as the free FA chain as M-H. These two fragments need to be searched in two different steps: in the fist step we will look for lysoPGs coeluting with the precursor using chainfrags = c("lysopg_M-H"); then, we will look for FA chains using chainfrags = c("fa_M-H"). This information can be combined later using combineChains function.

To indicate the fragments to be searched, the class of lipid is writen using the same names as the LipidMS databases without the "db" at the end (i.e. pa, dg, lysopa, mg, CE, etc.), and the adduct has to be indicated as it appears in the adductsTable, both parts separated by "_". In case some chain needs to be searched based on a neutral loss, this can be defined using "NL-" prefix, followed by the database and adduct. If this neutral loss is employed to find the remaining chain, "cbdiff-" prefix allows to calculate the difference in carbons and doubles bounds between the precursor and the building block found. For example, "cbdiff-dg_M+H-H2O" will look for DG as M+H-H2O and then, it will return the difference between their number of carbons and double bounds and the ones from the precursor. Otherwise, "NL-mg_M+H-H2O" will look for fragments coming from the loss of MGs.

In case these fragments identified as losses from the precursors are going to be employed for the intensity rules, this same prefix has to be added.

If a chain is calculated based on the difference of total number of carbons and double bounds between the precursor and a previously searched chain, chainfrags argument must be a character vector c("") and candidates data frame and chain fragments list must be provided.

Value

List of data frames with the chain fragments found.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

Search of characteristic fragments that confirm a given lipid class.

Usage

checkClass(candidates, coelfrags, clfrags, ftype, clrequisites, ppm = 10, dbs)

Arguments

Details

clfrags, ftype and clrequisites will indicate the rules to confirm a lipid class. All three arguments must have the same length.

This function allows three different types of fragments: fragments with a specific m/z as for example 227.0326 for PG in negative mode, which needs to be defined as clfrags = c(227.0326) and ftype = c("F"); neutral losses such as the head group of some PL (i.e. NL of 74.0359 in PG in negative mode), which will be defined as clfrags = c(74.0359) and ftype = c("NL"); or building blocks resulting from the loss of some groups, as for example, PA as M-H resulting from the loss of the head group (glycerol) in PG in ESI-, which will be defined as clfrags = c("pa_M-H") and ftype = c("BB"). The last two options could define the same fragments. In this case just one of them would be necessary.

When using the third type of fragment ("BB"), the building block will be specified in lower case (i.e. pa, dg, lysopa, mg, etc.) and the adduct will be given as it appears in the adductsTable, both separated by "_". Names for the building blocks are the ones used for the LipidMS databases without the "db" at the end.

In case the presence of a fragment indicates that the candidate does not belong to the lipid class (i.e. loss of CH3 in PE, which corresponds to a PC actually), this will be specified by using clrequisites = c("excluding").

Value

List with 2 elements: a matrix with logical values (presence/absense) of each expected fragment (columns) for each candidate (rows), and a logical vector with the confirmation of the lipid class for each candidate.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

Check intensity rules to confirm chains position.

Usage

checkIntensityRules(intrules, rates, intrequired, nchains, combinations)

Arguments

Details

This function will be employed when the targeted lipid class has more than one chain.

Taking PG subclass as an example, intensities of lysoPG fragments (informative for sn1) can be employed to confirm the chains structure (intrules = c("lysopg_sn1/lysopg_sn1")). In this case, the intensity of the lysoPG resulting from the loss of the FA chain in sn2 is at least 3 times greater (rates = c("3/1")) than the lysoPG resulting from the loss of the FA chain in sn1.

For the intrules argument, "/" will be use to separate the fragments related to each chain (sn1/sn2/etc), and "_" will be use to indicate the list in which they'll be searched. This will depend on the chain fragments rules defined previously. Following the example, as we use lysoPG to define the sn1 position, both fragments will be searched in this list (sn1).

For classes with more than one FA chain, if some intensity rule should be employed to identify their position but they are no defined yet, use "Unknown". If it is not necessary because the fragmentation rules are informative enough to define the position (i.e. sphingolipid species), just leave an empty vector.

Value

List of logical vectors with the confirmation for each combination.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

In silico generated database for commo CLs.

Usage

data("cldb")

Format

Data frame with 714 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

Given a RT and a list of peaks, this function subsets all coeluting fragments within a rt windows. It is used by identification functions to extract coeluting fragments from high energy functions for candidate precursor ions.

Usage

coelutingFrags(
  precursors,
  products,
  rttol,
  rawData = data.frame(),
  coelCutoff = 0
)

Arguments

Value

List of data frames with the coeluting fragments for each candidate.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

Calculate coelution score between two peaks.

Usage

coelutionScore(peak1, peak2, rawData)

Arguments

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

It calculates combinations of chain fragments that sum up the same number of carbons and double bounds as the precursor.

Usage

combineChains(candidates, nchains, sn1, sn2, sn3, sn4)

Arguments

Value

List of data frames with candidate chains structures.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

Confidence annotation levels and their hierarchy.

Usage

data("confLevels")

Format

Data frame with 5 observations and 2 variables.

level

character vector with the names of the annotation levels.

order

numeric vector that indicates the hierarchichal order.

Description

It allows to create easy-customizable lipid DBs for annotation with LipidMS package.

Usage

createLipidDB(lipid, chains, chains2)

Arguments

Details

lipidClass argument needs to be one of the following character values: "Cer", "CerP", "GlcCer", "SM", "Carnitine", "CE", "FA", "HFA", "Sph" (sphingoid bases), "SphP", "MG", "LPA", , "LPC", "LPE", "LPG", "LPI", "LPS", "FAHFA", "DG", "PC", "PE", "PG", "PI", "PS", "PA", "TG", "CL" or "all".

Value

List with the requested dbs (data frames)

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

fas <- c("8:0", "10:0", "12:0", "14:0", "14:1", "15:0", "16:0", "16:1",
"17:0", "18:0", "18:1", "18:2", "18:3", "18:4", "20:0", "20:1", "20:2",
"20:3", "20:4", "20:5", "22:0", "22:1", "22:2", "22:3", "22:4", "22:5",
"22:6", "24:0", "24:1", "26:0")
sph <- c("16:0", "16:1", "18:0", "18:1")
newdb <- createLipidDB(lipid = "PC", chains = fas, chains2 = sph)

Description

Cross the original MS1 peaklist with the annotation results.

Usage

crossTables(msobject, ppm = 5, rttol = 10, dbs)

Arguments

Value

msobject with an annotatedPeaklist, which is a data frame with 6 columns: mz, RT, int, LipidMSid, adduct and confidence level for the annotation. When multiple IDs are proposed for the same feature, they are sorted based on the annotation level and score.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

Process mzXML files individually: peakpicking and isotope anotation

Usage

dataProcessing(
  file,
  acquisitionmode,
  polarity,
  dmzagglom = 15,
  drtagglom = 500,
  drtclust = 100,
  minpeak = c(5, 3),
  drtgap = 10,
  drtminpeak = c(15, 15),
  drtmaxpeak = c(100, 200),
  recurs = 5,
  sb = c(3, 2),
  sn = 2,
  minint = c(1000, 100),
  weight = c(2, 3),
  dmzIso = 5,
  drtIso = 5,
  verbose = TRUE
)

Arguments

Details

It is important that mzXML files are centroided.

This function executes 2 steps: 1) peak-picking based on enviPick package and 2) isotope annotation based on CAMERA algorithm.

Numeric arguments accept one or two values for MS1 and MS2, respectively.

Value

an msobject that contains metadata of the mzXML file, raw data and extracted peaks.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

References

Peak-picking algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html

Isotope annotation has been adapted from CAMERA algorithm: Kuhl C, Tautenhahn R, Boettcher C, Larson TR, Neumann S (2012). “CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.” Analytical Chemistry, 84, 283–289. http://pubs.acs.org/doi/abs/10.1021/ac202450g.

See Also

batchdataProcessing and setmsbatch

Examples

## Not run: 
msobject <- dataProcessing("input_file.mzXML", acquisitionmode="DIA", polarity,
dmzagglom = 25, drtagglom = 500, drtclust = 60, minpeak = c(5, 3),
drtgap = 5, drtminpeak = 20, drtmaxpeak = 100, recurs = 5, sb = c(3, 2),
sn = 2, minint = c(1000, 100), weight = 2, dmzIso = 10, drtIso = 5)

## End(Not run)

Description

This function searches for the closest precursor selected for MS2 in DDA that matches mz tolerance and RT window of a list of candidates and extracts their fragments.

Usage

ddaFrags(candidates, precursors, rawData, ppm)

Arguments

Details

MS2 scans for a given precursor are searched within a rt window from minrt-rttol/2 to maxrt+rttol/2. If the same precursor was selected several times along the peak, the closest scan to the rt at the peak maximum is selected for annotation.

Coelution score for DDA fragments represents their relative intensity within the MS2 scan.

Value

List of data frames with the fragments for each candidate.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

In silico generated database for common DGs.

Usage

data("dgdb")

Format

Data frame with 147 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

In silico generated database for common FAs.

Usage

data("fadb")

Format

Data frame with 30 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

In silico generated database for common FAHFAs.

Usage

data("fahfadb")

Format

Data frame with 147 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

Use grouping results to target all peaks from the msbatch in each sample and refill intensities at the features table.

Usage

fillpeaksmsbatch(msbatch)

Arguments

Details

Once grouping has been performed, areas are extracted again for each peak and sample based on the peak parameters defined for each feature (mz and tolerance and initial and final RT).

Value

msbatch

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msbatch <- fillpeaksmsbatch(msbatch)

## End(Not run)

Description

Search of lipid candidates from a peaklist based on a set of expected adducts.

Usage

findCandidates(
  MS1,
  db,
  ppm,
  rt,
  adducts,
  rttol = 3,
  dbs,
  rawData = data.frame(),
  coelCutoff = 0
)

Arguments

Details

findCandidates looks for matches between the m/z of the MS1 peaklist and the expected m/z of the candidates in the database for each adduct. If several adducts are expected, results are combined.

Adducts allowed are contained in adductsTable data frame, which can be modified if required (see adductsTable).

Value

Data frame with the found candidates. It contains 6 columns: mz, RT, int (from the peaklist data.frame), ppms, cb (total number of carbons and double bounds of the FA chains) and adducts.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

Obtain an inclusion list for the identified lipids.

Usage

getInclusionList(df, dbs)

Arguments

Value

Data frame with 6 columns: formula, RT, neutral mass, m/z, adduct and the LipidMSid.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Description

Group features from an msbatch

Usage

groupmsbatch(
  msbatch,
  dmz = 5,
  drtagglom = 30,
  drt = 15,
  minsamples,
  minsamplesfrac = 0.25,
  parallel = FALSE,
  ncores,
  deleteduplicates = TRUE,
  thr_overlap_duplicates = 0.7,
  verbose = TRUE
)

Arguments

Details

First, peak partitions are created based on the enviPick algorithm to speed up the following clustering algorithm. Briefly, peaks are ordered increasingly by mz and RT and grouped based on user-defined tolerances (dmz and drt). Each peak is initialized as a partition and then, they are evaluated to decide whether or not they can be joined to the previous partition. If mz and RT of a peak matches tolerance of any of the peaks in the previous partition, it is reassigned. Then, clustering algorithm is executed to improve these partitions based on their mz following the next steps for each partition:

1. Each peak in the partition is initialized as a new cluster. For each cluster we will keep the minimum, maximum and mean value of the mz, which at this point have the same values. 2. Calculate a distance matrix between all clusters. This distance will be the greatest difference between minimum and maximum values of each cluster. 3. While any distance is different to NA, search the minimum distance between two clusters. 4. If distance is below the maximum distance allowed, join clusters and update minimum, maximum and mean values, else, set distance to NA and go back to point 3.

Then this same clustering algorithm is executed again to group peaks based on their RT. In this case, distances between clusters which share peaks from the same samples will be set to NA.

After groups have been defined, those clusters with a sample representation over minsamples or minsamplesfrac will be used for building the feature table. Finally, if deleteduplicates is set to TRUE, peaks overlap is checked to avoid duplicated or wrongly defined features.

Value

grouped msbatch

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

References

Partitioning algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html

Examples

## Not run: 
msbatch <- groupmsbatch(msbatch)

## End(Not run)

Description

In silico generated database for common HFAs.

Usage

data("hfadb")

Format

Data frame with 30 observations and the following 3 variables.

formula

character vector containing molecular formulas.

total

character vector indicating the total number of carbons and double bounds of the chains.

Mass

numeric vector with the neutral masses.

Description

AcylCer identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idAcylCerneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H", "M+CH3COO"),
  clfrags = c(),
  clrequired = c(),
  ftype = c(),
  chainfrags_sn1 = c("cbdiff-cer_M-H"),
  chainfrags_sn2 = c("sph_Mn-62.06001", "sph_M-H-H2O"),
  chainfrags_sn3 = c("fa_Mn-1.9918", "fa_Mn-19.0179"),
  intrules = c("cbdiff-cer_sn1/sph_sn2", "sph_sn2/fa_sn3"),
  rates = c("5/1", "2/1"),
  intrequired = c(T, T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idAcylCerneg function involves 5 steps. 1) FullMS-based identification of candidate AcylCer as M-H and M+CH3COO. 2) Search of AcylCer class fragments: no class fragments by default. 3) Search of specific fragments that inform about the acyl chain (Cer as M-H), the sphingoid base (neutral loss of 62.0600 of the Sph) and the FA chain (FA as M-H and M-H2O but with a N instead of an O, what results in a mass differences of 1.9918 and 19.0179 respectively). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, the fragment coming from the loss of the acyl chain must be at least 5 times more intense the fragment from the sphingoid base and this one, two times more intense than the FA chain from sn3.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idAcylCerneg(msobject)

## End(Not run)

Description

AcylCer identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idAcylCerpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+H-H2O", "M+Na"),
  clfrags = c(),
  clrequired = c(),
  ftype = c(),
  chainfrags_sn1 = c("cbdiff-cer_M+H", "cbdiff-cer_M+H-H2O", "cbdiff-cer_M+H-2H2O"),
  chainfrags_sn2 = c("sph_M+H-H2O", "sph_M+H-2H2O"),
  chainfrags_sn3 = c("fa_Mn+0.02329"),
  intrules = c("sph_sn2/cbdiff-cer_sn1", "sph_sn2/fa_sn3"),
  rates = c("2/1", "5/1"),
  intrequired = c(T, T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idAcylCerpos function involves 5 steps. 1) FullMS-based identification of candidate AcylCer as M+H, M+H-H2O and M+Na. 2) Search of AcylCer class fragments: there are no class fragments by default. 3) Search of specific fragments that inform about the acyl chain (Cer as M+H, M+H-H2O or M+H-2H2H), the sphingoid base (Sph as M+H-H2O or M+H-2H2O) and the FA chain (FA as M+H but with a N intead of an O, what results in a mass difference of 0.02329 with the Mn of the FA chain). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, Sph fragment must be twice more intense than the loss of the acyl chain and at least 5 times more intense than the FA chain from sn3.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idCerPneg(msobject)

## End(Not run)

Description

BA identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idBAneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H"),
  conjfrag = c("baconj_M-H"),
  bafrag = c("ba_M-H-H2O", "ba_M-H-2H2O"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idBAneg function involves 3 steps. 1) FullMS-based identification of candidate BA as M-H. 2) Search of BA-conjugate fragments if required. 3) Search of fragments coming from the loss of H2O.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (MS-only if no rules are defined, or Subclass level if they are supported by fragments) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idBAneg(msobject)

## End(Not run)

Description

Acylcarnitines identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idCarpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+Na"),
  clfrags = c(60.0807, 85.0295, "fa_M+H-H2O"),
  clrequired = c(F, F, F),
  ftype = c("F", "F", "BB"),
  chainfrags_sn1 = c("fa_M+H-H2O"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idCarpos function involves 3 steps. 1) FullMS-based identification of candidate carnitines as M+H and M+Na. 2) Search of carnitine class fragments: 60.0807 and 85.0295 or its loss (FA as M+H-H20) coeluting with the precursor ion. 3) Search of specific fragments coming from the FA chain (FA as M+H-H2O).

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as Carnitines only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idCarpos(msobject)

## End(Not run)

Description

CE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idCEpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("2M+NH4", "2M+Na", "M+NH4", "M+Na"),
  clfrags = c(369.3516, "fa_M+H-H2O"),
  clrequired = c(F, F),
  ftype = c("F", "BB"),
  chainfrags_sn1 = c("fa_M+H-H2O"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idCEpos function involves 3 steps. 1) FullMS-based identification of candidate CE as 2M+NH4, 2M+Na, M+NH4 and M+Na. 2) Search of CE class fragments: 369.3516 or its loss (FA as M+H-H20) coeluting with the precursor ion. 3) Search of specific fragments that confirm chain composition (FA as M+H-H2O).

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as CE only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idCEpos(msobject)

## End(Not run)

Description

Cer identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idCerneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H", "M+CH3COO"),
  clfrags = c(),
  clrequired = c(),
  ftype = c(),
  chainfrags_sn1 = c("NL-nlsph_M-H", "sph_M-H-2H2O", "sph_M-H-H2O"),
  chainfrags_sn2 = c("fa_Mn-1.9918", "fa_M-H-H2O"),
  intrules = c(),
  rates = c(),
  intrequired = c(),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idCerneg function involves 5 steps. 1) FullMS-based identification of candidate Cer as M-H and M+CH3COO. 2) Search of Cer class fragments: there are no class fragment by default. 3) Search of specific fragments that inform about the sphingoid base (Sph as M-H-2H2O resulting from the loss of the FA chain or loss of part of the sphingoid base) and the FA chain (FA as M-H but with a N instead of an O, what means a mass difference of 1.9918 from the exact mass of the FA or FA as M-H-H2O). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, there are no intensity rules by default.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idCerneg(msobject)

## End(Not run)

Description

CerP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idCerPneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H"),
  clfrags = c(78.9585, 96.9691),
  clrequired = c(F, F),
  ftype = c("F", "F"),
  chainfrags_sn1 = c("sphP_M-H"),
  chainfrags_sn2 = c("fa_Mn-1.9918", ""),
  intrules = c(),
  rates = c(),
  intrequired = c(),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idCerPneg function involves 5 steps. 1) FullMS-based identification of candidate CerP as M-H. 2) Search of CerP class fragments: 78.9585 and 96.9691. 3) Search of specific fragments that inform about the sphingoid base (SphP as M-H resulting from the loss of the FA chain) and the FA chain (FA as M-H but with a N instead of an O, what results in a mass difference of 1.9918 from the exact mass of the FA, or the difference between precursor and sn1 chain fragments). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, there are no intensity rules by default.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idCerPneg(msobject)

## End(Not run)

Description

Ceramides identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idCerpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H-H2O", "M+Na", "M+H"),
  clfrags = c(),
  clrequired = c(),
  ftype = c(),
  chainfrags_sn1 = c("sph_M+H-2H2O"),
  chainfrags_sn2 = c(""),
  intrules = c(),
  rates = c(),
  intrequired = c(),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idCerpos function involves 5 steps. 1) FullMS-based identification of candidate Cer as M+H, M+H-H2O and M+Na. 2) Search of Cer class fragments: there isn't any class fragment by default. 3) Search of specific fragments that inform about the sphingoid base (Sph as M+H-2H2O resulting from the loss of the FA chain) and the FA chain (by default it is calculated using the difference between precursor and sph fragments). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, there are no intensity rules by default.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idCerpos(msobject)

## End(Not run)

Description

CerP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idCerPpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H"),
  clfrags = c("cer_M+H-H2O", "cer_M+H-2H2O"),
  clrequired = c(F, F),
  ftype = c("BB", "BB"),
  chainfrags_sn1 = c("sph_M+H-2H2O"),
  chainfrags_sn2 = c(""),
  intrules = c(),
  rates = c(),
  intrequired = c(),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idCerPpos function involves 5 steps. 1) FullMS-based identification of candidate CerP as M+H. 2) Search of Cer class fragments: Cer as M+H-H2O and M+H-2H2O resulting from the loss of the phosphate group and 1 or 2 H2O molecules. 3) Search of specific fragments that inform about the sphingoid base (Sph as M+H-2H2O resulting from the loss of the FA chain and the phosphate group) and the FA chain (by default it is calculated using the difference between precursor and sph fragments). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, there are no intensity rules by default.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idCerPpos(msobject)

## End(Not run)

Description

CL identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idCLneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 5,
  rt,
  adducts = c("M-H", "M+Na-2H"),
  clfrags = c(),
  clrequired = c(),
  ftype = c(),
  chainfrags_sn1 = c("lysopa_M-H-H2O"),
  chainfrags_sn2 = c("lysopa_M-H-H2O"),
  chainfrags_sn3 = c("lysopa_M-H-H2O"),
  chainfrags_sn4 = c("lysopa_M-H-H2O"),
  intrules = c("Unknown"),
  rates = c(),
  intrequired = c(),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idCLneg function involves 5 steps. 1) FullMS-based identification of candidate CL as M-H or M-2H. 2) Search of CL class fragments: no class fragments are searched by defaults as they use to have bad coelution scores. 3) Search of specific fragments that inform about chain composition at sn1 (lysoPA as M-H-H2O), sn2 (lysoPA as M-H-H2O), sn3 (lysoPA as M-H-H2O) and sn4 (lysoPA as M-H-H2O). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. For CL there are no intensity rules by default.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idCLneg(msobject)

## End(Not run)

Description

DG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idDGpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H-H2O", "M+NH4", "M+Na"),
  clfrags = c(),
  clrequired = c(),
  ftype = c(),
  chainfrags_sn1 = c("mg_M+H-H2O"),
  chainfrags_sn2 = c("mg_M+H-H2O"),
  intrules = c("mg_sn1/mg_sn2"),
  rates = c("1"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idDGpos function involves 5 steps. 1) FullMS-based identification of candidate DG as M+H-H2O, M+NH4 and M+Na. 2) Search of DG class fragments: there are no class fragment by default. 3) Search of specific fragments that inform about the FA chains (MGs as M+H-H2O resulting from the loss of the FA chains). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position: MG coming from the loss of the sn2 chain is more intense than the one coming from the loss of sn1.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idDGpos(msobject)

## End(Not run)

Description

FAHFA identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idFAHFAneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H"),
  clfrags = c(),
  clrequired = c(),
  ftype = c(),
  chainfrags_sn1 = c("hfa_M-H"),
  chainfrags_sn2 = c("fa_M-H"),
  intrules = c("hfa_sn1/fa_sn2"),
  rates = c("3/1"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idFAHFAneg function involves 5 steps. 1) FullMS-based identification of candidate FAHFA as M-H. 2) Search of FAHFA class fragments: there is't any class fragment by default. 3) Search of specific fragments that inform about chain composition in sn1 (HFA as M-H resulting from the loss of the FA chain) and sn2 (FA chain as M-H). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, HFA intensity has to be higher than FA.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idFAHFAneg(msobject)

## End(Not run)

Description

FA identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idFAneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H", "2M-H"),
  clfrags = c("fa_M-H", "fa_M-H-H2O"),
  clrequired = c(FALSE, FALSE),
  ftype = c("BB", "BB"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idFAneg function involves 2 steps. 1) FullMS-based identification of candidate FA as M-H or 2M-H. 2) Search of FA class fragments: neutral loss of H2O coeluting with the precursor ion or the molecular ion.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, just MS-only or Subclass level (if any class fragment is defined) are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idFAneg(msobject)

## End(Not run)

Description

LPC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idLPCneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+CH3COO", "M-CH3", "M+CH3COO-CH3"),
  clfrags = c(168.0426, 224.0688, "lysopa_M-H", "lysopc_M-CH3"),
  clrequired = c(F, F, F, F),
  ftype = c("F", "F", "BB", "BB"),
  chainfrags_sn1 = c("fa_M-H"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idLPCneg function involves 3 steps. 1) FullMS-based identification of candidate LPC as M+CH3COO, M-CH3 and M+CH3COO-CH3. To avoid incorrect annotations of PE as PC, candidates which are present just as M-CH3 will be ignored. 2) Search of LPC class fragments: 168.0426, 224.0688, lysoPA as M-H or lysoPC as M-CH3 coeluting with the precursor ion. 3) Search of specific fragments that confirm chain composition (FA as M-H).

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPC only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idLPCneg(msobject)

## End(Not run)

Description

LPC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idLPCpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+Na"),
  clfrags = c(104.1075, 184.0739),
  clrequired = c(F, F),
  ftype = c("F", "F"),
  chainfrags_sn1 = c("mg_M+H-H2O"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idLPCpos function involves 3 steps. 1) FullMS-based identification of candidate LPC as M+H and M+Na. 2) Search of LPC class fragments: 104.1075 and 184.0739 coeluting with the precursor ion. 3) Search of specific fragments that confirm chain composition (MG as M+H-H2O).

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPC only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idLPCpos(msobject)

## End(Not run)

Description

LPE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idLPEneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H"),
  clfrags = c(140.0115, 196.038, 214.048, "lysope_M-CH3"),
  clrequired = c(F, F, F, "excluding"),
  ftype = c("F", "F", "F", "BB"),
  chainfrags_sn1 = c("fa_M-H"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idLPEneg function involves 3 steps. 1) FullMS-based identification of candidate LPE as M-H. 2) Search of LPE class fragments: 140.0115, 196.038 and 214.048 coeluting with the precursor ion. If a loss of CH3 group is found coeluting with any candidate, this will be excluded as it is a characteristic fragment of LPC.3) Search of specific fragments that confirm chain composition (FA as M-H).

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPE only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idLPEneg(msobject)

## End(Not run)

Description

LPE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idLPEpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+Na"),
  clfrags = c(141.01909),
  clrequired = c(F),
  ftype = c("NL"),
  chainfrags_sn1 = c("mg_M+H-H2O"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idLPEpos function involves 3 steps. 1) FullMS-based identification of candidate LPE as M+H and M+Na. 2) Search of LPE class fragments: neutral loss of 141.01909 coeluting with the precursor ion. 3) Search of specific fragments that confirm chain composition in sn1 (MG as M+H-H2O).

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPE only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idLPEpos(msobject)

## End(Not run)

Description

LPG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idLPGneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H"),
  clfrags = c(152.9958, 227.0326, 209.022, 74.0359),
  clrequired = c(F, F, F, F),
  ftype = c("F", "F", "F", "NL"),
  chainfrags_sn1 = c("fa_M-H"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idLPGneg function involves 3 steps. 1) FullMS-based identification of candidate LPG as M-H. 2) Search of LPG class fragments: 152.9958, 227.0326, 209.022 and neutral loss of 74.0359 coeluting with the precursor ion. 3) Search of specific fragments that confirm chain composition (FA as M-H).

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPG only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idLPGneg(msobject)

## End(Not run)

Description

LPI identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idLPIneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H"),
  clfrags = c(241.0115, 223.0008, 259.0219, 297.0375),
  clrequired = c(F, F, F, F),
  ftype = c("F", "F", "F", "F"),
  chainfrags_sn1 = c("fa_M-H"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idLPIneg function involves 3 steps. 1) FullMS-based identification of candidate LPI as M-H. 2) Search of LPI class fragments: 241.0115, 223.0008, 259.0219 and 297.0375 coeluting with the precursor ion. 3) Search of specific fragments that confirm chain composition (FA as M-H).

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPI only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idLPIneg(msobject)

## End(Not run)

Description

LPS identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idLPSneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H", "M+Na-2H"),
  clfrags = c(87.032),
  clrequired = c(F),
  ftype = c("NL"),
  chainfrags_sn1 = c("fa_M-H"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idLPSneg function involves 3 steps. 1) FullMS-based identification of candidate LPS as M-H and M+Na-2H. 2) Search of LPS class fragments: neutral loss of 87.032 coeluting with the precursor ion. 3) Search of specific fragments that confirm chain composition (FA as M-H).

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPS only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idLPSneg(msobject)

## End(Not run)

Description

MG identification based on fragmentation patterns for LC-MS/MS DIA and DDA data acquired in positive mode.

Usage

idMGpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H-H2O", "M+NH4", "M+Na"),
  clfrags = c(),
  clrequired = c(),
  ftype = c(),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idMGpos function involves 2 steps. 1) FullMS-based identification of candidate MG as M+H-H2O, M+NH4 and M+Na. 2) Search of MG class fragments if any is assigned.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, just MS-only or Subclass level (if any class fragment is defined) are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idMGpos(msobject)

## End(Not run)

Description

Lipids annotation based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode. This function compiles all functions writen for ESI- annotations.

Usage

idNEG(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 5,
  coelCutoff = 0.8,
  lipidClasses = c("FA", "FAHFA", "LPC", "LPE", "LPG", "LPI", "LPS", "PC", "PCo", "PCp",
    "PE", "PEo", "PEp", "PG", "PI", "PS", "Sph", "SphP", "Cer", "CerP", "AcylCer", "SM",
    "CL", "BA"),
  dbs,
  verbose = TRUE
)

Arguments

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification); and the annotatedPeaklist element shows the original MS1 peaklist with the annotations on it.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idNEG(msobject)

## End(Not run)

Description

PC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idPCneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+CH3COO", "M-CH3", "M+CH3COO-CH3"),
  clfrags = c(168.0426, 224.0688, "pc_M-CH3"),
  clrequired = c(F, F, F),
  ftype = c("F", "F", "BB"),
  chainfrags_sn1 = c("lysopc_M-CH3"),
  chainfrags_sn2 = c("fa_M-H", "lysopc_M-CH3"),
  intrules = c("lysopc_sn1/lysopc_sn2"),
  rates = c("3/1"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idPCneg function involves 5 steps. 1) FullMS-based identification of candidate PC as M+CH3COO, M-CH3 or M+CH3COO-CH3. To avoid incorrect annotations of PE as PC, candidates which are present just as M-CH3 will be ignored. 2) Search of PC class fragments: 168.0426, 224.0688 or loss of CH3 coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition in sn1 (lysoPC as M-CH3 resulting from the loss of the FA chain at sn2) and sn2 (lysoPC as M-CH3 resulting from the loss of sn1 or FA as M-H). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, lysoPC from sn1 is at least 3 times more intense than lysoPC from sn2.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idPCneg(msobject)

## End(Not run)

Description

PCo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idPConeg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+CH3COO", "M-CH3", "M+CH3COO-CH3"),
  clfrags = c(168.0426, 224.0688, "pco_M-CH3"),
  clrequired = c(F, F, F),
  ftype = c("F", "F", "BB"),
  chainfrags_sn1 = c("lysopco_M-CH3", "lysopco_M-CH3-H2O"),
  chainfrags_sn2 = c("fa_M-H", "fa_M-CO2-H"),
  intrules = c("lysopco_sn1/fa_sn2"),
  rates = c(1/3),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idPConeg function involves 5 steps. 1) FullMS-based identification of candidate PCo as M+CH3COO, M-CH3 or M+CH3COO-CH3. To avoid incorrect annotations of PEo as PCo, candidates which are present just as M-CH3 will be ignored. 2) Search of PCo class fragments: 168.0426, 224.0688 or loss of CH3 coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition in sn1 (LPCo as M-CH3 and M-CH3-H2O resulting from the loss of the FA chain at sn2) and sn2 (FA as M-H and M-CO2-H). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, FA fragments from sn2 are at least 3 times more intense than LPCo from sn1.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idPCneg(msobject)

## End(Not run)

Description

PCo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idPCopos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+Na"),
  clfrags = c(104.1075, 184.0739, 183.06604),
  clrequired = c(F, F, F),
  ftype = c("F", "F", "NL"),
  chainfrags_sn1 = c("lysopco_M+H", "lysopco_M+H-H2O"),
  chainfrags_sn2 = c("lysopc_M+H", "lysopc_M+H-H2O", ""),
  intrules = c("lysopco_sn1/lysopc_sn2"),
  rates = c("2/1"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idPCopos function involves 5 steps. 1) FullMS-based identification of candidate PCo as M+H and M+Na. 2) Search of PC class fragments: 104.1075, 184.0739 and neutral loss of 183.06604 coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition in sn1 (LPCo as M+H or M+H-H2O resulting from the loss of the FA chain at sn2) and sn2 (LPC as M+H-H2O resulting from the loss of the FA chain at sn1 or the difference between precursor and sn1 chain fragments). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, LPCo from sn1 is at least twice more intense than LPC from sn2.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idPCopos(msobject)

## End(Not run)

Description

PCp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idPCpneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+CH3COO", "M-CH3", "M+CH3COO-CH3"),
  clfrags = c(168.0426, 224.0688, "pcp_M-CH3"),
  clrequired = c(F, F, F),
  ftype = c("F", "F", "BB"),
  chainfrags_sn1 = c("lysopcp_M-CH3", "lysopcp_M-CH3-H2O"),
  chainfrags_sn2 = c("fa_M-H", "fa_M-CO2-H"),
  intrules = c("lysopcp_sn1/fa_sn2"),
  rates = c(1/3),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idPCpneg function involves 5 steps. 1) FullMS-based identification of candidate PCp as M+CH3COO, M-CH3 or M+CH3COO-CH3. To avoid incorrect annotations of PEp as PCp, candidates which are present just as M-CH3 will be ignored. 2) Search of PCp class fragments: 168.0426, 224.0688 or loss of CH3 coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition in sn1 (LPCp as M-CH3 and M-CH3-H2O resulting from the loss of the FA chain at sn2) and sn2 (FA as M-H and M-CO2-H). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, FA fragments from sn2 are at least 3 times more intense than LPCp from sn1.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idPCpneg(msobject)

## End(Not run)

Description

PC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idPCpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+Na"),
  clfrags = c(104.1075, 184.0739, 183.06604),
  clrequired = c(F, F, F),
  ftype = c("F", "F", "NL"),
  chainfrags_sn1 = c("lysopc_M+H", "lysopc_M+H-H2O"),
  chainfrags_sn2 = c("lysopc_M+H", "lysopc_M+H-H2O", ""),
  intrules = c("lysopc_sn1/lysopc_sn2"),
  rates = c("2/1"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idPCpos function involves 5 steps. 1) FullMS-based identification of candidate PC as M+H and M+Na. 2) Search of PC class fragments: 104.1075, 184.0739 and neutral loss of 183.06604 coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition in sn1 (lysoPC as M+H or M+H-H2O resulting from the loss of the FA chain at sn2) and sn2 (lysoPC as M+H or M+H-H2O resulting from the loss of the FA chain at sn1 or the difference between precursor and sn1 chain fragments). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, lysoPC from sn1 is at least twice more intense than lysoPC from sn2.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idPCpos(msobject)

## End(Not run)

Description

PCp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idPCppos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+Na"),
  clfrags = c(104.1075, 184.0739, 183.06604),
  clrequired = c(F, F, F),
  ftype = c("F", "F", "NL"),
  chainfrags_sn1 = c("lysopcp_M+H", "lysopcp_M+H-H2O"),
  chainfrags_sn2 = c("lysopc_M+H-H2O", ""),
  intrules = c("lysopcp_sn1/lysopc_sn2"),
  rates = c("1/2"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idPCppos function involves 5 steps. 1) FullMS-based identification of candidate PC as M+H and M+Na. 2) Search of PC class fragments: 104.1075, 184.0739 and neutral loss of 183.06604 coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition in sn1 (LPCp as M+H or M+H-H2O resulting from the loss of the FA chain at sn2) and sn2 (LPC as M+H-H2O resulting from the loss of the FA chain at sn1 or the difference between precursor and sn1 chain fragments). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, LPC from sn2 is at least twice more intense than LPCo from sn1.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idPCppos(msobject)

## End(Not run)

Description

PE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idPEneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 5,
  rt,
  adducts = c("M-H"),
  clfrags = c(140.0118, 196.038, 214.048, "pe_M-CH3"),
  clrequired = c(F, F, F, "excluding"),
  ftype = c("F", "F", "F", "BB"),
  chainfrags_sn1 = c("lysope_M-H"),
  chainfrags_sn2 = c("lysope_M-H", "fa_M-H"),
  intrules = c("lysope_sn1/lysope_sn2"),
  rates = c("3/1"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idPEneg function involves 5 steps. 1) FullMS-based identification of candidate PE as M-H. 2) Search of PE class fragments: 140.0115, 196.038, 214.048 ion coeluting with the precursor ion. If a loss of CH3 group is found coeluting with any candidate, this will be excluded as it is a characteristic fragment of PC. 3) Search of specific fragments that inform about chain composition in sn1 (lysoPE as M-H resulting from the loss of the FA chain at sn2) and sn2 (lysoPE as M-H resulting from the loss of the FA chain at sn1 or FA chain as M-H). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, lysoPE from sn1 is at least 3 times more intense than lysoPE from sn2.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idPEneg(msobject)

## End(Not run)

Description

PEo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.

Usage

idPEoneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 5,
  rt,
  adducts = c("M-H", "M+NaCH3COO"),
  clfrags = c(140.0118, 196.038, 214.048, "peo_M-CH3"),
  clrequired = c(F, F, F, "excluding"),
  ftype = c("F", "F", "F", "BB"),
  chainfrags_sn1 = c("lysopeo_M-H", "lysopeo_M-H-H2O"),
  chainfrags_sn2 = c("fa_M-H"),
  intrules = c("lysopeo_sn1/fa_sn2"),
  rates = c(1/3),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

Details

idPEoneg function involves 5 steps. 1) FullMS-based identification of candidate PEo as M-H and M+NaCH3COO. 2) Search of PEo class fragments: 140.0115, 196.038, 214.048 ion coeluting with the precursor ion. If a loss of CH3 group is found coeluting with any candidate, this will be excluded as it is a characteristic fragment of PCo. 3) Search of specific fragments that inform about chain composition in sn1 (lysoPEo as M-H and M-H-H2O resulting from the loss of the FA chain at sn2) and sn2 (FA chain as M-H). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. In this case, FA fragments from sn2 are at least 3 times more intense than LPEo from sn1.

Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Value

annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).

Note

This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msobject <- idPEoneg(msobject)

## End(Not run)

Description

PEo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.

Usage

idPEopos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+Na"),
  clfrags = c(141.0193),
  clrequired = c(F),
  ftype = c("NL"),
  chainfrags_sn1 = c("lysopeo_M+H", "lysopeo_M+H-H2O"),
  chainfrags_sn2 = c("mg_M+H-H2O"),
  intrules = c("lysopeo_sn1/mg_sn2"),
  rates = c("2/1"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)