Package 'LipidMS'

Title: Lipid Annotation for LC-MS/MS DDA or DIA Data
Description: Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>.
Authors: M Isabel Alcoriza-Balaguer
Maintainer: M Isabel Alcoriza-Balaguer <[email protected]>
License: GPL (>= 2)
Version: 3.0.6
Built: 2025-03-01 07:08:50 UTC
Source: https://github.com/maialba3/lipidms

Help Index


AcylCeramides database

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.


Adducts table

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.


Align samples from an msbatch

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

msbatch

msbatch obtained from the setmsbatch function.

dmz

mass tolerance between peak groups in ppm.

drt

maximum rt distance between peaks for alignment in seconds.

minsamples

minimum number of samples represented in each cluster used for the alignment.

minsamplesfrac

minimum samples fraction represented in each cluster used for the alignment. Used to calculate minsamples in case it is missing.

span

span parameter for loess rt deviation smoothing.

parallel

logical. If TRUE, parallel processing will be performed.

ncores

number of cores to be used in case parallel is TRUE.

verbose

print information messages.

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)

Lipid annotation for an msbatch

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

msbatch

msbatch

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 5 seconds.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

lipidClassesPos

classes of interest in ESI+.

lipidClassesNeg

classes of interest in ESI-.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

simplifyAnnotations

logical. If TRUE, only the most frequent id will be kept (recommended when only pool samples have been acquired in DIA or DDA). If FALSE, all annotations will be shown.

parallel

logical.

ncores

number of cores to be used in case parallel is TRUE.

Value

msbatch

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

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

msbatch$features

## End(Not run)

Load LipidMS default data bases

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)

Bile acids conjugates database

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.


Bile acids database

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.


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

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

files

file paths of the mzXML files. Optional.

metadata

csv file or data.frame with 3 columns: sample (samples named as the mzXML files), acquisitionmode (MS, DIA or DDA) and groups (i.e. blank, QC, sample). DIA, DDA and MS files are allowed, but only DIA and DDA files will be used for lipid annotation.

polarity

character value: negative or positive.

dmzagglom

mz tolerance (in ppm) used for partitioning and clustering.

drtagglom

rt window used for partitioning (in seconds).

drtclust

rt window used for clustering (in seconds).

minpeak

minimum number of measurements required for a peak.

drtgap

maximum RT gap length to be filled (in seconds).

drtminpeak

minimum RT width of a peak (in seconds). At least minpeak within the drtminpeak window are required to define a peak.

drtmaxpeak

maximum RT width of a single peak (in seconds).

recurs

maximum number of peaks within one EIC.

sb

signal-to-base ratio.

sn

signal-to-noise ratio.

minint

minimum intensity of a peak.

weight

weight for assigning measurements to a peak.

dmzIso

mass tolerance for isotope matching.

drtIso

time windows for isotope matching.

parallel

logical.

ncores

number of cores to be used in case parallel is TRUE.

verbose

print information messages.

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)

Carnitines database

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.


CEs database

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.


Ceramides database

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.


Ceramides Phosphate database

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.


Search of chain specific fragments

Description

Search of specific fragments that inform about the chains structure.

Usage

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

Arguments

coelfrags

coeluting fragments for each candidate. Output of coelutingFrags.

chainfrags

character vector containing the fragmentation rules for the chain fragments. If it is an empty vector, chains will be calculated based on the difference between the precursor and the other chain. See details.

ppm

m/z tolerance in ppm.

candidates

candidates data frame. If any chain needs to be calculated based on the difference between the precursor and the other chain, this argument will be required. Output of chainFrags.

f

known chains. If any chain needs to be calculated based on the difference between the precursor and the other chain, this argument will be required. Output of chainFrags.

dbs

list of data bases required for the annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be changed. If data bases have been customized using createLipidDB, they also have to be modified here.

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]>


Search of class fragments to confirm the lipid class.

Description

Search of characteristic fragments that confirm a given lipid class.

Usage

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

Arguments

candidates

output of findCandidates function.

coelfrags

list of peaks coeluting with each candidate. Output of coelutingFrags.

clfrags

vector containing the expected fragments for a given lipid class. See details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See details.

clrequisites

logical vector indicating if each class fragment is required or not. If none of the fragment is required, at least one of them must be present within the coeluting fragments. If the presence of any fragment excludes the class, it can be specified by using "excluding".

ppm

m/z tolerance in ppm.

dbs

list of data bases required for the annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be changed. If data bases have been customized using createLipidDB, they also have to be modified here. It is employed when some fragment belongs to "BB" ftype.

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]>


Check intensity rules

Description

Check intensity rules to confirm chains position.

Usage

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

Arguments

intrules

character vector specifying the fragments to compare. See details.

rates

character vector with the expected rates between fragments given as a string (i.e. "3/1"). See details.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

nchains

number of chains of the targeted lipid class.

combinations

output of combineChains.

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]>


Cardiolipins database

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.


Coeluting fragments extraction

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

precursors

candidates data frame. Output of findCandidates.

products

peaklist for MS2 function (MSMS).

rttol

rt window in seconds.

rawData

raw scans data. Output of dataProcessing function (MSMS$rawData).

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied.

Value

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

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


calculate coelution score between two peaks

Description

Calculate coelution score between two peaks.

Usage

coelutionScore(peak1, peak2, rawData)

Arguments

peak1

character vector specifying the peakID of the first peak.

peak2

character vector specifying the peakID of the second peak.

rawData

data frame with raw data for each scan. it need to have at least 5 columns: mz, RT, int, Scan (ordinal number for a given MS function) and peakID (peakID to which it has been assigned).

#' @keywords internal

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


Combine chain fragments that could belong to the same precursor.

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

candidates

candidates data frame. Output of findCandidates.

nchains

number of chains of the targeted lipid class.

sn1

list of chain fragments identified for sn1 position. Output of chainFrags.

sn2

list of chain fragments identified for sn2 position. Output of chainFrags. If required.

sn3

list of chain fragments identified for sn3 position. Output of chainFrags. If required.

sn4

list of chain fragments identified for sn4 position. Output of chainFrags. If required.

Value

List of data frames with candidate chains structures.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


Confidence Annotation Levels

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.


Customizable lipid DBs creator

Description

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

Usage

createLipidDB(lipid, chains, chains2)

Arguments

lipid

character value indicating the class of lipid. See Details.

chains

character vector indicating the FA chains to be employed

chains2

character vector containing the sphingoid bases to be employed if required.

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)

Cross the original MS1 peaklist with the annotation results

Description

Cross the original MS1 peaklist with the annotation results.

Usage

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

Arguments

msobject

annotated msobject

ppm

mass tolerance in ppm.

rttol

rt tolerance to match peaks in seconds.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

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]>


Process mzXML files individually: peakpicking and isotope annotation

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

file

file path.

acquisitionmode

character value: MS, DIA or DDA.

polarity

character value: negative or positive.

dmzagglom

mz tolerance (in ppm) used for partitioning and clustering.

drtagglom

RT window used for partitioning (in seconds).

drtclust

RT window used for clustering (in seconds).

minpeak

minimum number of measurements required for a peak.

drtgap

maximum RT gap length to be filled (in seconds).

drtminpeak

minimum RT width of a peak (in seconds). At least minpeak within the drtminpeak window are required to define a peak.

drtmaxpeak

maximum RT width of a single peak (in seconds).

recurs

maximum number of peaks within one EIC.

sb

signal-to-base ratio.

sn

signal-to-noise ratio.

minint

minimum intensity of a peak.

weight

weight for assigning measurements to a peak.

dmzIso

mass tolerance for isotope matching.

drtIso

time window for isotope matching.

verbose

print information messages.

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)

MS/MS scan extraction of a precursor in DDA

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

candidates

candidates data frame. Output of findCandidates.

precursors

data frame with the whole list of precursors selected for MS2.

rawData

peaklist for MS2 function (MSMS).

ppm

m/z tolerance in ppm.

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]>


DGs database

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.


FAs database

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.


FAHFAs database

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.


Fill peaks from a grouped msbatch

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

msbatch

msbatch obtained from the groupmsbatch function.

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)

Search of lipid candidates of a certain class

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

MS1

peaklist of the MS function. Data frame with 3 columns: mz, RT (in seconds) and int (intensity).

db

database (i.e. pcdb, dgdb, etc.). Data frame with at least 2 columns: Mass (exact mass) and total (total number of carbons and double bound of the FA chains, i.e. "34:1").

ppm

m/z tolerance in ppm.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

character vector containing the expected adducts to search for (i.e. "M+H", "M+Na", "M-H", etc.). See details.

rttol

rt tolerance in seconds to match adducts.

dbs

list of data bases required for the annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be changed. If data bases have been customized using createLipidDB, they also have to be modified here.

rawData

raw scans data. Output of dataProcessing function (MS1$rawData).

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied.

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]>


Obtain an inclusion list from the annotation results

Description

Obtain an inclusion list for the identified lipids.

Usage

getInclusionList(df, dbs)

Arguments

df

data frame. Output of identification functions (results table from an msobject or feature table from an msbatch).

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

Value

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

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


Group features from an msbatch

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

msbatch

msbatch obtained from setmsbatch or alignmsbatch functions.

dmz

mass tolerance between peak groups for grouping in ppm.

drtagglom

rt window for mz partitioning.

drt

rt window for peaks clustering.

minsamples

minimum number of samples represented in clusters used for grouping.

minsamplesfrac

minimum samples fraction represented in each cluster used for grouping. Used to calculate minsamples in case it is missing.

parallel

logical. If TRUE, parallel processing is performed.

ncores

number of cores to be used in case parallel is TRUE.

deleteduplicates

logical. Whether or not duplicated features should be removed after grouping based on the overlap between peak limits. dmz and drt parameters are used to filter the potential duplicates.

thr_overlap_duplicates

numeric value between 0 and 1 to establish the percentage of overlap threshold to consider two features as duplicated.

verbose

print information messages.

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)

HFAs database

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.


Acylceramides (AcylCer) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for AcylCer in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the sphingoid base. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

chainfrags_sn3

character vector containing the fragmentation rules for the acyl chain. See chainFrags for details.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected ratesbetween fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Acylceramides (AcylCer) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for Cer in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the sphingoid base. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

chainfrags_sn3

character vector containing the fragmentation rules for the acyl chain. See chainFrags for details.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected ratesbetween fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Bile Acids (BA) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for BA in ESI-. Adducts allowed can be modified in the adducsTable (dbs argument).

conjfrag

character vector containing the fragmentation rules for the BA-conjugates. By default just taurine and glycine are considered, but baconjdb can be modified to add more possible conjugates. See chainFrags for details. It can also be an empty vector.

bafrag

character vector containing the fragmentation rules for other BA fragments. See chainFrags for details. It can be an empty vector.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Acylcarnitine annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for Carnitines in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments. See chainFrags for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Cholesteryl Esters (CE) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for CE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments. See chainFrags for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Ceramides (Cer) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for Cer in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected ratesbetween fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Ceramides phosphate (CerP) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for CerP in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected ratesbetween fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Ceramides (Cer) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for Cer in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between peaks (adducts, parent and fragment ions...). Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Ceramides phosphate (CerP) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for Cer in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between peaks (adducts, parent and fragment ions...). Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Cardiolipines (CL) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for CL in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details.

chainfrags_sn3

character vector containing the fragmentation rules for the chain fragments in sn3 position. See chainFrags for details.

chainfrags_sn4

character vector containing the fragmentation rules for the chain fragments in sn4 position. See chainFrags for details.

intrules

character vector specifying the fragments to compare. See checkIntensityRules. If some intensity rules should be employed to identify the chains position but they are't known yet, use "Unknown". If it isn't required, leave an empty vector.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Diacylglycerols (DG) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for DG in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

FAHFA annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for FAHFA in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Fatty Acids (FA) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for FA in ESI-. Adducts allowed can be modified in addutcsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Lysophosphocholines (LPC) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for LPC in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments. See chainFrags for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Lysophosphocholines (LPC) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for LPC in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments. See chainFrags for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Lysophosphoethanolamines (LPE) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for LPE in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments. See chainFrags for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Lysophosphoethanolamines (LPE) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for LPE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments. See chainFrags for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Lysophosphoglycerols (LPG) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for LPG in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments. See chainFrags for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Lysophosphoinositols (LPI) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for LPI in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments. See chainFrags for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Lysophosphoserines (LPS) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for LPS in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments. See chainFrags for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Monoacylglycerol (MG) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for MG in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Lipids annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 5 seconds.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

lipidClasses

classes of interest to run the identification functions.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Phosphocholines (PC) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PC in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Plasmanyl Phosphocholines (PCo) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PCo in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Plasmanyl Phosphocholines (PCo) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PC in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Plasmenyl Phosphocholines (PCp) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PCp in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Phosphocholines (PC) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PC in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Plasmenyl Phosphocholines (PCp) annotation for ESI+

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PC in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Phosphoethanolamines (PE) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PE in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Plasmanyl Phosphoethanolamines (PEo) annotation for ESI-

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

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PEo in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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)

Plasmanyl Phosphoethanolamines (PEo) annotation for ESI+

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
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idPEopos function involves 5 steps. 1) FullMS-based identification of candidate PE as M+H and M+Na. 2) Search of PE class fragments: loss of head group (NL of 141.0193) coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition at sn1 (LPEo as M+H or M+H-H2O resulting from the loss of the FA chain at sn2) and sn2 (MG as M+H-H2O resulting just from the loss of the head group and the FA chain at sn1). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. LPEo from sn1 is at least 2 times more intense than MG 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 <- idPEopos(msobject)

## End(Not run)

Plasmenyl Phosphoethanolamines (PEp) annotation for ESI-

Description

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

Usage

idPEpneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 5,
  rt,
  adducts = c("M-H", "M+NaCH3COO"),
  clfrags = c(140.0118, 196.038, 214.048, "pep_M-CH3"),
  clrequired = c(F, F, F, "excluding"),
  ftype = c("F", "F", "F", "BB"),
  chainfrags_sn1 = c("lysopep_M-H", "lysopep_M-H-H2O"),
  chainfrags_sn2 = c("fa_M-H"),
  intrules = c("lysopep_sn1/fa_sn2"),
  rates = c(1/3),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PEp in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idPEpneg function involves 5 steps. 1) FullMS-based identification of candidate PEp as M-H and M+NaCH3COO. 2) Search of PEp 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 PCp. 3) Search of specific fragments that inform about chain composition in sn1 (lysoPEp 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 LPEp 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)

Phosphoethanolamines (PE) annotation for ESI+

Description

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

Usage

idPEpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+Na"),
  clfrags = c("dg_M+H-H2O"),
  clrequired = c(F),
  ftype = c("BB"),
  chainfrags_sn1 = c("lysope_M+H-H2O", "mg_M+H-H2O"),
  chainfrags_sn2 = c("mg_M+H-H2O"),
  intrules = c("lysope_sn1/lysope_sn1", "mg_sn1/mg_sn2"),
  rates = c("3/1", "1/2"),
  intrequired = c(F, F),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idPEpos function involves 5 steps. 1) FullMS-based identification of candidate PE as M+H and M+Na. 2) Search of PE class fragments: loss of head group (DG as M+H-H2O) coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition at sn1 (MG as M+H-H2O resulting from the loss of the FA chain at sn2 and the head group or LPE as M+H-H2O resulting just from the loss of the FA chain) and sn2 (MG as M+H-H2O resulting from the loss of the head group and FA chain from sn2). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. LPE or MG from sn1 is at least 3 times more intense than the ones 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 <- idPEpos(msobject)

## End(Not run)

Plasmenyl Phosphoethanolamines (PEp) annotation for ESI+

Description

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

Usage

idPEppos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+Na"),
  clfrags = c(140.012),
  clrequired = c(F),
  ftype = c("NL"),
  chainfrags_sn1 = c("lysopep_M+H", "lysopep_M+H-H2O"),
  chainfrags_sn2 = c("mg_M+H-H2O"),
  intrules = c("lysopep_sn1/mg_sn2"),
  rates = c("1/3"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idPEppos function involves 5 steps. 1) FullMS-based identification of candidate PE as M+H and M+Na. 2) Search of PE class fragments: loss of head group (NL of 140.012) coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition at sn1 (LPEp as M+H or M+H-H2O resulting from the loss of the FA chain at sn2) and sn2 (MG as M+H-H2O from sn2 resulting from the loss of the FA chain at sn1). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. MG from sn2 is at least 3 times more intense than LPEp 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 <- idPEppos(msobject)

## End(Not run)

Phosphoglycerols (PG) annotation for ESI-

Description

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

Usage

idPGneg(
  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("lysopg_M-H"),
  chainfrags_sn2 = c("lysopg_M-H", "fa_M-H"),
  intrules = c("lysopg_sn1/lysopg_sn2"),
  rates = c("2/1"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PG in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idPGneg function involves 5 steps. 1) FullMS-based identification of candidate PG as M-H. 2) Search of PG 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 inform about chain composition at sn1 (lysoPG as M-H resulting from the loss of the FA chain at sn2) and sn2 (lysoPG 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, lysoPG from sn1 is at least 3 times more intense than lysoPG 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 <- idPGneg(msobject)

## End(Not run)

Phosphoglycerols (PG) annotation for ESI+

Description

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

Usage

idPGpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+NH4", "M+Na"),
  clfrags = c("dg_M+H-H2O"),
  clrequired = c(F),
  ftype = c("BB"),
  chainfrags_sn1 = c("mg_M+H-H2O"),
  chainfrags_sn2 = c("mg_M+H-H2O"),
  intrules = c("mg_sn1/mg_sn2"),
  rates = c("1/2"),
  intrequired = c(F),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idPGpos function involves 5 steps. 1) FullMS-based identification of candidate PG as M+H, M+NH4 and M+Na. 2) Search of PG class fragments: loss of head group (DG as M+H-H2O) coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition at sn1 (MG as M+H-H2O resulting from the loss of the FA chain at sn2) and sn2 (MG as M+H-H2O resulting from the loss of the FA chain at sn1). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. MG from sn2 is at least twice more intense than the one 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 <- idPGpos(msobject)

## End(Not run)

Phosphoinositols (PI) annotation for ESI-

Description

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

Usage

idPIneg(
  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("lysopi_M-H", "lysopa_M-H"),
  chainfrags_sn2 = c("lysopi_M-H", "lysopa_M-H", "fa_M-H"),
  intrules = c("lysopi_sn1/lysopi_sn2", "lysopa_sn1/lysopa_sn2"),
  rates = c("3/1", "3/1"),
  intrequired = c(F, F),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PI in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idPIneg function involves 5 steps. 1) FullMS-based identification of candidate PI as M-H. 2) Search of PI class fragments: 241.0115, 223.0008, 259.0219 and 297.0375 coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition at sn1 (lysoPI as M-H resulting from the loss of the FA chain at sn2 or lysoPA as M-H if it also losses the head group) and sn2 (lysoPI or lysoPA 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, lysoPI or lysoPA from sn1 is at least 3 times more intense than lysoPI or lysoPA 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 <- idPIneg(msobject)

## End(Not run)

Phosphoinositols (PI) annotation for ESI+

Description

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

Usage

idPIpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H", "M+NH4", "M+Na"),
  clfrags = c("dg_M+H-H2O"),
  clrequired = c(F),
  ftype = c("BB"),
  chainfrags_sn1 = c("mg_M+H-H2O", "lysopi_M+H-H2O"),
  chainfrags_sn2 = c("mg_M+H-H2O", "lysopi_M+H-H2O"),
  intrules = c("mg_sn1/mg_sn2", "lysopi_sn1/lysopi_sn2"),
  rates = c("2/1", "2/1"),
  intrequired = c(F, F),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idPIpos function involves 5 steps. 1) FullMS-based identification of candidate PI as M+H, M+NH4 and M+Na. 2) Search of PI class fragments: loss of head group (DG as M+H-H2O) coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition at sn1 (MG as M+H-H2O or LPI as M+H-H2O resulting from the loss of the FA chain at sn2) and sn2 (MG as M+H-H2O or LPI as M+H-H2O resulting from the loss of the FA chain at sn1). 4) Look for possible chains structure based on the combination of chain fragments. 5) Check intensity rules to confirm chains position. MG or LPI from sn1 are at least twice more intense than the ones 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 <- idPIpos(msobject)

## End(Not run)

Lipids annotation for ESI+

Description

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

Usage

idPOS(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 5,
  coelCutoff = 0.8,
  lipidClasses = c("MG", "LPC", "LPE", "PC", "PCo", "PCp", "PE", "PEo", "PEp", "PG",
    "PI", "Sph", "SphP", "Cer", "AcylCer", "CerP", "SM", "Carnitines", "CE", "DG", "TG"),
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 5 seconds.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

lipidClasses

classes of interest to run the identification functions.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

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 <- idPOS(msobject)

## End(Not run)

Phosphoserines (PS) annotation for ESI-

Description

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

Usage

idPSneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H", "M+Na-2H"),
  clfrags = c(87.032, 152.9958),
  clrequired = c(F, F),
  ftype = c("NL", "F"),
  chainfrags_sn1 = c("lysopa_M-H", "lysopa_M-H-H2O"),
  chainfrags_sn2 = c("lysopa_M-H", "lysopa_M-H-H2O", "fa_M-H"),
  intrules = c("lysopa_sn1/lysopa_sn2"),
  rates = c("3/1"),
  intrequired = c(T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PS in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idPSneg function involves 5 steps. 1) FullMS-based identification of candidate PS as M-H or M+Na-2H. 2) Search of PS class fragments: neutral loss of 87.032 (serine) coeluting with the precursor ion. 3) Search of specific fragments that inform about chain composition at sn1 (lysoPA as M-H or M-H-H2O resulting from the loss of the FA chain at sn2 and the head group) and sn2 (lysoPA as M-H or M-H-H2O resulting from the loss of the FA chain at sn1 and the head group 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, lysoPA from sn1 is at least 3 times more intense than lysoPA 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 <- idPSneg(msobject)

## End(Not run)

Sphingomyelines (SM) annotation for ESI-

Description

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

Usage

idSMneg(
  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, "sm_M-CH3"),
  clrequired = c(F, F, F),
  ftype = c("F", "F", "BB"),
  chainfrags_sn1 = c("sph_Mn+150.032"),
  chainfrags_sn2 = c("fa_Mn-1.9918", ""),
  intrules = c(),
  rates = c(),
  intrequired = c(),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for PC in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idSMneg function involves 5 steps. 1) FullMS-based identification of candidate SM as M+CH3COO, M-CH3 or M+CH3COO-CH3. 2) Search of SM 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 (Sph+phosphocholine as M-CH3-H2O which results in a mass difference of Sph+150.032) and sn2 (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 <- idSMneg(msobject)

## End(Not run)

Sphyngomyelines (SM) annotation for ESI+

Description

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

Usage

idSMpos(
  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("sph_M+H-2H2O"),
  chainfrags_sn2 = c(""),
  intrules = c(),
  rates = c(),
  intrequired = c(),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for SM in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idSMpos function involves 5 steps. 1) FullMS-based identification of candidate SM as M+H and M+Na. 2) Search of SM 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 the composition of 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 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 as FA chain is unlikely to be detected.

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 <- idSMpos(msobject)

## End(Not run)

Sphingoid bases (Sph) annotation for ESI-

Description

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

Usage

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

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for Sph in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idSphneg function involves 2 steps. 1) FullMS-based identification of candidate Sph as M-H. 2) Search of Sph class fragments: neutral loss of 1 or 2 H2O molecules.

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 Sph 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 <- idSphneg(msobject)

## End(Not run)

Sphingoid bases phosphate (SphP) annotation for ESI-

Description

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

Usage

idSphPneg(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M-H"),
  clfrags = c(78.9585, 96.9691, "sphP_M-H-H2O"),
  clrequired = c(F, F, F),
  ftype = c("F", "F", "BB"),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for SphP in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idSphpos function involves 2 steps. 1) FullMS-based identification of candidate SphP as M-H. 2) Search of SphP class fragments: 78.9585, 96.969 or neutral loss of 1 H2O molecule.

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 SphP 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 <- idSphPneg(msobject)

## End(Not run)

Sphingoid bases (Sph) annotation for ESI-

Description

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

Usage

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

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursors and product ions. By default, 3 seconds.

rt

rt window where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for Sph in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idSphpos function involves 2 steps. 1) FullMS-based identification of candidate Sph as M+H. 2) Search of Sph class fragments: neutral loss of 1 or 2 H2O molecules.

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 Sph 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 <- idSphpos(msobject)

## End(Not run)

Sphingoid bases phosphate (SphP) annotation for ESI+

Description

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

Usage

idSphPpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+H"),
  clfrags = c("sphP_M+H-H2O", "sphP_M+H-2H2O", "sphP_M+H-H2O-NH4"),
  clrequired = c(F, F, F),
  ftype = c("BB", "BB", "BB"),
  coelCutoff = 0.7,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursors and product ions. By default, 3 seconds.

rt

rt window where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for Sph in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idSphPpos function involves 2 steps. 1) FullMS-based identification of candidate SphP as M+H. 2) Search of SphP class fragments: neutral loss of 1 or 2 H2O molecules, or H2O and NH4.

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 SphP 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 <- idSphPpos(msobject)

## End(Not run)

Triacylglycerols (TG) annotation for ESI+

Description

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

Usage

idTGpos(
  msobject,
  ppm_precursor = 5,
  ppm_products = 10,
  rttol = 3,
  rt,
  adducts = c("M+NH4", "M+Na"),
  clfrags = c(),
  clrequired = c(),
  ftype = c(),
  chainfrags_sn1 = c("cbdiff-dg_M+H-H2O"),
  chainfrags_sn2 = c("cbdiff-dg_M+H-H2O"),
  chainfrags_sn3 = c("cbdiff-dg_M+H-H2O"),
  intrules = c("cbdiff-dg_sn2/cbdiff-dg_sn1", "cbdiff-dg_sn2/cbdiff-dg_sn3",
    "cbdiff-dg_sn1/cbdiff-dg_sn3"),
  rates = c("1", "1", "1"),
  intrequired = c(T, T, T),
  coelCutoff = 0.8,
  dbs,
  verbose = TRUE
)

Arguments

msobject

an msobject returned by dataProcessing.

ppm_precursor

mass tolerance for precursor ions. By default, 5 ppm.

ppm_products

mass tolerance for product ions. By default, 10 ppm.

rttol

total rt window for coelution between precursor and product ions. By default, 3 seconds.

rt

rt range where the function will look for candidates. By default, it will search within all RT range in MS1.

adducts

expected adducts for TG in ESI+. Adducts allowed can be modified in adductsTable (dbs argument).

clfrags

vector containing the expected fragments for a given lipid class. See checkClass for details.

clrequired

logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details.

ftype

character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details.

chainfrags_sn1

character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.

chainfrags_sn2

character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains.

chainfrags_sn3

character vector containing the fragmentation rules for the chain fragments in sn3 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn2 chains.

intrules

character vector specifying the fragments to compare. See checkIntensityRules. If some intensity rules should be employed to identify the chains position but they are't known yet, use "Unknown". If it isn't required, leave an empty vector.

rates

character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules.

intrequired

logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.

coelCutoff

coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

verbose

print information messages.

Details

idTGpos function involves 5 steps. 1) FullMS-based identification of candidate TG as M+NH4 and M+Na. 2) Search of TG class fragments: there are no class fragment by default. 3) Search of specific fragments that inform about the FA chains: DGs resulting from the loss of FA chains 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 the case of TG, DG resulting from the loss of sn2 if the most intense, followed by the loss of sn1 and sn3, but this FA position level still needs to be improved due to the high level of coelution for TG.

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 <- idTGpos(msobject)

## End(Not run)

LipidMS shiny app

Description

Interactive UI for LipidMS

Usage

LipidMSapp()

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
# example data files can be download from github.com/maialba3/LipidMSv2.0_exampleFiles

library(LipidMS)
LipidMSapp()

## End(Not run)

LPAs database

Description

In silico generated database for common LPAs.

Usage

data("lysopadb")

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.


O-LPA database

Description

In silico generated database for common O-LPA.

Usage

data("lysopaodb")

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.


LPCs database

Description

In silico generated database for common LPCs.

Usage

data("lysopcdb")

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.


O-LPC database

Description

In silico generated database for common O-LPC.

Usage

data("lysopcodb")

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.


P-LPC database

Description

In silico generated database for common P-LPC.

Usage

data("lysopcpdb")

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.


LPEs database

Description

In silico generated database for common LPEs.

Usage

data("lysopedb")

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.


O-LPE database

Description

In silico generated database for common O-LPE.

Usage

data("lysopeodb")

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.


P-LPE database

Description

In silico generated database for common P-LPE.

Usage

data("lysopepdb")

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.


LPGs database

Description

In silico generated database for common LPGs.

Usage

data("lysopgdb")

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.


LPIs database

Description

In silico generated database for common LPIs.

Usage

data("lysopidb")

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.


LPSs database

Description

In silico generated database for common LPSs

Usage

data("lysopsdb")

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.


MGs database

Description

In silico generated database for common MGs.

Usage

data("mgdb")

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.


Neutral losses db for sphingoid bases. It is employed by idCerneg function.

Description

In silico generated database for neutral losses of sphingoid bases in ESI-.

Usage

data("nlsphdb")

Format

Data frame with 4 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.


Prepare output for LipidMS annotation functions

Description

Prepare a readable output for LipidMS identification functions.

Usage

organizeResults(
  candidates,
  coelfrags,
  clfrags,
  classConf,
  chainsComb,
  intrules,
  intConf,
  nchains,
  class,
  acquisitionmode
)

Arguments

candidates

candidates data frame. Output of findCandidates.

coelfrags

list of coeluting fragments for each candidate

clfrags

vector containing the expected fragments for a given lipid class.

classConf

output of checkClass

chainsComb

output of combineChains

intrules

character vector specifying the fragments to compare. See checkIntensityRules.

intConf

output of checkIntensityRules

nchains

number of chains of the targeted lipid class.

class

character value. Lipid class (i.e. PC, PE, DG, TG, etc.).

acquisitionmode

acquisition mode (DIA or DDA).

Details

Coelution score for DIA data is calculated as the mean coelution score of all fragments used for annotation, while for DDA data, the intensity score is given, which is calculated as the sum of the relative intensities of the fragments used for annotation.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


PAs database

Description

In silico generated database for common PAs.

Usage

data("padb")

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.


PCs database

Description

In silico generated database for common PCs.

Usage

data("pcdb")

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.


O-PC database

Description

In silico generated database for common O-PC.

Usage

data("pcodb")

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.


P-PC database

Description

In silico generated database for common P-PC.

Usage

data("pcpdb")

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.


PEs database

Description

In silico generated database for common PEs.

Usage

data("pedb")

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.


O-PE database

Description

In silico generated database for common O-PE.

Usage

data("peodb")

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.


P-PE database

Description

In silico generated database for common P-PE.

Usage

data("pepdb")

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.


PGs database

Description

In silico generated database for common PGs.

Usage

data("pgdb")

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.


PIs database

Description

In silico generated database for common PIs.

Usage

data("pidb")

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.


EIC for all samples in a msbatch

Description

EIC for all samples in a msbatch

Usage

ploteicmsbatch(msbatch, mz, ppm, rt, colorbygroup = TRUE, verbose = TRUE)

Arguments

msbatch

msbatch

mz

mz of interest

ppm

mass tolerance in ppm

rt

numeric vector with the RT range to be plotted

colorbygroup

logical. If TRUE, samples will be coloured based on their sample group (from metadata).

verbose

print information messages.

Value

plot

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


Plot informative peaks for lipid annotation

Description

Plot informative peaks for each lipid annotated with idPOS and idNEG (or similar functions).

Usage

plotLipids(msobject, span = 0.4, ppm = 10, verbose = TRUE)

Arguments

msobject

annotated msobject.

span

smoothing parameter. Numeric value between 0 and 1.

ppm

mz tolerance for EIC. If set to 0, the EIC will not be shown.

verbose

print information messages.

Details

Peak intensities are relative to the maximum intensity of each peak to ease visualization.

Grey lines show the the extracted ion chromatograms for the peaks.

Value

msobject with a plots element which contains a list of plots. Plots on the left side represent raw values while plots on the left are smoothed or clean scans (MS2 in DDA).

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


TIC for all samples in a msbatch

Description

TIC for all samples in a msbatch

Usage

plotticmsbatch(msbatch, rt, colorbygroup = TRUE)

Arguments

msbatch

msbatch

rt

numeric vector with the RT range to be plotted

colorbygroup

logical. If TRUE, samples will be coloured based on their sample group (from metadata).

Value

plot

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


PSs database

Description

In silico generated database for common PSs.

Usage

data("psdb")

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.


Plot retention time deviation

Description

Plot retention time deviation of an aligned msbatch

Usage

rtdevplot(msbatch, colorbygroup = TRUE)

Arguments

msbatch

aligned msbatch.

colorbygroup

logical. If TRUE, samples will be coloured based on their sample group (from metadata).

Value

plot

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


Targeted isotopes search

Description

This function uses annotation results of deisotoped data to search for isotopes in raw data.

Usage

searchIsotopes(
  msobject,
  label,
  adductsTable = LipidMS::adductsTable,
  ppm = 10,
  coelCutoff = 0.7,
  results,
  dbs
)

Arguments

msobject

msobject.

label

isotope employed for the experiment. It can be "13C" or "D".

adductsTable

adducts table employed for lipids annotation.

ppm

mass error tolerance.

coelCutoff

coelution score threshold between isotopes. By default, 0.7.

results

target list to search isotopes. If missing, all results from the msobject are searched. It is used by searchIsotopesmsbatch.

dbs

list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB.

Value

List with the isotopes for each compound in the results data frame.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>


Targeted isotopes search for msbatch

Description

This function uses annotation results of deisotoped data to search for isotopes in raw data.

Usage

searchIsotopesmsbatch(
  msbatch,
  label,
  adductsTable = LipidMS::adductsTable,
  ppm = 10,
  coelCutoff = 0.7
)

Arguments

msbatch

annotated msbatch.

label

isotope employed for the experiment. It can be "13C" or "D".

adductsTable

adducts table employed for lipids annotation.

ppm

mass error tolerance.

coelCutoff

coelution score threshold between isotopes. By default, 0.7.

Value

List with the isotopes for each compound in the results data frame.

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

Examples

## Not run: 
msbatch <- batchProcessing(metadata = "metadata.csv", polarity = "positive")
msbatch <- alignmsbatch(msbatch)
msbatch <- groupmsbatch(msbatch)
msbatch <- annotatemsbatch(msbatch)
searchIsotopesmsbatch(msbatch, label = "13C")

## End(Not run)

Create msbatch for batch processing.

Description

Create msbatch from a list of msobjects to build an msbatch.

Usage

setmsbatch(msobjectlist, metadata)

Arguments

msobjectlist

list of msobjects.

metadata

sample metadata. Optional. It can be a csv file or a data.frame with 3 columns (sample, acquistionmode and sampletype).

Details

samples are sorted following the metadata data.frame.

Value

msbatch

Author(s)

M Isabel Alcoriza-Balaguer <[email protected]>

See Also

dataProcessing and batchdataProcessing

Examples

## Not run: 
msbatch <- setmsbatch(msobjectlist)

## End(Not run)

SMs database

Description

In silico generated database for common SMs.

Usage

data("smdb")

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.


Sphingoid bases database

Description

In silico generated database for common sphingoid bases.

Usage

data("sphdb")

Format

Data frame with 4 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.


Sphingoid bases phosphate database

Description

In silico generated database for common sphingoid bases phosphate.

Usage

data("sphPdb")

Format

Data frame with 4 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.


TGs database

Description

In silico generated database for common TGs.

Usage

data("tgdb")

Format

Data frame with 376 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.