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 |
In silico generated database for common acylceramides.
data("acylcerdb")
data("acylcerdb")
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.
Table of possible adducts to be employed by LipidMS and related information.
data("adductsTable")
data("adductsTable")
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 to correct time drifts during acquisition queues.
alignmsbatch( msbatch, dmz = 5, drt = 30, minsamples, minsamplesfrac = 0.75, span = 0.4, parallel = FALSE, ncores, verbose = TRUE )
alignmsbatch( msbatch, dmz = 5, drt = 30, minsamples, minsamplesfrac = 0.75, span = 0.4, parallel = FALSE, ncores, verbose = TRUE )
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. |
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.
aligned msbatch
M Isabel Alcoriza-Balaguer <[email protected]>
Partitioning algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
## Not run: msbatch <- alignmsbatch(msbatch) ## End(Not run)
## Not run: msbatch <- alignmsbatch(msbatch) ## End(Not run)
Summarize annotation results of an msbatch into the feature table
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 )
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 )
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. |
msbatch
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msbatch <- annotatemsbatch(msbatch) msbatch$features ## End(Not run)
## Not run: msbatch <- annotatemsbatch(msbatch) msbatch$features ## End(Not run)
load all LipidMS default data bases required to run identification functions.
assignDB()
assignDB()
list of data frames
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: dbs <- assignDB() ## End(Not run)
## Not run: dbs <- assignDB() ## End(Not run)
Common bile acids conjugates. It can be modified to look for other BA species.
data("baconjdb")
data("baconjdb")
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.
In silico generated database for common bile acids.
data("badb")
data("badb")
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.
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 )
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 )
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. |
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.
msbatch
M Isabel Alcoriza-Balaguer <[email protected]>
Peak-picking algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
## Not run: # if metadata is a data frame: msbatch <- batchdataProcessing(metadata$sample, metadata, polarity = "positive", dmzagglom = 25, drtagglom = 500, drtclust = 60, minpeak = c(5, 3), drtgap = 5, drtminpeak = 20, drtmaxpeak = 100, recurs = 5, sb = c(3, 2), sn = 2, minint = c(1000, 100), weight = 2, dmzIso = 10, drtIso = 5) # if metadata is a csv file: msbatch <- batchdataProcessing(metadata = "metadata.csv", polarity = "positive", dmzagglom = 25, drtagglom = 500, drtclust = 60, minpeak = c(5, 3), drtgap = 5, drtminpeak = 20, drtmaxpeak = 100, recurs = 5, sb = c(3, 2), sn = 2, minint = c(1000, 100), weight = 2, dmzIso = 10, drtIso = 5) ## End(Not run)
## Not run: # if metadata is a data frame: msbatch <- batchdataProcessing(metadata$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)
In silico generated database for common carnitines.
data("carnitinesdb")
data("carnitinesdb")
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.
In silico generated database for common CEs.
data("CEdb")
data("CEdb")
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.
In silico generated database for common ceramides.
data("cerdb")
data("cerdb")
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.
In silico generated database for common ceramides phosphate.
data("cerPdb")
data("cerPdb")
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 specific fragments that inform about the chains structure.
chainFrags(coelfrags, chainfrags, ppm = 10, candidates, f = NULL, dbs)
chainFrags(coelfrags, chainfrags, ppm = 10, candidates, f = NULL, dbs)
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. |
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.
List of data frames with the chain fragments found.
M Isabel Alcoriza-Balaguer <[email protected]>
Search of characteristic fragments that confirm a given lipid class.
checkClass(candidates, coelfrags, clfrags, ftype, clrequisites, ppm = 10, dbs)
checkClass(candidates, coelfrags, clfrags, ftype, clrequisites, ppm = 10, dbs)
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" |
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").
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.
M Isabel Alcoriza-Balaguer <[email protected]>
Check intensity rules to confirm chains position.
checkIntensityRules(intrules, rates, intrequired, nchains, combinations)
checkIntensityRules(intrules, rates, intrequired, nchains, combinations)
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. |
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.
List of logical vectors with the confirmation for each combination.
M Isabel Alcoriza-Balaguer <[email protected]>
In silico generated database for commo CLs.
data("cldb")
data("cldb")
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.
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.
coelutingFrags( precursors, products, rttol, rawData = data.frame(), coelCutoff = 0 )
coelutingFrags( precursors, products, rttol, rawData = data.frame(), coelCutoff = 0 )
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. |
List of data frames with the coeluting fragments for each candidate.
M Isabel Alcoriza-Balaguer <[email protected]>
Calculate coelution score between two peaks.
coelutionScore(peak1, peak2, rawData)
coelutionScore(peak1, peak2, rawData)
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 |
M Isabel Alcoriza-Balaguer <[email protected]>
It calculates combinations of chain fragments that sum up the same number of carbons and double bounds as the precursor.
combineChains(candidates, nchains, sn1, sn2, sn3, sn4)
combineChains(candidates, nchains, sn1, sn2, sn3, sn4)
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. |
List of data frames with candidate chains structures.
M Isabel Alcoriza-Balaguer <[email protected]>
Confidence annotation levels and their hierarchy.
data("confLevels")
data("confLevels")
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.
It allows to create easy-customizable lipid DBs for annotation with LipidMS package.
createLipidDB(lipid, chains, chains2)
createLipidDB(lipid, chains, chains2)
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. |
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".
List with the requested dbs (data frames)
M Isabel Alcoriza-Balaguer <[email protected]>
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)
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.
crossTables(msobject, ppm = 5, rttol = 10, dbs)
crossTables(msobject, ppm = 5, rttol = 10, dbs)
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. |
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.
M Isabel Alcoriza-Balaguer <[email protected]>
Process mzXML files individually: peakpicking and isotope anotation
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 )
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 )
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. |
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.
an msobject that contains metadata of the mzXML file, raw data and extracted peaks.
M Isabel Alcoriza-Balaguer <[email protected]>
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.
batchdataProcessing and setmsbatch
## 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)
## 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)
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.
ddaFrags(candidates, precursors, rawData, ppm)
ddaFrags(candidates, precursors, rawData, ppm)
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. |
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.
List of data frames with the fragments for each candidate.
M Isabel Alcoriza-Balaguer <[email protected]>
In silico generated database for common DGs.
data("dgdb")
data("dgdb")
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.
In silico generated database for common FAs.
data("fadb")
data("fadb")
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.
In silico generated database for common FAHFAs.
data("fahfadb")
data("fahfadb")
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.
Use grouping results to target all peaks from the msbatch in each sample and refill intensities at the features table.
fillpeaksmsbatch(msbatch)
fillpeaksmsbatch(msbatch)
msbatch |
msbatch obtained from the groupmsbatch function. |
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).
msbatch
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msbatch <- fillpeaksmsbatch(msbatch) ## End(Not run)
## Not run: msbatch <- fillpeaksmsbatch(msbatch) ## End(Not run)
Search of lipid candidates from a peaklist based on a set of expected adducts.
findCandidates( MS1, db, ppm, rt, adducts, rttol = 3, dbs, rawData = data.frame(), coelCutoff = 0 )
findCandidates( MS1, db, ppm, rt, adducts, rttol = 3, dbs, rawData = data.frame(), coelCutoff = 0 )
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. |
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
Obtain an inclusion list for the identified lipids.
getInclusionList(df, dbs)
getInclusionList(df, dbs)
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. |
Data frame with 6 columns: formula, RT, neutral mass, m/z, adduct and the LipidMSid.
M Isabel Alcoriza-Balaguer <[email protected]>
Group features from an msbatch
groupmsbatch( msbatch, dmz = 5, drtagglom = 30, drt = 15, minsamples, minsamplesfrac = 0.25, parallel = FALSE, ncores, deleteduplicates = TRUE, thr_overlap_duplicates = 0.7, verbose = TRUE )
groupmsbatch( msbatch, dmz = 5, drtagglom = 30, drt = 15, minsamples, minsamplesfrac = 0.25, parallel = FALSE, ncores, deleteduplicates = TRUE, thr_overlap_duplicates = 0.7, verbose = TRUE )
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. |
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.
grouped msbatch
M Isabel Alcoriza-Balaguer <[email protected]>
Partitioning algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
## Not run: msbatch <- groupmsbatch(msbatch) ## End(Not run)
## Not run: msbatch <- groupmsbatch(msbatch) ## End(Not run)
In silico generated database for common HFAs.
data("hfadb")
data("hfadb")
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.
AcylCer identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idAcylCerneg(msobject) ## End(Not run)
## Not run: msobject <- idAcylCerneg(msobject) ## End(Not run)
AcylCer identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idCerPneg(msobject) ## End(Not run)
## Not run: msobject <- idCerPneg(msobject) ## End(Not run)
BA identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idBAneg(msobject) ## End(Not run)
## Not run: msobject <- idBAneg(msobject) ## End(Not run)
Acylcarnitines identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idCarpos(msobject) ## End(Not run)
## Not run: msobject <- idCarpos(msobject) ## End(Not run)
CE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idCEpos(msobject) ## End(Not run)
## Not run: msobject <- idCEpos(msobject) ## End(Not run)
Cer identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idCerneg(msobject) ## End(Not run)
## Not run: msobject <- idCerneg(msobject) ## End(Not run)
CerP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idCerPneg(msobject) ## End(Not run)
## Not run: msobject <- idCerPneg(msobject) ## End(Not run)
Ceramides identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idCerpos(msobject) ## End(Not run)
## Not run: msobject <- idCerpos(msobject) ## End(Not run)
CerP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idCerPpos(msobject) ## End(Not run)
## Not run: msobject <- idCerPpos(msobject) ## End(Not run)
CL identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idCLneg(msobject) ## End(Not run)
## Not run: msobject <- idCLneg(msobject) ## End(Not run)
DG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idDGpos(msobject) ## End(Not run)
## Not run: msobject <- idDGpos(msobject) ## End(Not run)
FAHFA identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idFAHFAneg(msobject) ## End(Not run)
## Not run: msobject <- idFAHFAneg(msobject) ## End(Not run)
FA identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idFAneg(msobject) ## End(Not run)
## Not run: msobject <- idFAneg(msobject) ## End(Not run)
LPC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idLPCneg(msobject) ## End(Not run)
## Not run: msobject <- idLPCneg(msobject) ## End(Not run)
LPC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idLPCpos(msobject) ## End(Not run)
## Not run: msobject <- idLPCpos(msobject) ## End(Not run)
LPE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idLPEneg(msobject) ## End(Not run)
## Not run: msobject <- idLPEneg(msobject) ## End(Not run)
LPE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idLPEpos(msobject) ## End(Not run)
## Not run: msobject <- idLPEpos(msobject) ## End(Not run)
LPG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idLPGneg(msobject) ## End(Not run)
## Not run: msobject <- idLPGneg(msobject) ## End(Not run)
LPI identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idLPIneg(msobject) ## End(Not run)
## Not run: msobject <- idLPIneg(msobject) ## End(Not run)
LPS identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idLPSneg(msobject) ## End(Not run)
## Not run: msobject <- idLPSneg(msobject) ## End(Not run)
MG identification based on fragmentation patterns for LC-MS/MS DIA and DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idMGpos(msobject) ## End(Not run)
## Not run: msobject <- idMGpos(msobject) ## End(Not run)
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.
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 )
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 )
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. |
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idNEG(msobject) ## End(Not run)
## Not run: msobject <- idNEG(msobject) ## End(Not run)
PC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPCneg(msobject) ## End(Not run)
## Not run: msobject <- idPCneg(msobject) ## End(Not run)
PCo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPCneg(msobject) ## End(Not run)
## Not run: msobject <- idPCneg(msobject) ## End(Not run)
PCo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPCopos(msobject) ## End(Not run)
## Not run: msobject <- idPCopos(msobject) ## End(Not run)
PCp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPCpneg(msobject) ## End(Not run)
## Not run: msobject <- idPCpneg(msobject) ## End(Not run)
PC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPCpos(msobject) ## End(Not run)
## Not run: msobject <- idPCpos(msobject) ## End(Not run)
PCp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPCppos(msobject) ## End(Not run)
## Not run: msobject <- idPCppos(msobject) ## End(Not run)
PE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPEneg(msobject) ## End(Not run)
## Not run: msobject <- idPEneg(msobject) ## End(Not run)
PEo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPEoneg(msobject) ## End(Not run)
## Not run: msobject <- idPEoneg(msobject) ## End(Not run)
PEo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPEopos(msobject) ## End(Not run)
## Not run: msobject <- idPEopos(msobject) ## End(Not run)
PEp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPEoneg(msobject) ## End(Not run)
## Not run: msobject <- idPEoneg(msobject) ## End(Not run)
PE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPEpos(msobject) ## End(Not run)
## Not run: msobject <- idPEpos(msobject) ## End(Not run)
PEp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPEppos(msobject) ## End(Not run)
## Not run: msobject <- idPEppos(msobject) ## End(Not run)
PG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPGneg(msobject) ## End(Not run)
## Not run: msobject <- idPGneg(msobject) ## End(Not run)
PG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPGpos(msobject) ## End(Not run)
## Not run: msobject <- idPGpos(msobject) ## End(Not run)
PI identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPIneg(msobject) ## End(Not run)
## Not run: msobject <- idPIneg(msobject) ## End(Not run)
PI identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPIpos(msobject) ## End(Not run)
## Not run: msobject <- idPIpos(msobject) ## End(Not run)
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.
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 )
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 )
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. |
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPOS(msobject) ## End(Not run)
## Not run: msobject <- idPOS(msobject) ## End(Not run)
PS identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idPSneg(msobject) ## End(Not run)
## Not run: msobject <- idPSneg(msobject) ## End(Not run)
SM identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idSMneg(msobject) ## End(Not run)
## Not run: msobject <- idSMneg(msobject) ## End(Not run)
SM identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idSMpos(msobject) ## End(Not run)
## Not run: msobject <- idSMpos(msobject) ## End(Not run)
Sph identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idSphneg(msobject) ## End(Not run)
## Not run: msobject <- idSphneg(msobject) ## End(Not run)
SphP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idSphPneg(msobject) ## End(Not run)
## Not run: msobject <- idSphPneg(msobject) ## End(Not run)
Sph identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idSphpos(msobject) ## End(Not run)
## Not run: msobject <- idSphpos(msobject) ## End(Not run)
SphP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idSphPpos(msobject) ## End(Not run)
## Not run: msobject <- idSphPpos(msobject) ## End(Not run)
TG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
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 )
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 )
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. |
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).
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).
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.
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: msobject <- idTGpos(msobject) ## End(Not run)
## Not run: msobject <- idTGpos(msobject) ## End(Not run)
Interactive UI for LipidMS
LipidMSapp()
LipidMSapp()
M Isabel Alcoriza-Balaguer <[email protected]>
## Not run: # example data files can be download from github.com/maialba3/LipidMSv2.0_exampleFiles library(LipidMS) LipidMSapp() ## End(Not run)
## Not run: # example data files can be download from github.com/maialba3/LipidMSv2.0_exampleFiles library(LipidMS) LipidMSapp() ## End(Not run)
In silico generated database for common LPAs.
data("lysopadb")
data("lysopadb")
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.
In silico generated database for common O-LPA.
data("lysopaodb")
data("lysopaodb")
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.
In silico generated database for common LPCs.
data("lysopcdb")
data("lysopcdb")
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.
In silico generated database for common O-LPC.
data("lysopcodb")
data("lysopcodb")
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.
In silico generated database for common P-LPC.
data("lysopcpdb")
data("lysopcpdb")
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.
In silico generated database for common LPEs.
data("lysopedb")
data("lysopedb")
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.
In silico generated database for common O-LPE.
data("lysopeodb")
data("lysopeodb")
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.
In silico generated database for common P-LPE.
data("lysopepdb")
data("lysopepdb")
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.
In silico generated database for common LPGs.
data("lysopgdb")
data("lysopgdb")
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.
In silico generated database for common LPIs.
data("lysopidb")
data("lysopidb")
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.
In silico generated database for common LPSs
data("lysopsdb")
data("lysopsdb")
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.
In silico generated database for common MGs.
data("mgdb")
data("mgdb")
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.
In silico generated database for neutral losses of sphingoid bases in ESI-.
data("nlsphdb")
data("nlsphdb")
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 a readable output for LipidMS identification functions.
organizeResults( candidates, coelfrags, clfrags, classConf, chainsComb, intrules, intConf, nchains, class, acquisitionmode )
organizeResults( candidates, coelfrags, clfrags, classConf, chainsComb, intrules, intConf, nchains, class, acquisitionmode )
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). |
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.
M Isabel Alcoriza-Balaguer <[email protected]>
In silico generated database for common PAs.
data("padb")
data("padb")
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.
In silico generated database for common PCs.
data("pcdb")
data("pcdb")
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.
In silico generated database for common O-PC.
data("pcodb")
data("pcodb")
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.
In silico generated database for common P-PC.
data("pcpdb")
data("pcpdb")
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.
In silico generated database for common PEs.
data("pedb")
data("pedb")
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.
In silico generated database for common O-PE.
data("peodb")
data("peodb")
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.
In silico generated database for common P-PE.
data("pepdb")
data("pepdb")
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.
In silico generated database for common PGs.
data("pgdb")
data("pgdb")
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.
In silico generated database for common PIs.
data("pidb")
data("pidb")
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
ploteicmsbatch(msbatch, mz, ppm, rt, colorbygroup = TRUE, verbose = TRUE)
ploteicmsbatch(msbatch, mz, ppm, rt, colorbygroup = TRUE, verbose = TRUE)
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. |
plot
M Isabel Alcoriza-Balaguer <[email protected]>
Plot informative peaks for each lipid annotated with idPOS and idNEG (or similar functions).
plotLipids(msobject, span = 0.4, ppm = 10, verbose = TRUE)
plotLipids(msobject, span = 0.4, ppm = 10, verbose = TRUE)
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. |
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.
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).
M Isabel Alcoriza-Balaguer <[email protected]>
TIC for all samples in a msbatch
plotticmsbatch(msbatch, rt, colorbygroup = TRUE)
plotticmsbatch(msbatch, rt, colorbygroup = TRUE)
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). |
plot
M Isabel Alcoriza-Balaguer <[email protected]>
In silico generated database for common PSs.
data("psdb")
data("psdb")
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 of an aligned msbatch
rtdevplot(msbatch, colorbygroup = TRUE)
rtdevplot(msbatch, colorbygroup = TRUE)
msbatch |
aligned msbatch. |
colorbygroup |
logical. If TRUE, samples will be coloured based on their sample group (from metadata). |
plot
M Isabel Alcoriza-Balaguer <[email protected]>
This function uses annotation results of deisotoped data to search for isotopes in raw data.
searchIsotopes( msobject, label, adductsTable = LipidMS::adductsTable, ppm = 10, coelCutoff = 0.7, results, dbs )
searchIsotopes( msobject, label, adductsTable = LipidMS::adductsTable, ppm = 10, coelCutoff = 0.7, results, dbs )
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. |
List with the isotopes for each compound in the results data frame.
M Isabel Alcoriza-Balaguer <[email protected]>
This function uses annotation results of deisotoped data to search for isotopes in raw data.
searchIsotopesmsbatch( msbatch, label, adductsTable = LipidMS::adductsTable, ppm = 10, coelCutoff = 0.7 )
searchIsotopesmsbatch( msbatch, label, adductsTable = LipidMS::adductsTable, ppm = 10, coelCutoff = 0.7 )
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. |
List with the isotopes for each compound in the results data frame.
M Isabel Alcoriza-Balaguer <[email protected]>
## 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)
## 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 from a list of msobjects to build an msbatch.
setmsbatch(msobjectlist, metadata)
setmsbatch(msobjectlist, metadata)
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). |
samples are sorted following the metadata data.frame.
msbatch
M Isabel Alcoriza-Balaguer <[email protected]>
dataProcessing and batchdataProcessing
## Not run: msbatch <- setmsbatch(msobjectlist) ## End(Not run)
## Not run: msbatch <- setmsbatch(msobjectlist) ## End(Not run)
In silico generated database for common SMs.
data("smdb")
data("smdb")
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.
In silico generated database for common sphingoid bases.
data("sphdb")
data("sphdb")
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.
In silico generated database for common sphingoid bases phosphate.
data("sphPdb")
data("sphPdb")
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.
In silico generated database for common TGs.
data("tgdb")
data("tgdb")
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.