PG identification based on fragmentation patterns for LC-MS/MS AIF data acquired in negative mode.
idPGneg(MS1, MSMS1, MSMS2 = data.frame(), ppm_precursor = 5,
ppm_products = 10, rttol = 3, rt = c(min(MS1$RT), max(MS1$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("fa_M-H"),
intrules = c("lysopg_sn1/lysopg_sn1"), rates = c("2/1"),
intrequired = c(T), dbs = list(pgdb = LipidMS::pgdb, lysopgdb =
LipidMS::lysopgdb, fadb = LipidMS::fadb, adductsTable =
LipidMS::adductsTable))data frame cointaining all peaks from the full MS function. It must have three columns: m.z, RT (in seconds) and int (intensity).
data frame cointaining all peaks from the low energy function. It must have three columns: m.z, RT and int.
data frame cointaining all peaks from the high energy function if it is the case. It must have three columns: m.z, RT and int. Optional.
mass tolerance for precursor ions. By default, 5 ppm.
mass tolerance for product ions. By default, 10 ppm.
total rt window for coelution between precursor and product ions. By default, 3 seconds.
rt range where the function will look for candidates. By default, it will search within all RT range in MS1.
expected adducts for PG in ESI-. Adducts allowed can be modified in adductsTable (dbs argument).
vector containing the expected fragments for a given lipid class. See checkClass for details.
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.
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.
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details.
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.
character vector specifying the fragments to compare. See checkIntensityRules.
character vector with the expected rates between fragments given as a string (i.e. "3/1"). See checkIntensityRules.
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure.
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 changed. If data bases have been customized using createLipidDB, they also have to be modified here.
List with PG annotations (results) and some additional information (class fragments and chain fragments).
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 (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, class of lipid, 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 (m.z error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level).
# NOT RUN {
idPGneg(MS1 = LipidMS::mix_neg_fullMS, MSMS1 = LipidMS::mix_neg_Ce20,
MSMS2 = LipidMS::mix_neg_Ce40)
idPGneg(MS1 = LipidMS::serum_neg_fullMS, MSMS1 = LipidMS::serum_neg_Ce20,
MSMS2 = LipidMS::serum_neg_Ce40)
# }
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