PI identification based on fragmentation patterns for LC-MS/MS AIF data acquired in negative mode.
idPIneg(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(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("fa_M-H"), intrules = c("lysopi_sn1/lysopi_sn1",
"lysopa_sn1/lysopa_sn1"), rates = c("3/1", "3/1"), intrequired = c(F, F),
dbs = list(pidb = LipidMS::pidb, lysopidb = LipidMS::lysopidb, lysopadb =
LipidMS::lysopadb, 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 PI 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 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.
List with PI annotations (results) and some additional information (class fragments and chain fragments).
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 (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, 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 {
idPIneg(MS1 = LipidMS::mix_neg_fullMS, MSMS1 = LipidMS::mix_neg_Ce20,
MSMS2 = LipidMS::mix_neg_Ce40)
idPIneg(MS1 = LipidMS::serum_neg_fullMS, MSMS1 = LipidMS::serum_neg_Ce20,
MSMS2 = LipidMS::serum_neg_Ce40)
# }
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