MG identification based on fragmentation patterns for LC-MS/MS AIF data acquired in positive mode.
idMGpos(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-H2O", "M+NH4", "M+Na"), clfrags = c(),
clrequired = c(), ftype = c(), dbs = list(mgdb = LipidMS::mgdb,
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 MG 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.
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 MG annotations (results) and some additional information (class fragments and chain fragments).
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, 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 (in this case, just MS-only or Subclass level (if any class fragment is defined) are possible).
# NOT RUN {
idMGpos(MS1 = LipidMS::mix_pos_fullMS, MSMS1 = LipidMS::mix_pos_Ce20,
MSMS2 = LipidMS::mix_pos_Ce40)
idMGpos(MS1 = LipidMS::serum_pos_fullMS, MSMS1 = LipidMS::serum_pos_Ce20,
MSMS2 = LipidMS::serum_pos_Ce40)
# }
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