Cer identification based on fragmentation patterns for LC-MS/MS AIF data acquired in negative mode.
idCerneg(MS1, MSMS1, MSMS2, 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"),
intrules = c(), rates = c(), intrequired = c(), coelCutoff = 0.8,
dbs)list with two data frames cointaining all peaks from the full MS function ("peaklist" data frame) and the raw MS scans data ("rawScans" data frame). They must have four columns: m.z, RT (in seconds), int (intensity) and peakID (link between both data frames). "rawScans" data frame also needs a extra column named "Scan", which indicates the scan order number. Output of dataProcessing function. In case no coelution score needs to be applied, this argument can be just the peaklist data frame.
list with two data frames cointaining all peaks from the high energy function ("peaklist" data frame) and the raw MS scans data ("rawScans" data frame). They must have four columns: m.z, RT (in seconds), int (intensity) and peakID (link between both data frames). "rawScans" data frame also needs a extra column named "Scan", which indicates the scan order number. Output of dataProcessing function. In case no coelution score needs to be applied, this argument can be just the peaklist data frame.
list with two data frames cointaining all peaks from a second high energy function ("peaklist" data frame) and the raw MS scans data ("rawScans" data frame). They must have four columns: m.z, RT (in seconds), int (intensity) and peakID (link between both data frames). "rawScans" data frame also needs a extra column named "Scan", which indicates the scan order number. Output of dataProcessing function. In case no coelution score needs to be applied, this argument can be just the peaklist data frame. 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 Cer 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 ratesbetween 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.
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8.
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 Cer annotations (results) and some additional information (class fragments and chain fragments).
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 intead of an O, what means a mass difference
of 1.9918 from the exact mass of the FA). 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, 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) and PFCS (parent-fragment coelution score mean of all fragments used for the identification).
# NOT RUN {
idCerneg(MS1 = LipidMS::MS1_neg, MSMS1 = LipidMS:MSMS1_neg,
MSMS2 = LipidMS::MSMS2_neg)
# }
# NOT RUN {
# }
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