doFindmain | R Documentation |
Cluster annotation function: inference of 'M' - molecular weight of the compound giving rise to each spectrum - using the InterpretMSSpectrum::findMain function
doFindmain(
ramclustObj = NULL,
cmpd = NULL,
mode = "positive",
mzabs.error = 0.005,
ppm.error = 10,
ads = NULL,
nls = NULL,
scoring = "auto",
plot.findmain = TRUE,
writeMat = TRUE,
writeMS = TRUE,
use.z = TRUE
)
ramclustObj |
ramclustR object to annotate. |
cmpd |
integer: vector defining compound numbers to annotated. if NULL (default), all compounds |
mode |
character: "positive" or "negative" |
mzabs.error |
numeric: absolute mass deviation allowd, default = 0.01 |
ppm.error |
numeric: ppm mass error _added_ to mzabs.error, default = 10 |
ads |
character: vector of allowed adducts, i.e. c("[M+H]+"). if NULL, default positive mode values of H+, Na+, K+, and NH4+, as monomer, dimer, and trimer, are assigned. Negative mode include "[M-H]-", "[M+Na-2H]-", "[M+K-2H]-", "[M+CH2O2-H]-" as monomer, dimer, and trimer. |
nls |
character: vector of allowed neutral losses, i.e. c("[M+H-H2O]+"). if NULL, an extensive list derived from CAMERA's will be used. |
scoring |
character: one of 'imss' , 'ramclustr', or 'auto'. default = 'auto'. see details. |
plot.findmain |
logical: should pdf polts be generated for evaluation? detfault = TRUE. PDF saved to working.directory/spectra |
writeMat |
logical: should individual .mat files (for MSFinder) be generated in a 'mat' subdirectory in the 'spectra' folder? default = TRUE. |
writeMS |
logical: should individual .ms files (for Sirius) be generated in a 'ms' subdirectory in the 'spectra' folder? default = TRUE. Note that no import functions are yet written for Sirius output. |
use.z |
logical: if you have previously run the 'assign.z' function from ramclustR, there will be a slot reflecting the feature mass after accounting for charge (fm) - if TRUE this is used instead of feature m/z (fmz) in interpreting MS data and exporting spectra for annotation. |
a partially annotated ramclustR object. base structure is that of a standard R heirarchical clustering output, with additional slots described in ramclustR documentation (?ramclustR). New slots added after using the interpretMSSpectrum functionality include those described below.
$M: The inferred molecular weight of the compound giving rise to the each spectrum
$M.ppm: The ppm error of all the MS signals annotated, high error values should be considered 'red flags'.
$M.ann: The annotated spectrum supporting the interpretation of M
$use.findmain: Logical vector indicating whether findmain scoring (TRUE) or ramclustR scoring (FALSE) was used to support inference of M. By default, findmain scoring is used. When ramclustR scoring differs from findmain scoring, the scoring metric which predicts higher M is selected.
$M.ramclustr: M selected using ramclustR scoring
$M.ppm.ramclustr: ppm error of M selected using ramclustR scoring. Used to resolve concflicts between ramclustR and findmain M assignment when scoring = auto.
$M.ann.ramclustr: annotated spectrum supporting M using ramclustR scoring
$M.nann.ramclustr: number of masses annotated using ramclustR scoring. Used to resolve concflicts between ramclustR and findmain M assignment when scoring = auto.
$M.space.ramclustr: the 'space' of scores between the best and second best ramclustR scores. Calculated as a ratio. Used to resolve concflicts between ramclustR and findmain M assignment when scoring = auto.
$M.findmain: M selected using findmain scoring
$M.ppm.findmain: ppm error of M selected using findmain scoring. Used to resolve concflicts between ramclustR and findmain M assignment when scoring = auto.
$M.ann.findmain: annotated spectrum supporting M using findmain scoring
$M.nann.findmain: number of masses annotated using findmain scoring. Used to resolve concflicts between ramclustR and findmain M assignment when scoring = auto.
$M.space.findmain: the 'space' of scores between the best and second best findmain scores. Calculated as a ratio. Used to resolve concflicts between ramclustR and findmain M assignment when scoring = auto.
Corey Broeckling
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Broeckling CD, Ganna A, Layer M, Brown K, Sutton B, Ingelsson E, Peers G, Prenni JE. Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction. Anal Chem. 2016 Sep 20;88(18):9226-34. doi: 10.1021/acs.analchem.6b02479. Epub 2016 Sep 8. PubMed PMID: 7560453.
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