Package 'RMassBank'

Description

Shifts both 'parent' and 'children' spectra of the 'RmbSpectraSet' by the same mass.

Usage

## S4 method for signature 'RmbSpectraSet,ANY'
e1 - e2

Arguments

Description

Shifts all spectra in a 'RmbSpectrum2List' by the same mass

Usage

## S4 method for signature 'RmbSpectrum2List,ANY'
e1 - e2

Arguments

Description

Add a negative mass shift to a spectrum

Usage

## S4 method for signature 'Spectrum,numeric'
e1 - e2

Arguments

Description

Parses a title for a single MassBank record using the title format specified in the option titleFormat. Internally used, not exported.

Usage

.parseTitleString(mbdata)

Arguments

Details

If the option is not set, a standard title format is used (for record definition version 1 or 2).

Value

A string with the title.

Author(s)

Michael Stravs, Eawag

References

MassBank record format: http://www.massbank.jp/manuals/MassBankRecord_en.pdf

See Also

buildRecord

Examples

## Not run: 
		# used in buildRecord()
		title <- .parseTitleString(mbdata)

## End(Not run)

Description

TODO: consider whether to add functionality to move reanalysis stuff from legacy data back in.

Usage

.updateObject.RmbSpectrum2.formulaSource(w)

Arguments

Value

The updated RmbSpectrum2 object

Author(s)

stravsmi

Description

Shifts both 'parent' and 'children' spectra of the 'RmbSpectraSet' by the same mass.

Usage

## S4 method for signature 'RmbSpectraSet,ANY'
e1 + e2

Arguments

Description

Shifts all spectra in a 'RmbSpectrum2List' by the same mass

Usage

## S4 method for signature 'RmbSpectrum2List,ANY'
e1 + e2

Arguments

Description

Add a mass shift to a spectrum

Usage

## S4 method for signature 'Spectrum,numeric'
e1 + e2

Arguments

Description

Add, subtract, and multiply molecular formulas.

Usage

add.formula(f1, f2, as.formula = TRUE, as.list = FALSE)
multiply.formula(f1, n, as.formula = TRUE, as.list = FALSE)

Arguments

Details

Note that the results are not checked for plausibility at any stage, nor reordered.

Value

The resulting formula, as specified above.

Author(s)

Michael Stravs

See Also

formulastring.to.list, is.valid.formula, order.formula

Examples

##

add.formula("C6H12O6", "C3H3")
add.formula("C6H12O6", "C-3H-3")
add.formula("C6H12O6", multiply.formula("C3H3", -1))

Description

Adds the peaklist of a MassBank-Record to the specs of an msmsWorkspace

Usage

addMB(w, cpdID, fileName, mode)

Arguments

Value

The msmsWorkspace with the additional peaklist from the record

Author(s)

Erik Mueller

See Also

addPeaksManually

Examples

## Not run: 
		addMB("filepath_to_records/RC00001.txt")

## End(Not run)

Description

Loads a table with additional peaks to add to the MassBank spectra. Required columns are cpdID, scan, int, mzFound, OK.

Usage

addPeaks(mb, filename_or_dataframe)

Arguments

Details

All peaks with OK=1 will be included in the spectra.

Value

The mbWorkspace with loaded additional peaks.

Author(s)

Michael Stravs

See Also

mbWorkflow

Examples

## Not run: addPeaks("myrun_additionalPeaks.csv")

Description

Adds a manual peaklist in matrix-format

Usage

addPeaksManually(w, cpdID, handSpec, mode)

Arguments

Value

The msmsWorkspace with the additional peaklist added to the right spectrum

Author(s)

Erik Mueller

See Also

msmsWorkflow

Examples

## Not run: 
		handSpec <- cbind(mz=c(274.986685367956, 259.012401087427, 95.9493025990907, 96.9573002472772),
                               int=c(357,761, 2821, 3446))
		addPeaksManually(w, cpdID, handSpec)

## End(Not run)

Description

Adds a new column of a defined type to a data.frame and initializes it to a value. The advantage of doing this over adding it with $ or [,""] is that the case nrow(o) == 0 is adequately handled and doesn't raise an error.

Usage

addProperty(o, name, type, value = NA)

## S4 method for signature 'RmbSpectrum2,character,character'
addProperty(o, name, type, value = NA)

## S4 method for signature 'data.frame,character,character'
addProperty(o, name, type, value = NA)

Arguments

Value

Expanded data frame.

Methods (by class)

• addProperty(o = data.frame, name = character, type = character): Add a new column to a data.frame

Author(s)

stravsmi

Description

Groups an array of analyzed spectra and creates aggregated peak tables

Usage

aggregateSpectra(spec,  addIncomplete=FALSE)

Arguments

Details

addIncomplete is relevant for recalibration. For recalibration, we want to use only high-confidence peaks, therefore we set addIncomplete to FALSE. When we want to generate a peak list for actually generating MassBank records, we want to include all peaks into the peak tables.

Value

A summary data.frame with all peaks (possibly multiple rows for one m/z value from a spectrum, see below) with columns:

Further columns are later added by workflow steps 6 (electronic noise culler), 7 and 8.

Author(s)

Michael Stravs

See Also

msmsWorkflow, analyzeMsMs

Examples

## As used in the workflow:
## Not run: %
	w@spectra <- lapply(w@spectra, function(spec)
		analyzeMsMs(spec, mode="pH", detail=TRUE, run="recalibrated", cut=0, cut_ratio=0 ) )
	w@aggregate <- aggregateSpectra(w@spectra)

## End(Not run)

Description

Analyzes MSMS spectra of a compound by fitting formulas to each subpeak.

Usage

analyzeMsMs(
  msmsPeaks,
  mode = "pH",
  detail = FALSE,
  run = "preliminary",
  filterSettings = getOption("RMassBank")$filterSettings,
  spectraList = getOption("RMassBank")$spectraList,
  method = "formula"
)

analyzeMsMs.formula(
  msmsPeaks,
  mode = "pH",
  detail = FALSE,
  run = "preliminary",
  filterSettings = getOption("RMassBank")$filterSettings
)

analyzeMsMs.intensity(
  msmsPeaks,
  mode = "pH",
  detail = FALSE,
  run = "preliminary",
  filterSettings = getOption("RMassBank")$filterSettings
)

Arguments

Details

The analysis function uses Rcdk. Note that in this step, satellite peaks are removed by a simple heuristic rule (refer to the documentation of filterPeakSatellites for details.)

Value

The processed RmbSpectraSet object. Added (or filled, respectively, since the slots are present before) data include

Functions

• analyzeMsMs.formula(): Analyze the peaks using formula annotation

• analyzeMsMs.intensity(): Analyze the peaks going only by intensity values

Author(s)

Michael Stravs

See Also

msmsWorkflow, filterLowaccResults, filterPeakSatellites, reanalyzeFailpeaks

Examples

## Not run: analyzed <- analyzeMsMs(spec, "pH", TRUE)

Description

Generates the PK$ANNOTATION entry from the peaklist obtained. This function is overridable by using the "annotator" option in the settings file.

Usage

annotator.default(annotation, formulaTag)

Arguments

Value

The annotated peak table. Table colnames() will be used for the titles (preferrably don't use spaces in the column titles; however no format is strictly enforced by the MassBank data format.

Author(s)

Michele Stravs, Eawag <[email protected]>

Examples

## Not run: 
annotation <- annotator.default(annotation)

## End(Not run)

Description

Writes the results from different msmsWorkflow steps to a file.

Usage

archiveResults(w, fileName, settings = getOption("RMassBank"))

Arguments

Examples

# This doesn't really make a lot of sense,
		# it stores an empty workspace.
		RmbDefaultSettings()
		w <- newMsmsWorkspace()
		archiveResults(w, "narcotics.RData")

Description

Takes a spectra block for a compound, as returned from analyzeMsMs, and an aggregated cleaned peak table, together with a MassBank information block, as stored in the infolists and loaded via loadInfolist/readMbdata and processes them to a MassBank record

Usage

buildRecord(o, ..., cpd, mbdata, analyticalInfo, additionalPeaks)

## S4 method for signature 'RmbSpectraSet'
buildRecord(o, ..., cpd, mbdata, analyticalInfo, additionalPeaks)

## S4 method for signature 'RmbSpectrum2'
buildRecord(
  o,
  ...,
  cpd = NULL,
  mbdata = list(),
  analyticalInfo = list(),
  additionalPeaks = NULL
)

Arguments

Value

An object of the same type as was used for the input with new information added to it

Author(s)

Michael Stravs

References

MassBank record format: http://www.massbank.jp/manuals/MassBankRecord_en.pdf

See Also

mbWorkflow, addPeaks, toMassbank

Description

This is a wrapper for webchem::cir_query, using the CACTUS API at https://cactus.nci.nih.gov/chemical/structure_documentation for the conversion. Before converting the CAS number, the name is checked whether it contains the word 'derivative'. If so, the conversion is stopped and NA is returned. Also, a warning will be printed in this case.

Usage

CAS2SMILES(CAS_number, name)

Arguments

Details

The API allows only one query per second. This is a hard- coded feature

Value

The SMILES code of the compound as character-string

Author(s)

pstahlhofen

Examples

SMILES_ethanol <- CAS2SMILES("64-17-5", "Ethanol")

Description

Checks for isotopes in a msmsWorkspace

Usage

checkIsotopes(
  w,
  mode = "pH",
  intensity_cutoff = 0,
  intensity_precision = "none",
  conflict = "strict",
  isolationWindow = 2,
  evalMode = "complete",
  plotSpectrum = TRUE,
  settings = getOption("RMassBank")
)

Arguments

Details

text describing parameter inputs in more detail.

• intensity_precisionThis parameter determines how strict the intensity values should adhere to the calculated intensity in relation to the parent peak. Options for this parameter are "none", where the intensity is irrelevant, "low", which has an error margin of 70% and "high", where the error margin is set to 35%. The recommended setting is "low", but can be changed to adjust to the intensity precision of the mass spectrometer.

Value

The msmsWorkspace with annotated isolation peaks

Author(s)

Michael Stravs, Eawag <[email protected]>

Erik Mueller, UFZ

Description

Checks if a specific compound (RmbSpectraSet) was found with child spectra in the raw file (found), has a complete set of MS2 spectra with useful peaks (complete), or is empty (note: spectra are currently not ever marked empty - empty should mean found, but no useful peaks at all. This is not yet currently tested.)

Usage

checkSpectra(s, property)

## S4 method for signature 'RmbSpectraSet,character'
checkSpectra(s, property)

Arguments

Value

TRUE or FALSE

Methods (by class)

• checkSpectra(s = RmbSpectraSet, property = character):

Author(s)

stravsmi

Description

Removes known electronic noise peaks from a peak table

Usage

cleanElnoise(peaks, noise=getOption("RMassBank")$electronicNoise,
		width = getOption("RMassBank")$electronicNoiseWidth)

## S4 method for signature 'data.frame,numeric,numeric'
cleanElnoise(
  peaks,
  noise = getOption("RMassBank")$electronicNoise,
  width = getOption("RMassBank")$electronicNoiseWidth
)

## S4 method for signature 'RmbSpectrum2,numeric,numeric'
cleanElnoise(
  peaks,
  noise = getOption("RMassBank")$electronicNoise,
  width = getOption("RMassBank")$electronicNoiseWidth
)

## S4 method for signature 'RmbSpectrum2List,numeric,numeric'
cleanElnoise(
  peaks,
  noise = getOption("RMassBank")$electronicNoise,
  width = getOption("RMassBank")$electronicNoiseWidth
)

## S4 method for signature 'RmbSpectraSet,numeric,numeric'
cleanElnoise(
  peaks,
  noise = getOption("RMassBank")$electronicNoise,
  width = getOption("RMassBank")$electronicNoiseWidth
)

Arguments

Value

Extends the aggregate data frame by column noise (logical), which is TRUE if the peak is marked as noise.

Methods (by class)

• cleanElnoise(peaks = data.frame, noise = numeric, width = numeric): Remove known electronic noise peaks

• cleanElnoise(peaks = RmbSpectrum2, noise = numeric, width = numeric): Remove known electronic noise peaks

• cleanElnoise(peaks = RmbSpectrum2List, noise = numeric, width = numeric): Remove known electronic noise peaks

• cleanElnoise(peaks = RmbSpectraSet, noise = numeric, width = numeric): Remove known electronic noise peaks

Author(s)

Michael Stravs

See Also

msmsWorkflow

Examples

# As used in the workflow:
## Not run: 
	    w@aggregated <- 
		cleanElnoise(w@aggregated)	

## End(Not run)

Description

Combines multiple msmsWorkspace items to one workspace which is used for multiplicity filtering.

Usage

combineMultiplicities(workspaces)

Arguments

Details

This feature is particularily meant to be used in conjunction with the confirmMode option of msmsWorkflow: a file can be analyzed with confirmMode = 0 (default) and subsequently with confirmMode = 1 (take second highest scan). The second analysis should contain "the same" spectra as the first one (but less intense) and can be used to confirm the peaks in the first spectra.

TO DO: Enable the combination of workspaces for combining e.g. multiple energy settings measured separately.

Value

A msmsWorkspace object prepared for step 8 processing.

Author(s)

Stravs MA, Eawag <[email protected]>

See Also

msmsWorkspace-class

Examples

## Not run: 
	w <- newMsmsWorkspace
 w@files <- c("spec1", "spec2")
 w1 <- msmsWorkflow(w, steps=c(1:7), mode="pH")
 w2 <- msmsWorkflow(w, steps=c(1:7), mode="pH", confirmMode = 1)
 wTotal <- combineMultiplicities(c(w1, w2))
 wTotal <- msmsWorkflow(wTotal, steps=8, mode="pH", archivename = "output")
 # continue here with mbWorkflow 

## End(Not run)

Description

The resulting SDF will be written to a file named 'Compoundlist.sdf'. The header for each block is the chemical name, tags for ID, SMILES and CAS are added in the description block

Usage

compoundlist2SDF(filename)

Arguments

Value

This method has no return value.

Author(s)

pstahlhofen

Examples

## Not run: 
			compoundlist2SDF("Compoundlist_filtered.csv")

## End(Not run)

Description

This method will automatically look for all single-block JCAMP files in the directory by picking all files ending in '.dx' (and not '.jdx'). A csv-file named 'Compoundlist.csv' will be created in the same directory. The Compoundlist contains columns 'ID', 'Name', 'SMILES' and 'CAS' where 'SMILES' might be empty if the compound is a derivative or if the CAS number could not be converted (see CAS2SMILES).

Usage

createCompoundlist()

Value

This method has no return value.

Author(s)

pstahlhofen

See Also

CAS2SMILES

Examples

## Not run: 
			# Prepare the compoundlist-creation
			splitMultiblockDX('my_multiblock_jcamp.jdx')
			createCompoundlist()

## End(Not run)

Description

Creates a MOL file (in memory or on disk) for a compound specified by the compound ID or by a SMILES code.

Usage

createMolfile(id_or_smiles, fileName = FALSE)

Arguments

Details

The function invokes OpenBabel (and therefore needs a correctly set OpenBabel path in the RMassBank settings), using the SMILES code retrieved with findSmiles or using the SMILES code directly. The current implementation of the workflow uses the latter version, reading the SMILES code directly from the MassBank record itself.

Value

A character array containing the MOL/SDF format file, ready to be written to disk.

Author(s)

Michael Stravs

References

OpenBabel: http://openbabel.org

See Also

findSmiles

Examples

# Benzene:
## Not run: 
createMolfile("C1=CC=CC=C1")

## End(Not run)

Description

Select a subset of external IDs from a CTS record.

Usage

CTS.externalIdSubset(data, database)

Arguments

Value

Returns an array of all external identifiers stored in the record for the given database.

Author(s)

Michele Stravs, Eawag <[email protected]>

Examples

## Not run: 
# Return all CAS registry numbers stored for benzene.
data <- getCtsRecord("UHOVQNZJYSORNB-UHFFFAOYSA-N")
cas <- CTS.externalIdSubset(data, "CAS")

## End(Not run)

Description

Find all available databases for a CTS record

Usage

CTS.externalIdTypes(data)

Arguments

Value

Returns an array of all database names for which there are external identifiers stored in the record.

Author(s)

Michele Stravs, Eawag <[email protected]>

Examples

## Not run: 
# Return all databases for which the benzene entry has
# links in the CTS record.

data <- getCTS("UHOVQNZJYSORNB-UHFFFAOYSA-N")
databases <- CTS.externalIdTypes(data)

## End(Not run)

Description

Calculates the Ring and Double Bond Equivalents for a chemical formula. The highest valence state of each atom is used, such that the returned DBE should never be below 0.

Usage

dbe(formula)

Arguments

Value

Returns the DBE for the given formula.

Author(s)

Michael Stravs

Examples

#
	dbe("C6H12O6")

Description

The deprofile functions convert profile-mode high-resolution input data to "centroid"-mode data amenable to i.e. centWave. This is done using full-width, half-height algorithm, spline algorithm or local maximum method.

Usage

deprofile.scan(scan, noise = NA, method = "deprofile.fwhm", 
					colnames = TRUE, ...)

				deprofile(df, noise, method, ...)

				deprofile.fwhm(df, noise = NA, cut = 0.5)

				deprofile.localMax(df, noise = NA)

				deprofile.spline(df, noise=NA, minPts = 5, step = 0.00001)

Arguments

Details

The deprofile.fwhm method is basically an R-semantic version of the "Exact Mass" m/z deprofiler from MZmine. It takes the center between the m/z values at half-maximum intensity for the exact peak mass. The logic is stolen verbatim from the Java MZmine algorithm, but it has been rewritten to use the fast R vector operations instead of loops wherever possible. It's slower than the Java implementation, but still decently fast IMO. Using matrices instead of the data frame would be more memory-efficient and also faster, probably.

The deprofile.localMax method uses local maxima and is probably the same used by e.g. Xcalibur. For well-formed peaks, "deprofile.fwhm" gives more accurate mass results; for some applications, deprofile.localMax might be better (e.g. for fine isotopic structure peaks which are not separated by a valley and also not at half maximum.) For MS2 peaks, which have no isotopes, deprofile.fwhm is probably the better choice generally.

deprofile.spline calculates the mass using a cubic spline, as the HiRes peak detection in OpenMS does.

The word "centroid" is used for convenience to denote not-profile-mode data. The data points are NOT mathematical centroids; we would like to have a better word for it.

The noise parameter was only included for completeness, I personally don't use it.

deprofile.fwhm and deprofile.localMax are the workhorses; deprofile.scan takes a 2-column scan as input. deprofile dispatches the call to the appropriate worker method.

Value

deprofile.scan: a matrix with 2 columns for m/z and intensity

Note

Known limitations: If the absolute leftmost stick or the absolute rightmost stick in a scan are maxima, they will be discarded! However, I don't think this will ever present a practical problem.

Author(s)

Michael Stravs, Eawag <[email protected]>

References

mzMine source code http://sourceforge.net/svn/?group_id=139835

Examples

## Not run: 
mzrFile <- openMSfile("myfile.mzML")
acqNo <- xraw@acquisitionNum[[50]]
scan.mzML.profile <- mzR::peaks(mzrFile, acqNo)
scan.mzML <- deprofile.scan(scan.mzML.profile) 
close(mzrFile)

## End(Not run)

Description

Exports MassBank recfile data arrays and corresponding molfiles to physical files on hard disk, for one compound.

Usage

exportMassbank(compiled, molfile = NULL)

Arguments

Details

The data from compiled is still used here, because it contains the "visible" accession number. In the plain-text format contained in files, the accession number is not "accessible" anymore since it's in the file.

Value

No return value.

Note

An improvement would be to write the accession numbers into names(compiled) and later into names(files) so compiled wouldn't be needed here anymore. (The compound ID would have to go into names(molfile), since it is also retrieved from compiled.)

Author(s)

Michael Stravs

References

MassBank record format: http://www.massbank.jp/manuals/MassBankRecord_en.pdf

See Also

createMolfile, toMassbank, mbWorkflow

Description

This method takes the info which is added to the aggregated table in the reanalysis and multiplicity filtering steps of the workflow, and adds it back into the spectra.

Usage

fillback(o, id, aggregated)

## S4 method for signature 'msmsWorkspace,missing,missing'
fillback(o)

## S4 method for signature 'RmbSpectraSet,missing,data.frame'
fillback(o, aggregated)

## S4 method for signature 'RmbSpectrum2,character,data.frame'
fillback(o, id, aggregated)

Arguments

Value

o msmsWorkspace, RmbSpectraSet or Rmbspectrum2 The same object that was given as input with new information filled into it

Description

Read the Compoundlist given by the filename and write a 'Compoundlist_filtered.csv', containing only the lines with a SMILES string

Usage

filterCompoundlist(filename)

Arguments

Value

This method has no return value.

Author(s)

pstahlhofen

Examples

## Not run: 
			filterCompoundlist('Compoundlist.csv')

## End(Not run)

Description

Filters a peak table (with annotated formulas) for accuracy. Low-accuracy peaks are removed.

Usage

filterLowaccResults(peaks, mode="fine", filterSettings  = getOption("RMassBank")$filterSettings)

Arguments

Details

In the coarse mode, mass tolerance is set to 10 ppm (above m/z 120) and 15 ppm (below m/z 120). This is useful for formula assignment before recalibration, where a wide window is desirable to accomodate the high mass deviations at low m/z values, so we get a nice recalibration curve.

In the fine run, the mass tolerance is set to 5 ppm over the whole mass range. This should be applied after recalibration.

Value

A list(TRUE = goodPeakDataframe, FALSE = badPeakDataframe) is returned: A data frame with all peaks which are "good" is in return[["TRUE"]].

Author(s)

Michael Stravs

See Also

analyzeMsMs, filterPeakSatellites

Examples

# from analyzeMsMs:
## Not run: childPeaksFilt <- filterLowaccResults(childPeaksInt, filterMode)

Description

Multiplicity filtering: Removes peaks which occur only once in a n-spectra set.

Usage

filterMultiplicity(w, archivename=NA, mode="pH", recalcBest = TRUE,
		multiplicityFilter = getOption("RMassBank")$multiplicityFilter)

Arguments

Details

This function executes multiplicity filtering for a set of spectra using the workhorse function filterPeaksMultiplicity (see details there) and retrieves problematic filtered peaks (peaks which are of high intensity but were discarded, because either no formula was assigned or it was not present at least 2x), using the workhorse function problematicPeaks. The results are returned in a format ready for further processing with mbWorkflow.

Value

A list object with values:

Author(s)

Michael Stravs

See Also

filterPeaksMultiplicity,problematicPeaks

Examples

## Not run: 
    refilteredRcSpecs <- filterMultiplicity(
			w, "myarchive", "pH")

## End(Not run)

Description

Filters satellite peaks in FT spectra which arise from FT artifacts and from conversion to stick mode. A very simple rule is used which holds mostly true for MSMS spectra (and shouldn't be applied to MS1 spectra which contain isotope structures...)

Usage

filterPeakSatellites(peaks, filterSettings = getOption("RMassBank")$filterSettings)

Arguments

Details

The function cuts off all peaks within 0.5 m/z from every peak, in decreasing intensity order, which are below 5 intensity. E.g. for peaks m/z=100, int=100; m/z=100.2, int=2, m/z=100.3, int=6, m/z 150, int=10: The most intense peak (m/z=100) is selected, all neighborhood peaks below 5 peak) and the next less intense peak is selected. Here this is the m/z=150 peak. All low-intensity neighborhood peaks are removed (nothing). The next less intense peak is selected (m/z=100.3) and again neighborhood peaks are cut away (nothing to cut here. Note that the m/z = 100.2 peak was alredy removed.)

Value

Returns the peak table with satellite peaks removed.

Note

This is a very crude rule, but works remarkably well for our spectra.

Author(s)

Michael Stravs

See Also

analyzeMsMs, filterLowaccResults

Examples

# From the workflow:
## Not run: 
    # Filter out satellite peaks:
    shot <- filterPeakSatellites(shot)
    shot_satellite_n <- setdiff(row.names(shot_full), row.names(shot))
    shot_satellite <- shot_full[shot_satellite_n,]
    # shot_satellite contains the peaks which were eliminated as satellites.

## End(Not run)

Description

For every compound, every peak (with annotated formula) is compared across all spectra. Peaks whose formula occurs only once for all collision energies / spectra types, are discarded. This eliminates "stochastic formula hits" of pure electronic noise peaks efficiently from the spectra. Note that in the author's experimental setup two spectra were recorded at every collision energy, and therefore every peak-formula should appear at least twice if it is real, even if it is by chance a fragment which appears on only one collision energy setting. The function was not tested in a different setup. Therefore, use with a bit of caution.

Usage

filterPeaksMultiplicity(w, recalcBest = TRUE)

Arguments

Value

The peak table is returned, enriched with columns:

• formulaMultiplicityThe # of occurrences of this formula in the spectra of its compounds.

Author(s)

Michael Stravs, EAWAG <[email protected]>

Examples

## Not run: 
		peaksFiltered <- filterPeaksMultiplicity(peaksMatched(w), 
			"formula", TRUE)
		peaksOK <- subset(peaksFiltered, formulaMultiplicity > 1)

## End(Not run)

Description

Extract EICs from raw data for a determined mass window.

Usage

findEIC(
  msRaw,
  mz,
  limit = NULL,
  rtLimit = NA,
  headerCache = NULL,
  floatingRecalibration = NULL,
  peaksCache = NULL,
  polarity = NA,
  msLevel = 1,
  precursor = NULL
)

Arguments

Value

A [rt, intensity, scan] matrix (scan being the scan number.)

Author(s)

Michael A. Stravs, Eawag <[email protected]>

See Also

findMsMsHR

Description

Retrieves the exact mass of the uncharged molecule. It works directly from the SMILES and therefore is used in the MassBank workflow (mbWorkflow) - there, all properties are calculated from the SMILES code retrieved from the database. (Alternatively, takes also the compound ID as parameter and looks it up.) Calculation relies on Rcdk.

Usage

findMass(cpdID_or_smiles, retrieval = "standard", mode = "pH")

Arguments

Value

Returns the exact mass of the uncharged molecule.

Author(s)

Michael Stravs

See Also

findMz

Examples

##
findMass("OC[C@H]1OC(O)[C@H](O)[C@@H](O)[C@@H]1O")

Description

Extracts MS/MS spectra from LC-MS raw data for a specified precursor, specified either via the RMassBank compound list (see loadList) or via a mass.

Usage

findMsMsHR(
  fileName = NULL,
  msRaw = NULL,
  cpdID,
  mode = "pH",
  confirmMode = 0,
  useRtLimit = TRUE,
  ppmFine = getOption("RMassBank")$findMsMsRawSettings$ppmFine,
  mzCoarse = getOption("RMassBank")$findMsMsRawSettings$mzCoarse,
  fillPrecursorScan = getOption("RMassBank")$findMsMsRawSettings$fillPrecursorScan,
  rtMargin = getOption("RMassBank")$rtMargin,
  deprofile = getOption("RMassBank")$deprofile,
  headerCache = NULL,
  peaksCache = NULL,
  enforcePolarity = getOption("RMassBank")$enforcePolarity,
  diaWindows = getOption("RMassBank")$findMsMsRawSettings$diaWindows
)

findMsMsHR.mass(
  msRaw,
  mz,
  limit.coarse,
  limit.fine,
  rtLimits = NA,
  maxCount = NA,
  headerCache = NULL,
  fillPrecursorScan = FALSE,
  deprofile = getOption("RMassBank")$deprofile,
  peaksCache = NULL,
  cpdID = NA,
  polarity = NA,
  diaWindows = getOption("RMassBank")$findMsMsRawSettings$diaWindows
)

Arguments

Details

Different versions of the function get the data from different sources. Note that findMsMsHR and findMsMsHR.direct differ mainly in that findMsMsHR opens a file whereas findMsMs.direct uses an open file handle - both are intended to be used in a full process which involves compound lists etc. In contrast, findMsMsHR.mass is a low-level function which uses the mass directly for lookup and is intended for use as a standalone function in unrelated applications.

Value

An RmbSpectraSet (for findMsMsHR). Contains parent MS1 spectrum (@parent), a block of dependent MS2 spectra ((@children) and some metadata (id,mz,name,mode in which the spectrum was acquired.

For findMsMsHR.mass: a list of RmbSpectraSets as defined above, sorted by decreasing precursor intensity.

Functions

• findMsMsHR.mass(): A submethod of find MsMsHR that retrieves basic spectrum data

Note

findMsMs.direct is deactivated

Author(s)

Michael A. Stravs, Eawag <[email protected]>

See Also

findEIC

Examples

## Not run: 
			loadList("mycompoundlist.csv")
			# if Atrazine has compound ID 1:
			msms_atrazine <- findMsMsHR(fileName = "Atrazine_0001_pos.mzML", cpdID = 1, mode = "pH")
			# Or alternatively:
			msRaw <- openMSfile("Atrazine_0001_pos.mzML")
			msms_atrazine <- findMsMsHR(msRaw=msRaw, cpdID = 1, mode = "pH")
			# Or directly by mass (this will return a list of spectra sets):
			mz <- findMz(1)$mzCenter
			msms_atrazine_all <- findMsMsHR.mass(msRaw, mz, 1, ppm(msRaw, 10, p=TRUE))
			msms_atrazine <- msms_atrazine_all[[1]]

## End(Not run)

Description

This interface has been discontinued. findMsMsHR now supports the same parameters (use named parameters).

Usage

findMsMsHR.direct(
  msRaw,
  cpdID,
  mode = "pH",
  confirmMode = 0,
  useRtLimit = TRUE,
  ppmFine = getOption("RMassBank")$findMsMsRawSettings$ppmFine,
  mzCoarse = getOption("RMassBank")$findMsMsRawSettings$mzCoarse,
  fillPrecursorScan = getOption("RMassBank")$findMsMsRawSettings$fillPrecursorScan,
  rtMargin = getOption("RMassBank")$rtMargin,
  deprofile = getOption("RMassBank")$deprofile,
  headerCache = NULL
)

Arguments

Value

an error

Author(s)

stravsmi

Description

Extract an MS/MS spectrum or multiple MS/MS spectra based on the TIC of the MS2 and precursor mass, picking the most intense MS2 scan. Can be used, for example, to get a suitable MS2 from direct infusion data which was collected with purely targeted MS2 without MS1.

Usage

findMsMsHR.ticms2(
  msRaw,
  mz,
  limit.coarse,
  limit.fine,
  rtLimits = NA,
  maxCount = NA,
  headerCache = NULL,
  fillPrecursorScan = FALSE,
  deprofile = getOption("RMassBank")$deprofile,
  trace = "ms2tic"
)

Arguments

Details

Note that this is not a precise function and only really makes sense in direct infusion and if the precursor is really known, because MS2 precursor data is only "roughly" accurate (to 2 dp). The regular findMsMsHR functions confirm the exact mass of the precursor in the MS1 scan.

Value

a list of "spectrum sets" as defined in findMsMsHR, sorted by decreasing precursor intensity.

Author(s)

stravsmi

Description

This function is currently used to read msp files containing data that were already processed in order to convert the results to MassBank records.

Usage

findMsMsHRperMsp(fileName, cpdIDs, mode = "pH")

findMsMsHRperMsp.direct(fileName, cpdIDs, mode = "pH")

Arguments

Value

An RmbSpectraSet with integrated information from the msp files

Functions

• findMsMsHRperMsp.direct(): A submethod of findMsMsHrperxcms that retrieves basic spectrum data

Description

Picks peaks from mz-files and returns the pseudospectra that CAMERA creates with the help of XCMS

Usage

findMsMsHRperxcms(
  fileName,
  cpdID,
  mode = "pH",
  findPeaksArgs = NULL,
  plots = FALSE,
  MSe = FALSE
)

findMsMsHRperxcms.direct(
  fileName,
  cpdID,
  mode = "pH",
  findPeaksArgs = NULL,
  plots = FALSE,
  MSe = FALSE
)

Arguments

Value

The spectra generated from XCMS

Functions

• findMsMsHRperxcms.direct(): A submethod of findMsMsHrperxcms that retrieves basic spectrum data

Author(s)

Erik Mueller

See Also

msmsWorkflow toRMB

Examples

## Not run: 
		fileList <- list.files(system.file("XCMSinput", package = "RMassBank"), "Glucolesquerellin", full.names=TRUE)[3]
	loadList(system.file("XCMSinput/compoundList.csv",package="RMassBank"))
     psp <- findMsMsHRperxcms(fileList,2184)

## End(Not run)

Description

Retrieves compound information from the loaded compound list or calculates it from the SMILES code in the list.

Usage

findMz(cpdID, mode = "pH", ppm = 10, deltaMz = 0, retrieval="standard",
unknownMass = getOption("RMassBank")$unknownMass )

findRt(cpdID) 

findSmiles(cpdID) 

findFormula(cpdID, retrieval="standard") 

findCAS(cpdID)

findName(cpdID)

findLevel(cpdID, compact=FALSE)

Arguments

Value

findMz will return a list(mzCenter=, mzMin=, mzMax=) with the molecular weight of the given ion, as calculated from the SMILES code and Rcdk.

findRt, findSmiles,findCAS,findName will return the corresponding entry from the compound list. findFormula returns the molecular formula as determined from the SMILES code.

Author(s)

Michael Stravs

See Also

findMass, loadList, findMz.formula

Examples

## Not run: %
	findMz(123, "pH", 5)
	findFormula(123)

## End(Not run)

Description

Find the exact mass +/- a given margin for a given formula or its ions and adducts.

Usage

findMz.formula(formula, mode = "pH", ppm = 10, deltaMz = 0)

Arguments

Value

A list(mzMin=, mzCenter=, mzMax=) with the masses.

Author(s)

Michael A. Stravs, Eawag <[email protected]>

See Also

findMz

Examples

findMz.formula("C6H6")

Description

This function reads out the content of different slots of the workspace object and finds out which steps have already been processed on it.

Usage

findProgress(workspace)

Arguments

Value

An array containing all msmsWorkflow steps which have likely been processed.

Author(s)

Stravs MA, Eawag <[email protected]>

Examples

## Not run: 
findProgress(w)

## End(Not run)

Description

flatten converts a list of MassBank compound information sets (as retrieved by gatherData) to a flat table, to be exported into an infolist. readMbdata reads a single record from an infolist flat table back into a MassBank (half-)entry.

Usage

flatten(mbdata) 

readMbdata(row)

Arguments

Details

Neither the flattening system itself nor the implementation are particularly fantastic, but since hand-checking of records is a necessary evil, there is currently no alternative (short of coding a complete GUI for this and working directly on the records.)

Value

flatten returns a tibble (not a data frame or matrix) to be written to CSV.

readMbdata returns a list of type list(id= compoundID, ..., 'ACCESSION' = '', 'RECORD_TITLE' = '', ) etc.

Author(s)

Michael Stravs

References

MassBank record format: http://www.massbank.jp/manuals/MassBankRecord_en.pdf

See Also

gatherData,loadInfolist

Examples

## Not run: 
	# Collect some data to flatten
	ids <- c(40,50,60,70)
 data <- lapply(ids, gatherData)
 # Flatten the data trees to a table
 flat.table <- flatten(data)
 # reimport the table into a tree
 data.reimported <- apply(flat.table, 1, readMbdata)

## End(Not run)

Description

Converts molecular formulas from string to list representation or vice versa.

Usage

list.to.formula(flist) 

formulastring.to.list(formula)

Arguments

Details

The function doesn't care about whether your formula makes sense. However, "C3.5O4" will give list("C" = 3, "O" = 4) because regular expressions are used for matching (however, list("C" = 3.5, "O" = 4) gives "C3.5O4".) Duplicate elements cause problems; only "strict" molecular formulas ("CH4O", but not "CH3OH") work correctly.

Value

list.to.formula returns a string representation of the formula; formulastring.to.list returns the list representation.

Author(s)

Michael Stravs

See Also

add.formula, order.formula, is.valid.formula

Examples

#
	list.to.formula(list("C" = 4, "H" = 12))
	# This is also OK and useful to calculate e.g. adducts or losses.
	list.to.formula(list("C" = 4, "H" = -1))
	formulastring.to.list(list.to.formula(formulastring.to.list("CHIBr")))

Description

Retrieves annotation data for a compound from the internet service US EPA CCTE based on the inchikey generated by babel or Cactus

Usage

gatherCCTE(key, api_key)

Arguments

Details

The data retrieved is the US EPA DTXSID, the US EPA chemical dashboard substance ID, the CAS-RN, the DTX preferred name, and the DTXCID (chemical ID).

Value

Returns a list with 5 slots: dtxsid The US EPA chemical dashboard substance id dtxcid The US EPA chemical dashboard chemical id preferredName The US EPA chemical dashboard preferred name casrn The latest CAS registration number smiles The SMILES annotation of the structure

Author(s)

Tobias Schulze

References

CCTE REST: https://api-ccte.epa.gov/docs/

See Also

mbWorkflow

Examples

# Gather data for compound ID 131
## Not run: gatherCCTE("QEIXBXXKTUNWDK-UHFFFAOYSA-N", api_key = NA)

Description

Retrieves annotation data for a compound from the internet services CTS, Pubchem, Chemspider and Cactvs, based on the SMILES code and name of the compounds stored in the compound list.

Usage

gatherData(id)

Arguments

Details

Composes the "upper part" of a MassBank record filled with chemical data about the compound: name, exact mass, structure, CAS no., links to PubChem, KEGG, ChemSpider. The instrument type is also written into this block (even if not strictly part of the chemical information). Additionally, index fields are added at the start of the record, which will be removed later: id, dbcas, dbname from the compound list, dataused to indicate the used identifier for CTS search (smiles or dbname).

Additionally, the fields ACCESSION and RECORD_TITLE are inserted empty and will be filled later on.

Value

Returns a list of type list(id= compoundID, ..., 'ACCESSION' = '', 'RECORD_TITLE' = '', ) etc.

Author(s)

Michael Stravs

References

Chemical Translation Service: http://uranus.fiehnlab.ucdavis.edu:8080/cts/homePage cactus Chemical Identifier Resolver: http://cactus.nci.nih.gov/chemical/structure MassBank record format: http://www.massbank.jp/manuals/MassBankRecord_en.pdf Pubchem REST: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST.html Chemspider InChI conversion: https://www.chemspider.com/InChI.asmx

See Also

mbWorkflow

Examples

# Gather data for compound ID 131
## Not run: gatherData(131)

Description

Retrieves annotation data for a compound by using babel, based on the SMILES code and name of the compounds stored in the compound list.

Usage

gatherDataBabel(id)

Arguments

Details

Composes the "upper part" of a MassBank record filled with chemical data about the compound: name, exact mass, structure, CAS no.. The instrument type is also written into this block (even if not strictly part of the chemical information). Additionally, index fields are added at the start of the record, which will be removed later: id, dbcas, dbname from the compound list.

Additionally, the fields ACCESSION and RECORD_TITLE are inserted empty and will be filled later on.

This function is an alternative to gatherData, in case CTS is down or if information on one or more of the compounds in the compound list are sparse

Value

Returns a list of type list(id= compoundID, ..., 'ACCESSION' = '', 'RECORD_TITLE' = '', ) etc.

Author(s)

Michael Stravs, Erik Mueller

References

MassBank record format: http://www.massbank.jp/manuals/MassBankRecord_en.pdf

See Also

mbWorkflow

Examples

# Gather data for compound ID 131
## Not run: gatherDataBabel(131)

Description

Retrieves annotation data for an unknown compound by using basic information present

Usage

gatherDataUnknown(id, mode, retrieval)

Arguments

Details

Composes the "upper part" of a MassBank record filled with chemical data about the compound: name, exact mass, structure, CAS no.. The instrument type is also written into this block (even if not strictly part of the chemical information). Additionally, index fields are added at the start of the record, which will be removed later: id, dbcas, dbname from the compound list.

Additionally, the fields ACCESSION and RECORD_TITLE are inserted empty and will be filled later on.

This function is used to generate the data in case a substance is unknown, i.e. not enough information is present to derive anything about formulas or links

Value

Returns a list of type list(id= compoundID, ..., 'ACCESSION' = '', 'RECORD_TITLE' = '', ) etc.

Author(s)

Michael Stravs, Erik Mueller

References

MassBank record format: http://www.massbank.jp/manuals/MassBankRecord_en.pdf

See Also

mbWorkflow

Examples

# Gather data for compound ID 131
## Not run: gatherDataUnknown(131,"pH")

Description

Retrieves annotation data for a compound from the internet service Pubchem based on the inchikey generated by babel or Cactus

Usage

gatherPubChem(key)

Arguments

Details

The data retrieved is the Pubchem CID, a synonym from the Pubchem database, the IUPAC name (using the preferred if available) and a Chebi link

Value

Returns a list with 4 slots: PcID The Pubchem CID Synonym An arbitrary synonym for the compound name IUPAC A IUPAC-name (preferred if available) Chebi The identification number of the chebi database

Author(s)

Erik Mueller

References

Pubchem REST: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST.html Chebi: http://www.ebi.ac.uk/chebi

See Also

mbWorkflow

Examples

# Gather data for compound ID 131
## Not run: gatherPubChem("QEIXBXXKTUNWDK-UHFFFAOYSA-N")

Description

Collects the info for 'ai, ac_lc, ac_ms' for general, LC and MS info respectively. The info comes from the settings except for the compound-specific part, which is omitted if there is no 'cpd' specified.

Usage

getAnalyticalInfo(cpd = NULL)

Arguments

Description

Retrieves information from the Cactus Chemical Identifier Resolver (PubChem).

Usage

getCactus(identifier, representation)

Arguments

Details

It is not necessary to specify in which format the identifier is. Somehow, cactus does this automatically.

Value

The result of the query, in plain text. Can be NA, or one or multiple lines (character array) of results.

Note

Note that the InChI key is retrieved with a prefix (InChIkey=), which must be removed for most database searches in other databases (e.g. CTS).

Author(s)

Michael Stravs

References

cactus Chemical Identifier Resolver: http://cactus.nci.nih.gov/chemical/structure

See Also

getCtsRecord, getPcId

Examples

# Benzene:
getCactus("C1=CC=CC=C1", "cas")
getCactus("C1=CC=CC=C1", "stdinchikey")
getCactus("C1=CC=CC=C1", "chemspider_id")

Description

Retrieves CCTE CASRN from US EPA for a search term.

Usage

getCASRN(key, api_key)

Arguments

Details

Only the first result is returned currently. The function should be regarded as experimental and has not thoroughly been tested.

Value

The CCTE CAS RN (in string type)

Author(s)

Tobias Schulze

References

CCTE search: https://api-ccte.epa.gov/docs

CCTE REST: https://api-ccte.epa.gov/docs

Examples

## Not run: 
getCASRN("MKXZASYAUGDDCJ-NJAFHUGGSA-N")

## End(Not run)

Description

Given an InChIKey, this function queries the chemspider web API to retrieve the Chemspider ID of he compound with that InChIkey.

Usage

getCSID(query)

Arguments

Value

Returns the chemspide

Author(s)

Michele Stravs, Eawag <[email protected]>

Erik Mueller, UFZ <[email protected]>

Examples

## Not run: 
# Return all CAS registry numbers stored for benzene.
data <- getCtsRecord("UHOVQNZJYSORNB-UHFFFAOYSA-N")
cas <- CTS.externalIdSubset(data, "CAS")

## End(Not run)

Description

Convert a single ID to another using CTS.

Usage

getCtsKey(query, from = "Chemical Name", to = "InChIKey")

Arguments

Value

An unordered array with the resulting converted key(s).

Author(s)

Michele Stravs, Eawag <[email protected]>

Examples

k <- getCtsKey("benzene", "Chemical Name", "InChIKey")

Description

Retrieves a complete CTS record from the InChI key.

Usage

getCtsRecord(key)

Arguments

Value

Returns a list with all information from CTS: inchikey, inchicode, formula, exactmass contain single values. synonyms contains an unordered list of scored synonyms (type, name, score, where type indicates either a normal name or a specific IUPAC name, see below). externalIds contains an unordered list of identifiers of the compound in various databases (name, value, where name is the database name and value the identifier in that database.)

Note

Currently, the CTS results are still incomplete; the name scores are all 0, formula and exact mass return zero.

Author(s)

Michele Stravs, Eawag <[email protected]>

References

Chemical Translation Service: https://cts.fiehnlab.ucdavis.edu

Examples

data <- getCtsRecord("UHOVQNZJYSORNB-UHFFFAOYSA-N")
# show all synonym "types"
types <- unique(unlist(lapply(data$synonyms, function(i) i$type)))
## Not run: print(types)

Description

Returns a data frame with columns for all non-empty slots in a RmbSpectrum2 object. Note that MSnbase::Spectrum has a method as.data.frame, however that one will return only mz, intensity. This function is kept separate to ensure downwards compatibility since it returns more columns than MSnbase as.data.frame.

Usage

## S4 method for signature 'RmbSpectrum2'
getData(s)

Arguments

Value

A data frame with columns for every set slot.

Author(s)

stravsmi

Description

Retrieves CCTE DTXCID from US EPA for a search term.

Usage

getDTXCID(key, api_key)

Arguments

Details

Only the first result is returned currently. The function should be regarded as experimental and has not thoroughly been tested.

Value

The DTXCID (in string type)

Author(s)

Tobias Schulze

References

CCTE search: https://api-ccte.epa.gov/docs

CCTE REST: https://api-ccte.epa.gov/docs

Examples

## Not run: 
getDTXCID("MKXZASYAUGDDCJ-NJAFHUGGSA-N")

## End(Not run)

Description

Retrieves CCTE DTXSID from US EPA for a search term.

Usage

getDTXSID(key, api_key)

Arguments

Details

Only the first result is returned currently. The function should be regarded as experimental and has not thoroughly been tested.

Value

The DTXSID (in string type)

Author(s)

Tobias Schulze

References

CCTE search: https://api-ccte.epa.gov/docs

CCTE REST: https://api-ccte.epa.gov/docs

Examples

## Not run: 
getDTXSID("MKXZASYAUGDDCJ-NJAFHUGGSA-N")

## End(Not run)

Description

Retrieves CCTE SMILES from US EPA for a search term.

Usage

getDTXSMILES(key, api_key)

Arguments

Details

Only the first result is returned currently. The function should be regarded as experimental and has not thoroughly been tested.

Value

The SMILES (in string type)

Author(s)

Tobias Schulze

References

CCTE search: https://api-ccte.epa.gov/docs

CCTE REST: https://api-ccte.epa.gov/docs

Examples

## Not run: 
getDTXSMILES("MKXZASYAUGDDCJ-NJAFHUGGSA-N")

## End(Not run)

Description

The content will always be returned as character-string

Usage

getField(parsedJDX, field_name)

Arguments

Value

The field's content

Author(s)

pstahlhofen

See Also

readJDX

Examples

## Not run: 
			parsedJDX <- readJDX('my_singleblock_jcamp.dx')
			title <- getField(parsedJDX, "TITLE")

## End(Not run)

Description

Generates a Rcdk molecule object from SMILES code, which is fully typed and usable (in contrast to the built-in parse.smiles).

Usage

getMolecule(smiles)

Arguments

Details

NOTE: As of today (2012-03-16), Rcdk discards stereochemistry when loading the SMILES code! Therefore, do not trust this function blindly, e.g. don't generate InChI keys from the result. It is, however, useful if you want to compute the mass (or something else) with Rcdk.

Value

A Rcdk IAtomContainer reference.

Author(s)

Michael Stravs

See Also

parse.smiles

Examples

# Lindane:
getMolecule("C1(C(C(C(C(C1Cl)Cl)Cl)Cl)Cl)Cl")
# Benzene:
getMolecule("C1=CC=CC=C1")

Description

Retrieves PubChem CIDs for a search term.

Usage

getPcId(query, from = "inchikey")

Arguments

Details

Only the first result is returned currently. The function should be regarded as experimental and has not thoroughly been tested.

Value

The PubChem CID (in string type).

Author(s)

Michael Stravs, Erik Mueller

References

PubChem search: http://pubchem.ncbi.nlm.nih.gov/

Pubchem REST: https://pubchem.ncbi.nlm.nih.gov/pug_rest/PUG_REST.html

See Also

getCtsRecord, getCactus

Examples

getPcId("MKXZASYAUGDDCJ-NJAFHUGGSA-N")

Description

Retrieves CCTE Preferred Name from US EPA for a search term.

Usage

getPrefName(key, api_key)

Arguments

Details

Only the first result is returned currently. The function should be regarded as experimental and has not thoroughly been tested.

Value

The CCTE Preferred Name (in string type)

Author(s)

Tobias Schulze

References

CCTE search: https://api-ccte.epa.gov/docs

CCTE REST: https://api-ccte.epa.gov/docs

Examples

## Not run: 
getPrefName("MKXZASYAUGDDCJ-NJAFHUGGSA-N")

## End(Not run)

Description

Checks whether the formula is chemically valid, i.e. has no zero-count or negative-count elements.

Usage

is.valid.formula(formula)

Arguments

Details

The check is only meant to identify formulas which have negative elements, which can arise from the subtraction of adducts. It is not a high-level formula "validity" check like e.g. the Rcdk function isvalid.formula which uses the nitrogen rule or a DBE rule.

Author(s)

Michael Stravs

See Also

list.to.formula, add.formula, order.formula

Examples

#
is.valid.formula(list(C=0,H=1,Br=2))
is.valid.formula("CH2Cl")
is.valid.formula("C0H2")

Description

Loads MassBank compound information lists (i.e. the lists which were created in the first two steps of the MassBank mbWorkflow and subsequently edited by hand.).

Usage

loadInfolists(mb, path)

 loadInfolist(mb, fileName)

 resetInfolists(mb)

Arguments

Details

resetInfolists clears the information lists, i.e. it creates a new empty list in mbdata_archive. loadInfolist loads a single CSV file, whereas loadInfolists loads a whole directory.

Value

The new workspace with loaded/reset lists.

Author(s)

Michael Stravs, Tobias Schulze

Examples

#
## Not run: mb <- resetInfolists(mb)
	mb <- loadInfolist(mb, "my_csv_infolist.csv")
## End(Not run)

Description

Loads a CSV compound list with compound IDs

Usage

loadList(path, listEnv=NULL, check=TRUE)

resetList()

Arguments

Details

The list is loaded into the variable compoundList in the environment listEnv (which defaults to the global environment) and used by the findMz, findCAS, ... functions. The CSV file is required to have at least the following columns, which are used for further processing and must be named correctly (but present in any order): ID, Name, SMILES, RT, CAS

resetList() clears a currently loaded list.

Value

No return value.

Author(s)

Michael Stravs

See Also

findMz

Examples

##
## Not run: loadList("mylist.csv")

Description

Makes a list.tsv file in the "moldata" folder.

Usage

makeMollist(compiled)

Arguments

Details

Generates the list.tsv file which is needed by MassBank to connect records with their respective molfiles. The first compound name is linked to a mol-file with the compound ID (e.g. 2334.mol for ID 2334).

Value

No return value.

Author(s)

Michael A. Stravs, Eawag <[email protected]>

Description

Generates a peak cache table for use with findMsMsHR functions.

Usage

makePeaksCache(msRaw, headerCache)

Arguments

Value

A list of dataframes as from mzR::peaks.

Author(s)

stravsmi

Description

Recalibrates MS/MS spectra by building a recalibration curve of the assigned putative fragments of all spectra in aggregatedSpecs (measured mass vs. mass of putative associated fragment) and additionally the parent ion peaks.

Usage

makeRecalibration(w,  
 	recalibrateBy = getOption("RMassBank")$recalibrateBy,
		recalibrateMS1 = getOption("RMassBank")$recalibrateMS1,
		recalibrator = getOption("RMassBank")$recalibrator,
		recalibrateMS1Window = getOption("RMassBank")$recalibrateMS1Window 
		)

 recalibrateSpectra(rawspec = NULL, rc = NULL, rc.ms1=NULL, w = NULL,
		recalibrateBy = getOption("RMassBank")$recalibrateBy,
		recalibrateMS1 = getOption("RMassBank")$recalibrateMS1)




 recalibrateSingleSpec(spectrum, rc, 
		recalibrateBy = getOption("RMassBank")$recalibrateBy)

Arguments

Details

Note that the actually used recalibration functions are governed by the general MassBank settings (see recalibrate).

If a set of acquired LC-MS runs contains spectra for two different ion types (e.g. [M+H]+ and [M+Na]+) which should both be processed by RMassBank, it is necessary to do this in two separate runs. Since it is likely that one ion type will be the vast majority of spectra (e.g. most in [M+H]+ mode), and only few spectra will be present for other specific adducts (e.g. only few [M+Na]+ spectra), it is possible that too few spectra are present to build a good recalibration curve using only e.g. the [M+Na]+ ions. Therefore we recommend, for one set of LC/MS runs, to build the recalibration curve for one ion type (msmsWorkflow(mode="pH", steps=c(1:8), newRecalibration=TRUE)) and reuse the same curve for processing different ion types (msmsWorkflow(mode="pNa", steps=c(1:8), newRecalibration=FALSE)). This also ensures a consistent recalibration across all spectra of the same batch.

Value

makeRecalibration: a list(rc, rc.ms1) with recalibration curves for the MS2 and MS1 spectra.

recalibrateSpectra: if rawspec is not NULL, returns the recalibrated spectra as RmbSpectraSetList. All spectra have their mass recalibrated and evaluation data deleted.

recalibrateSingleSpec: the recalibrated Spectrum (same object, recalibrated masses, evaluation data like assigned formulae etc. deleted).

Author(s)

Michael Stravs, Eawag <[email protected]>

Examples

## Not run:  
			rcCurve <- recalibrateSpectra(w, "pH")
			w@spectra <- recalibrateSpectra(mode="pH", rawspec=w@spectra, w=myWorkspace)
			w@spectra <- recalibrateSpectra(mode="pH", rawspec=w@spectra,	rcCurve$rc, rcCurve$rc.ms1)
			
## End(Not run)

Description

Uses data generated by msmsWorkflow to create MassBank records.

Usage

mbWorkflow(
  mb,
  steps = c(1, 2, 3, 4, 5, 6, 7, 8),
  infolist_path = "./infolist.csv",
  gatherData = "online",
  filter = TRUE
)

Arguments

Details

See the vignette vignette("RMassBank") for detailed informations about the usage.

Steps:

Step 1: Find which compounds don't have annotation information yet. For these compounds, pull information from several databases (using gatherData).

Step 2: If new compounds were found, then export the infolist.csv and stop the workflow. Otherwise, continue.

Step 3: Take the archive data (in table format) and reformat it to MassBank tree format.

Step 4: Compile the spectra. Using the skeletons from the archive data, create MassBank records per compound and fill them with peak data for each spectrum. Also, assign accession numbers based on scan mode and relative scan no.

Step 5: Convert the internal tree-like representation of the MassBank data into flat-text string arrays (basically, into text-file style, but still in memory)

Step 6: For all OK records, generate a corresponding molfile with the structure of the compound, based on the SMILES entry from the MassBank record. (This molfile is still in memory only, not yet a physical file)

Step 7: If necessary, generate the appropriate subdirectories, and actually write the files to disk.

Step 8: Create the list.tsv in the molfiles folder, which is required by MassBank to attribute substances to their corresponding structure molfiles.

Value

The processed mbWorkspace.

Author(s)

Michael A. Stravs, Eawag <[email protected]>

See Also

mbWorkspace-class

Examples

## Not run: 
		mb <- newMbWorkspace(w) # w being a msmsWorkspace
		mb <- loadInfolists(mb, "D:/myInfolistPath")
		mb <- mbWorkflow(mb, steps=c(1:3), "newinfos.csv")
		

## End(Not run)

Description

A workspace which stores input and output data for use with mbWorkflow.

Usage

## S4 method for signature 'mbWorkspace'
show(object)

Arguments

Details

Slots:

spectra, aggregated

The corresponding input data from msmsWorkspace-class

additionalPeaks

A list of additional peaks which can be loaded using addPeaks.

mbdata, mbdata_archive, mbdata_relisted

Infolist data: Data for annotation of MassBank records, which can be loaded using loadInfolists.

compiled, compiled_ok

Compiled tree-structured MassBank records. compiled_ok contains only the compounds with at least one valid spectrum.

mbfiles

Compiled MassBank records in text representation.

molfile

MOL files with the compound structures.

ok,problems

Index lists for internal use which denote which compounds have valid spectra.

Methods:

show

Shows a brief summary of the object. Currently only a stub.

Author(s)

Michael Stravs, Eawag <[email protected]>

See Also

mbWorkflow

Description

This procedure first sorts peaks by intensity (descending sort) and then starts iterating over the peaks, removing all entries that deviate "sufficiently far" from the currently selected peak. See the Details section for a full explanation and information on how to fine-tune peak removal.

Usage

mergePeaks(peaks, ...)

## S4 method for signature 'data.frame'
mergePeaks(peaks, ...)

## S4 method for signature 'matrix'
mergePeaks(peaks, ...)

## S4 method for signature 'RmbSpectrum2'
mergePeaks(peaks, ...)

## S4 method for signature 'Spectrum'
mergePeaks(peaks, ...)

Arguments

Details

Three parameters must be passed to mergePeaks for peak-removal control in this order: - cutoff_dppm_limit - cutoff_absolute_limit - cutoff_intensity_limit The method iterates through the peaks, beginning with the highest-intensity peak and in each step removes all other peaks that fulfill conditions 1 AND 2 relative to the selected peak 1. Their m/z value does not deviate too far from the one of the selected peak. i.e. if the selected peak is p and the checked peak is c, it holds that EITHER |p$mz - c$mz| <= cutoff_absolute_limit OR |p$mz - c$mz| <= ppm(p$mz, cutoff_dppm_limit, p=TRUE) (see ppm) 2. Their intensity is much smaller than the one of the selected peak, i.e. c$mz < cutoff_intensity_limit * p$mz for a suitable cutoff_intensity_limit between 0 and 1.

Value

object of the same class as peaks The result contains a reduced peak-table ordered by m/z

See Also

getData, setData, ppm

Examples

## Not run: mergePeaks(spectrum, 10, 0.5, 0.05)

Description

This method takes a collection of RmbSpectrum2 objects and merges them into a single RmbSpectrum2 object

Usage

mergeSpectra(spectra, ...)

## S4 method for signature 'RmbSpectrum2List'
mergeSpectra(spectra, ...)

Arguments

Details

Information from all spectra is retrieved via getData combined with rbind and placed into the new spectrum with setData

Value

A single RmbSpectrum2 object containing the merged information

See Also

getData, setData

Description

The filenames of the raw LC-MS runs are read from the array files in the global enviroment. See the vignette vignette("RMassBank") for further details about the workflow.

Usage

msmsRead(
  w,
  filetable = NULL,
  files = NULL,
  cpdids = NULL,
  readMethod,
  mode = NULL,
  confirmMode = FALSE,
  useRtLimit = TRUE,
  Args = NULL,
  settings = getOption("RMassBank"),
  progressbar = "progressBarHook",
  MSe = FALSE,
  plots = FALSE
)

Arguments

Value

The msmsWorkspace with msms-spectra read.

Author(s)

Michael Stravs, Eawag <[email protected]>

Erik Mueller, UFZ

See Also

msmsWorkspace-class, msmsWorkflow

Description

The filenames of the raw LC-MS runs are read from the array files in the global enviroment. See the vignette vignette("RMassBank") for further details about the workflow.

Usage

msmsRead.RAW(
  w,
  xRAW = NULL,
  cpdids = NULL,
  mode,
  findPeaksArgs = NULL,
  settings = getOption("RMassBank"),
  progressbar = "progressBarHook",
  plots = FALSE
)

Arguments

Value

The msmsWorkspace with msms-spectra read.

Author(s)

Michael Stravs, Eawag <[email protected]>

Erik Mueller, UFZ

See Also

msmsWorkspace-class, msmsWorkflow

Description

Extracts and processes spectra from a specified file list, according to loaded options and given parameters.

Usage

msmsWorkflow(
  w,
  mode = "pH",
  steps = c(1:8),
  confirmMode = FALSE,
  newRecalibration = TRUE,
  useRtLimit = TRUE,
  archivename = NA,
  readMethod = "mzR",
  filetable = NULL,
  findPeaksArgs = NULL,
  plots = FALSE,
  precursorscan.cf = FALSE,
  settings = getOption("RMassBank"),
  analyzeMethod = "formula",
  progressbar = "progressBarHook",
  MSe = FALSE
)

Arguments

Details

The filenames of the raw LC-MS runs are read from the array files in the global enviroment. See the vignette vignette("RMassBank") for further details about the workflow.

Value

The processed msmsWorkspace.

Author(s)

Michael Stravs, Eawag <[email protected]>

See Also

msmsWorkspace-class

Description

A workspace which stores input and output data for msmsWorkflow.

Usage

## S4 method for signature 'msmsWorkspace'
show(object)

Arguments

Details

Slots:

files

The input file names

spectra

The spectra per compound (RmbSpectraSet) extracted from the raw files

aggregated

A data.frame with an aggregated peak table from all spectra. Further columns are added during processing.

rc, rc.ms1

The recalibration curves generated in workflow step 4.

parent

For the workflow steps after 4: the parent workspace containing the state (spectra, aggregate) before recalibration, such that the workflow can be reprocessed from start.

archivename

The base name of the files the archive is stored to during the workflow.

settings

The RMassBank settings used during the workflow, if stored with the workspace.

#'

Methods:

show

Shows a brief summary of the object and processing progress.

Author(s)

Michael Stravs, Eawag <[email protected]>

See Also

msmsWorkflow

Description

Creates a new workspace for use with mbWorkflow.

Usage

newMbWorkspace(w)

Arguments

Details

The workspace input data will be loaded from the msmsWorkspace-class object provided by the parameter w.

Value

A new mbWorkflow object with the loaded input data.

Author(s)

Michael Stravs, Eawag <[email protected]>

See Also

mbWorkflow, msmsWorkspace-class

Description

Creates an empty workspace or loads an existing workspace from disk.

Usage

newMsmsWorkspace(files = character(0))

Arguments

Details

newMsmsWorkspace creates a new empty workspace for use with msmsWorkflow.

loadMsmsWorkspace loads a workspace saved using archiveResults. Note that it also successfully loads data saved with the old RMassBank data format into the new msmsWorkspace object.

Value

A new msmsWorkspace object

Author(s)

Michael Stravs, Eawag <[email protected]>

See Also

msmsWorkflow, msmsWorkspace-class

Description

Scale spectrum to specified intensity range

Usage

## S4 method for signature 'RmbSpectrum2'
normalize(object, ..., scale = 999, precision = 0, slot = "intensity")

Arguments

Description

Scale all spectra in a 'RmbSpectrum2List' to a specified intensity.

Usage

## S4 method for signature 'RmbSpectrum2List'
normalize(object, ...)

Arguments

Description

Orders a chemical formula in the commonly accepted order (CH followed by alphabetic ordering).

Usage

order.formula(formula, as.formula = TRUE, as.list = FALSE)

Arguments

Author(s)

Michele Stravs

See Also

list.to.formula, add.formula, is.valid.formula

Examples

#
order.formula("H4C9")
order.formula("C2N5HClBr")

Description

Can parse MassBank-records(only V2)

Usage

parseMassBank(Files)

Arguments

Value

The mbWorkspace that the plaintext-record creates. All parsed information will be stored in the 'compiled_ok' slot.

Author(s)

Erik Mueller

See Also

validate

Examples

## Not run: 
	paths <- c("filepath_to_records/RC000001.txt",
	           "filepath_to_records/RC000002.txt")
		mb <- parseMassBank(paths)

## End(Not run)

Description

Can parse MassBank-records(only V2)

Usage

parseMbRecord(filename, readAnnotation=TRUE)

Arguments

Value

An RmbSpectrum2 object created from the plaintext-record

Author(s)

Erik Mueller

See Also

validate

Examples

## Not run: 
		parseMassBank("filepath_to_records/RC00001.txt")

## End(Not run)

Description

Select matching/unmatching peaks from aggregate table

Usage

peaksMatched(o)

## S4 method for signature 'data.frame'
peaksMatched(o)

## S4 method for signature 'msmsWorkspace'
peaksMatched(o)

Arguments

Value

Selects the peaks from the aggregate table which matched within filter criteria (peaksMatched) or didn't match (peaksUnmatched).

Functions

• peaksMatched(data.frame): A method to retrieve the matched peaks from the "aggregated" slot (a data.frame object) in an msmsWorkSpace

• peaksMatched(msmsWorkspace): A method to retrieve the matched peaks from an msmsWorkSpace

Author(s)

stravsmi

Description

Select matching/unmatching peaks from aggregate table

Usage

peaksUnmatched(o, cleaned = FALSE)

## S4 method for signature 'data.frame'
peaksUnmatched(o, cleaned = FALSE)

## S4 method for signature 'msmsWorkspace'
peaksUnmatched(o, cleaned = FALSE)

Arguments

Value

Selects the peaks from the aggregate table which matched within filter criteria (peaksMatched) or didn't match (peaksUnmatched).

Methods (by class)

• peaksUnmatched(data.frame): A method to retrieve the unmatched peaks from the "aggregated" slot (a data.frame object) in an msmsWorkSpace

• peaksUnmatched(msmsWorkspace): A method to retrieve the unmatched peaks from an msmsWorkSpace

Author(s)

stravsmi

Description

Plots the peaks of one or two mbWorkspace to compare them.

Usage

plotMbWorkspaces(w1, w2 = NULL)

Arguments

Details

This functions plots one or two mbWorkspaces in case the use has used different methods to acquire similar spectra. w1 must always be supplied, while w2 is optional. The wokspaces need to be fully processed for this function to work.

Value

A logical indicating whether the information was plotted or not

Author(s)

Erik Mueller

Examples

#
## Not run: plotMbWorkspaces(w1,w2)

Description

Plot the recalibration graph.

Usage

plotRecalibration(w, recalibrateBy = getOption("RMassBank")$recalibrateBy)

		plotRecalibration.direct(rcdata, rc, rc.ms1, title, mzrange,
		recalibrateBy = getOption("RMassBank")$recalibrateBy)

Arguments

Author(s)

Michele Stravs, Eawag <[email protected]>

Description

Calculates ppm values for a given mass.

Usage

ppm(mass, dppm, l = FALSE, p = FALSE)

Arguments

Details

This is a helper function used in RMassBank code.

Value

By default (l=FALSE, p=FALSE) the function returns the mass plus the ppm error (for 123.00000 and 10 ppm: 123.00123, or for 123 and -10 ppm: 122.99877).

For l=TRUE, the function returns the upper and lower limit (sic!) For p=TRUE, just the difference itself is returned (0.00123 for 123/10ppm).

Author(s)

Michael A. Stravs, Eawag <[email protected]>

Examples

ppm(100, 10)

Description

Finds a list of peaks in spectra with a high relative intensity (>10 1e4, or >1 checked. Peaks orbiting around the parent peak mass (calculated from the compound ID), which are very likely co-isolated substances, are ignored.

Usage

problematicPeaks(sp)

Arguments

Value

The modified RmbSpectrum2 object with additional columns/properties 'problematicPeaks' (logical 'TRUE' if the peak is intense and unannotated), 'aMax' (base peak intensity), 'mzCenter' (the precursor m/z).

Note

TODO: there is hardcoded logic in this function that needs to be resolved eventually!

Author(s)

Michael Stravs

See Also

msmsWorkflow

Examples

# As used in the workflow: 

sp <- new("RmbSpectrum2", mz = c(100,200,300,400,500), intensity = c(999999,888888,777777,666666,555555))
sp@ok <- TRUE
property(sp, "mzFound", addNew=TRUE) <- sp@mz
sp@good <- c(TRUE, TRUE, TRUE, FALSE, FALSE)
sp@precursorMz <- 600
sp_checked <- problematicPeaks(sp)
# stopifnot(sum(getData(sp_checked)$problematicPeak) == 2)

Description

Generates a list of intense unmatched peaks for further review (the "failpeak list") and exports it if the archive name is given.

Usage

processProblematicPeaks(w, archivename = NA)

Arguments

Value

Returns the aggregate data.frame with added column "problematic" (logical) which marks peaks which match the problematic criteria

Author(s)

stravsmi

Description

This function provides a standard implementation for the progress bar in RMassBank.

Usage

progressBarHook(object = NULL, value = 0, min = 0, max = 100, close = FALSE)

Arguments

Details

RMassBank calls the progress bar function in the following three ways: pb <- progressBarHook(object=NULL, value=0, min=0, max=LEN) to create a new progress bar. pb <- progressBarHook(object=pb, value= VAL) to set the progress bar to a new value (between the set min and max) progressBarHook(object=pb, close=TRUE) to close the progress bar. (The actual calls are performed with do.call, e.g. progressbar <- "progressBarHook" pb <- do.call(progressbar, list(object=pb, value= nProg)) . See the source code for details.)

To substitute the standard progress bar for an alternative implementation (e.g. for use in a GUI), the developer can write his own function which behaves in the same way as progressBarHook, i.e. takes the same parameters and can be called in the same way.

Value

Returns a progress bar instance identifier (i.e. an identifier which can be used as object in subsequent calls.)

Author(s)

Michele Stravs, Eawag <[email protected]>

Description

This searches the 'properties' slot of the object and returns a column with matching name (if found) or NULL otherwise.

Usage

property(o, property)

## S4 method for signature 'RmbSpectrum2,character'
property(o, property)

Arguments

Value

The corresponding column of o@properties

Description

Update the 'properties' slot of the given object. If the column you want to update does not exist yet and addNew = FALSE (default), this will cause a warning and the object will not be changed

Usage

property(o, property, addNew=FALSE, class="") <- value

## S4 replacement method for signature 'RmbSpectrum2,character,logical,character'
property(o, property, addNew = FALSE, class = "") <- value

## S4 replacement method for signature 'RmbSpectrum2,character,missing,character'
property(o, property, addNew = FALSE, class = "") <- value

## S4 replacement method for signature 'RmbSpectrum2,character,logical,missing'
property(o, property, addNew = FALSE, class = "") <- value

## S4 replacement method for signature 'RmbSpectrum2,character,missing,missing'
property(o, property, addNew = FALSE, class = "") <- value

Arguments

Details

Please note that this is a replacement method, meaning that property(o, property) <- value can be used as a short-hand for the equivalent o <- 'property<-'(o, property, value)

Value

The RmbSpectrum2 object with an updated 'properties' slot

Description

Reanalysis of peaks with no matching molecular formula by allowing additional elements (e.g. "N2O").

Usage

reanalyzeFailpeaks(w, custom_additions, filterSettings =
				getOption("RMassBank")$filterSettings, progressbar = "progressBarHook")
reanalyzeFailpeak(mass, custom_additions, cpdID, mode,
				filterSettings = getOption("RMassBank")$filterSettings)

Arguments

Details

reanalyzeFailpeaks examines the unmatchedPeaksC table in specs and sends every peak through reanalyzeFailpeak.

Value

The aggregate data frame extended by the columns: #'

It would be good to merge the analysis functions of analyzeMsMs with the one used here, to simplify code changes.

Author(s)

Michael Stravs

See Also

analyzeMsMs, msmsWorkflow

Examples

## As used in the workflow:
## Not run:     
	reanalyzedRcSpecs <- reanalyzeFailpeaks(w@aggregated, custom_additions="N2O", mode="pH")
# A single peak:
reanalyzeFailpeak("N2O", 105.0447, 1234, 1, 1)

## End(Not run)

Description

Predefined fits to use for recalibration: Loess fit and GAM fit.

Usage

recalibrate.loess(rcdata)

		recalibrate.identity(rcdata)

		recalibrate.mean(rcdata)

		recalibrate.linear(rcdata)

Arguments

Details

recalibrate.loess() provides a Loess fit (recalibrate.loess) to a given recalibration parameter. If MS and MS/MS data should be fit together, recalibrate.loess provides good default settings for Orbitrap instruments.

recalibrate.identity() returns a non-recalibration, i.e. a predictor which predicts 0 for all input values. This can be used if the user wants to skip recalibration in the RMassBank workflow.

#' recalibrate.mean() and recalibrate.linear() are simple recalibrations which return a constant shift or a linear recalibration. They will be only useful in particular cases.

recalibrate() itself is only a dummy function and does not do anything.

Alternatively other functions can be defined. Which functions are used for recalibration is specified by the RMassBank options file. (Note: if recalibrateMS1: common, the recalibrator: MS1 value is irrelevant, since for a common curve generated with the function specified in recalibrator: MS2 will be used.)

Value

Returns a model for recalibration to be used with predict and the like.

Author(s)

Michael Stravs, EAWAG <[email protected]>

Examples

## Not run: 
rcdata <- subset(spec$peaksMatched, formulaCount==1)
ms1data <- recalibrate.addMS1data(spec, mode, 15)
rcdata <- rbind(rcdata, ms1data)
rcdata$recalfield <- rcdata$dppm
rcCurve <- recalibrate.loess(rcdata)
# define a spectrum and recalibrate it
s <- matrix(c(100,150,200,88.8887,95.0005,222.2223), ncol=2)
colnames(s) <- c("mz", "int")
recalS <- recalibrateSingleSpec(s, rcCurve)

Alternative: define an custom recalibrator function with different parameters
recalibrate.MyOwnLoess <- function(rcdata)
{
	return(loess(recalfield ~ mzFound, data=rcdata, family=c("symmetric"),
					degree = 2, span=0.4))
}
# This can then be specified in the RMassBank settings file:
# recalibrateMS1: common
# recalibrator:
#    MS1: recalibrate.loess
#    MS2: recalibrate.MyOwnLoess")
# [...]

## End(Not run)

Description

Returns the precursor peaks for all MS1 spectra in the spec dataset with annotated formula to be used in recalibration.

For all spectra in spec$specFound, the precursor ion is extracted from the MS1 precursor spectrum. All found ions are returned in a data frame with a format matching spec$peaksMatched and therefore suitable for rbinding to the spec$peaksMatched table. However, only minimal information needed for recalibration is returned.

Usage

recalibrate.addMS1data(spec, recalibrateMS1Window = 
				getOption("RMassBank")$recalibrateMS1Window)

Arguments

Value

A dataframe with columns mzFound, formula, mzCalc, dppm, dbe, int, dppmBest, formulaCount, good, cpdID, scan, parentScan, dppmRc. However, columns dbe, int, formulaCount, good, scan, parentScan do not contain real information and are provided only as fillers.

Author(s)

Michael Stravs, EAWAG <[email protected]>

Examples

## Not run: 
# More or less as used in recalibrateSpectra:
		rcdata <- peaksMatched(w)
		rcdata <- rcdata[rcdata$formulaCount == 1, ,drop=FALSE]
		ms1data <- recalibrate.addMS1data(w, "pH", 15)
		rcdata <- rbind(rcdata, ms1data)
 # ... continue constructing recalibration curve with rcdata

## End(Not run)

Description

The logging file to be used can be specified by the user in the logging_file field of settings.ini

Usage

rmb_log_debug(...)

Arguments

Author(s)

pstahlhofen

See Also

logger::log_debug

Description

The logging file to be used can be specified by the user in the logging_file field of settings.ini

Usage

rmb_log_error(...)

Arguments

Author(s)

pstahlhofen

See Also

logger::log_error

Description

The logging file to be used can be specified by the user in the logging_file field of settings.ini

Usage

rmb_log_fatal(...)

Arguments

Author(s)

pstahlhofen

See Also

logger::log_fatal

Description

The logging file to be used can be specified by the user in the logging_file field of settings.ini

Usage

rmb_log_info(...)

Arguments

Author(s)

pstahlhofen

See Also

logger::log_info

Description

The logging file to be used can be specified by the user in the logging_file field of settings.ini

Usage

rmb_log_success(...)

Arguments

Author(s)

pstahlhofen

See Also

logger::log_success

Description

The logging file to be used can be specified by the user in the logging_file field of settings.ini

Usage

rmb_log_trace(...)

Arguments

Author(s)

pstahlhofen

See Also

logger::log_trace

Description

The logging file to be used can be specified by the user in the logging_file field of settings.ini

Usage

rmb_log_warn(...)

Arguments

Author(s)

pstahlhofen

See Also

logger::log_warn

Description

Load, set and reset settings for RMassBank.

Usage

loadRmbSettings(file_or_list) 

loadRmbSettingsFromEnv(env = .GlobalEnv)

RmbDefaultSettings()

RmbSettingsTemplate(target)

Arguments

Details

RmbSettingsTemplate creates a template file in which you can adjust the settings as you like. Before using RMassBank, you must then load the settings file using loadRmbSettings. RmbDefaultSettings loads the default settings. loadRmbSettingsFromEnv loads the settings stored in env$RmbSettings, which is useful when reloading archives with saved settings inside.

Note: no settings are loaded upon loading MassBank! This is intended, so that one never forgets to load the correct settings.

The settings are described in RmbSettings.

Value

None.

Note

The default settings will not work for you unless you have, by chance, installed OpenBabel into the same directory as I have!

Author(s)

Michael Stravs

See Also

RmbSettings

Examples

# Create a standard settings file and load it (unedited)
	RmbSettingsTemplate("mysettings.ini")
 loadRmbSettings("mysettings.ini")
 unlink("mysettings.ini")

Description

Describes all settings for the RMassBank settings file.

Details

• deprofile Whether and how to deprofile input raw files. Leave the setting empty if your raw files are already in "centroid" mode. If your input files are in profile mode, you have the choice between algorithms deprofile.spline, deprofile.fwhm, deprofile.localMax; refer to the individual manpages for more information.

• rtMargin, rtShift The allowed retention time deviation relative to the values specified in your compound list (see loadList), and the systematic shift (due to the use of, e.g., pre-columns or other special equipment.

• babeldir Directory to OpenBabel. Required for creating molfiles for MassBank export. If no OpenBabel directory is given, RMassBank will attempt to use the CACTUS webservice for SDF generation. It is strongly advised to install OpenBabel; the CACTUS structures have explicit hydrogen atoms. The path should point to the directory where babel.exe (or the Linux "babel" equivalent) lies.

• use_version Which MassBank record format to use; version 2 is strongly advised, version 1 is considered outdated and should be used only if for some reason you are running old servers and an upgrade is not feasible.

• use_rean_peaks Whether to include peaks from reanalysis (see reanalyzeFailpeaks) in the MassBank records. Boolean, TRUE or FALSE.

• annotations A list of constant annotations to use in the MassBank records. The entries authors, copyright, license, instrument, instrument_type, compound_class correspond to the MassBank entries AUTHORS, COPYRIGHT, PUBLICATION, LICENSE, AC$INSTRUMENT, AC$INSTRUMENT_TYPE, CH$COMPOUND_CLASS. The entry confidence_comment is added as COMMENT: CONFIDENCE entry.

  The entry internal_id_fieldname is used to name the MassBank entry which will keep a reference to the internal compound ID used in the workflow: for internal_id_fieldname = MYID and e.g. compound 1234, an entry will be added to the MassBank record with COMMENT: MYID 1234. The internal fieldname should not be left empty!

  The entries lc_gradient, lc_flow, lc_solvent_a, lc_solvent_b, lc_column correspond to the MassBank entries AC$CHROMATOGRAPHY: FLOW_GRADIENT, FLOW_RATE, SOLVENT A, SOLVENT B, COLUMN_NAME.

  ms_type, ionization correspond to AC$MASS_SPECTROMETRY: MS_TYPE, IONIZATION.

  entry_prefix is the two-letter prefix used when building MassBank accession codes.

  Entries under ms_dataprocessing are added as MS$DATA_PROCESSING: entries, in addition to the default WHOLE: RMassBank.

• annotator For advanced users: option to select your own custom annotator. Check annotator.default and the source code for details.

• spectraList This setting describes the experimental annotations for the single data-dependent scans. For every data-dependent scan event, a spectraList entry with mode, ces, ce, res denoting collision mode, collision energy in short and verbose notation, and FT resolution.

• accessionNumberShifts This denotes the starting points for accession numbers for different ion types. For example, pH: 0, mH: 50 means that [M+H]+ spectra will start at XX123401 (XX being the entry_prefix and 1234 the compound id) and [M-H]- will start at XX123451.

• electronicNoise, electronicNoiseWidth Known electronic noise peaks and the window to be used by cleanElnoise

• recalibrateBy dppm or dmz to recalibrate either by delta ppm or by delta mz.

• recalibrateMS1 common or separate to recalibrate MS1 data points together or separately from MS2 data points.

• recalibrator: MS1, MS2 The functions to use for recalibration of MS1 and MS2 data points. Note that the MS1 setting is only meaningful if recalibrateMS1: separate, otherwise the MS2 setting is used for a common recalibration curve. See recalibrate.loess for details.

• multiplicityFilter Define the multiplicity filtering level. Default is 2, a value of 1 is off (no filtering) and >2 is harsher filtering.

• titleFormat The title of MassBank records is a mini-summary of the record, for example "Dinotefuran; LC-ESI-QFT; MS2; CE: 35 By default, the first compound name CH$NAME, instrument type AC$INSTRUMENT_TYPE, MS/MS type AC$MASS_SPECTROMETRY: MS_TYPE, collision energy RECORD_TITLE_CE, resolution AC$MASS_SPECTROMETRY: RESOLUTION and precursor MS$FOCUSED_ION: PRECURSOR_TYPE are used. If alternative information is relevant to differentiate acquired spectra, the title should be adjusted. For example, many TOFs do not have a resolution setting. See MassBank documentation for more.

• filterSettings A list of settings that affect the MS/MS processing. The entries ppmHighMass, ppmLowMass, massRangeDivision set values for pre-processing, prior to recalibration. ppmHighMass defines the ppm error for the high mass range (default 10 ppm for Orbitraps), ppmLowMass is the error for the low mass range (default 15 ppm for Orbitraps) and massRangeDivision is the m/z value defining the split between the high and low mass range (default m/z = 120).

  The entry ppmFine defines the ppm cut-off post recalibration. The default value of 5 ppm is recommended for Orbitraps. For other instruments this can be interpreted from the recalibration plot. All ppm limits are one-sided (e.g. this includes values to +5 ppm or -5 ppm deviation from the exact mass).

  The entries prelimCut, prelimCutRatio define the intensity cut-off and cut-off ratio (in the peak selection for the recalibration only. Careful: the default value 1e4 for Orbitrap LTQ positive mode could remove all peaks for TOF data and will remove too many peaks for Orbitrap LTQ negative mode spectra!

  The entry specOKLimit defines the intensity limit to include MS/MS spectra. MS/MS spectra must have at least one peak above this limit to proceed through the workflow.

  dbeMinLimit defines the minimum allowable ring and double bond equivalents (DBE) allowed for assigned formulas. This assumes maximum valuences for elements with multiple valence states. The default is -0.5 (accounting for fragments being ions).

  The entries satelliteMzLimit, satelliteIntLimit define the cut-off m/z and intensity values for satellite peak removal (an artefact of Fourier Transform processing). All peaks within the m/z limit (default 0.5) and intensity ratio (default 0.05 or 5 Fourier Transform instruments only (e.g. Orbitrap).

• filterSettings Parameters for adjusting the raw data retrieval. The entry ppmFine defines the ppm error to look for the precursor in the MS1 (parent) spectrum. Default is 10 ppm for Orbitrap.

  mzCoarse defines the error to search for the precursor specification in the MS2 spectrum. This is often only saved to 2 decimal places and thus can be quite inaccurate. The accuracy also depends on the isolation window used. The default settings (for e.g. Orbitrap) is 0.5 (Da, or Th for m/z).

  The entry fillPrecursorScan is largely untested. The default value (FALSE) assumes all necessary precursor information is available in the mzML file. A setting ot TRUE tries to fill in the precursor data scan number if it is missing. Only tested on one case study so far - feedback welcome!

Author(s)

Michael Stravs, Emma Schymanski

See Also

loadRmbSettings

Description

Set of spectra pertaining to one compound

Slots