aggregateSpectra: Aggregate analyzed spectra
In RMassBank: Workflow to process tandem MS files and build MassBank records

Description Usage Arguments Details Value Author(s) See Also Examples

Groups an array of analyzed spectra and creates aggregated peak tables

1	aggregateSpectra(spec, addIncomplete=FALSE)

`spec`	The `RmbSpectraSetList` of spectra sets (`RmbSpectraSet` objects) to aggregate
`addIncomplete`	Whether or not the peaks from incomplete files (files for which less than the maximal number of spectra are present)

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.

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

`mzFound, intensity`	Mass and intensity of the peak
`good`	if the peak passes filter criteria
`mzCalc, formula, dbe, dppm`	calculated mass, formula, dbe and ppm deviation of the assigned formula
`formulaCount, dppmBest`	Number of matched formulae for this m/z value, and ppm deviation of the best match
`scan, cpdID, parentScan`	Scan number of the child and parent spectrum in the raw file, also the compound ID to which the peak belongs
`dppmRc`	ppm deviation recalculated from the aggregation function
`index`	Aggregate-table peak index, so the table can be subsetted, edited and results reinserted back into this table easily

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

Michael Stravs

msmsWorkflow, analyzeMsMs

## 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)